diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
index 63f8c06c3..20a4b0abd 100644
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -40,7 +40,7 @@ jobs:
 
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2023-06-27
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
 
@@ -85,7 +85,7 @@ jobs:
 
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2023-06-27
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
 
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index edff9ac55..821702878 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -102,28 +102,32 @@ jobs:
       PCRE2_SYS_STATIC: 1
     strategy:
       matrix:
-        build: [windows-msvc, macos, macos-aarch64, linux-musl, linux-gnu]
+        build: [windows-msvc, macos, macos-aarch64, linux-musl, linux-gnu, linux-aarch64]
         include:
           - build: windows-msvc
             os: windows-latest
-            rust: nightly-2023-06-27
+            rust: nightly-2024-02-06
             target: x86_64-pc-windows-msvc
           - build: macos
             os: macos-13
-            rust: nightly-2023-06-27
+            rust: nightly-2024-02-06
             target: x86_64-apple-darwin
           - build: macos-aarch64
             os: macos-13
-            rust: nightly-2023-06-27
+            rust: nightly-2024-02-06
             target: aarch64-apple-darwin
           - build: linux-musl
             os: ubuntu-22.04
-            rust: nightly-2023-06-27
+            rust: nightly-2024-02-06
             target: x86_64-unknown-linux-musl
           - build: linux-gnu
             os: ubuntu-22.04
-            rust: nightly-2023-06-27
+            rust: nightly-2024-02-06
             target: x86_64-unknown-linux-gnu
+          - build: linux-aarch64
+            os: ubuntu-22.04
+            rust: nightly-2024-02-06
+            target: aarch64-unknown-linux-gnu
 
     steps:
       - name: Checkout repo
@@ -181,7 +185,7 @@ jobs:
         run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry
 
       - name: Strip release binary
-        if: matrix.build != 'windows-msvc'
+        if: matrix.build != 'windows-msvc' && matrix.build != 'linux-aarch64'
         run: strip "target/${{ matrix.target }}/release/ezkl"
 
       - name: Strip release binary (Windows)
diff --git a/.github/workflows/rust.yml b/.github/workflows/rust.yml
index 5ac291c0d..da69afffb 100644
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -574,7 +574,7 @@ jobs:
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
       - name: Run pytest
-        run: source .env/bin/activate; pytest -vv
+        run: source .env/bin/activate; pip install pytest-asyncio; pytest -vv
 
   accuracy-measurement-tests:
     runs-on: ubuntu-latest-32-cores
diff --git a/Cargo.lock b/Cargo.lock
index 928785cae..bcfefe2d4 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -2,16 +2,6 @@
 # It is not intended for manual editing.
 version = 3
 
-[[package]]
-name = "Inflector"
-version = "0.11.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fe438c63458706e03479442743baae6c88256498e6431708f6dfc520a26515d3"
-dependencies = [
- "lazy_static",
- "regex",
-]
-
 [[package]]
 name = "addr2line"
 version = "0.21.0"
@@ -27,17 +17,6 @@ version = "1.0.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "f26201604c87b1e01bd3d98f8d5d9a8fcbb815e8cedb41ffccbeb4bf593a35fe"
 
-[[package]]
-name = "aes"
-version = "0.8.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b169f7a6d4742236a0a00c541b845991d0ac43e546831af1249753ab4c3aa3a0"
-dependencies = [
- "cfg-if",
- "cipher",
- "cpufeatures",
-]
-
 [[package]]
 name = "ahash"
 version = "0.7.8"
@@ -76,6 +55,169 @@ version = "0.2.16"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0942ffc6dcaadf03badf6e6a2d0228460359d5e34b57ccdc720b7382dfbd5ec5"
 
+[[package]]
+name = "alloy"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-consensus",
+ "alloy-contract",
+ "alloy-core",
+ "alloy-eips",
+ "alloy-genesis",
+ "alloy-node-bindings",
+ "alloy-provider",
+ "alloy-rpc-client",
+ "alloy-rpc-types",
+ "alloy-serde",
+ "alloy-signer",
+ "alloy-signer-wallet",
+ "alloy-transport",
+ "alloy-transport-http",
+ "reqwest",
+]
+
+[[package]]
+name = "alloy-consensus"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-eips",
+ "alloy-primitives 0.7.2",
+ "alloy-rlp",
+ "alloy-serde",
+ "c-kzg",
+ "serde",
+]
+
+[[package]]
+name = "alloy-contract"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-dyn-abi",
+ "alloy-json-abi",
+ "alloy-network",
+ "alloy-primitives 0.7.2",
+ "alloy-provider",
+ "alloy-rpc-types",
+ "alloy-sol-types",
+ "alloy-transport",
+ "futures",
+ "futures-util",
+ "thiserror",
+]
+
+[[package]]
+name = "alloy-core"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e30b83573b348305b9629a094b5331093a030514cd5713433799495cb283fea1"
+dependencies = [
+ "alloy-dyn-abi",
+ "alloy-json-abi",
+ "alloy-primitives 0.7.2",
+ "alloy-sol-types",
+]
+
+[[package]]
+name = "alloy-dyn-abi"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "545885d9b0b2c30fd344ae291439b4bfe59e48dd62fbc862f8503d98088967dc"
+dependencies = [
+ "alloy-json-abi",
+ "alloy-primitives 0.7.2",
+ "alloy-sol-type-parser",
+ "alloy-sol-types",
+ "const-hex",
+ "itoa",
+ "serde",
+ "serde_json",
+ "winnow 0.6.5",
+]
+
+[[package]]
+name = "alloy-eips"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "alloy-rlp",
+ "alloy-serde",
+ "c-kzg",
+ "once_cell",
+ "serde",
+ "sha2",
+]
+
+[[package]]
+name = "alloy-genesis"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "alloy-serde",
+ "serde",
+ "serde_json",
+]
+
+[[package]]
+name = "alloy-json-abi"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "786689872ec4e7d354810ab0dffd48bb40b838c047522eb031cbd47d15634849"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "alloy-sol-type-parser",
+ "serde",
+ "serde_json",
+]
+
+[[package]]
+name = "alloy-json-rpc"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "serde",
+ "serde_json",
+ "thiserror",
+ "tracing",
+]
+
+[[package]]
+name = "alloy-network"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-consensus",
+ "alloy-eips",
+ "alloy-json-rpc",
+ "alloy-primitives 0.7.2",
+ "alloy-rpc-types",
+ "alloy-signer",
+ "alloy-sol-types",
+ "async-trait",
+ "futures-utils-wasm",
+ "thiserror",
+]
+
+[[package]]
+name = "alloy-node-bindings"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-genesis",
+ "alloy-primitives 0.7.2",
+ "k256",
+ "serde_json",
+ "tempfile",
+ "thiserror",
+ "tracing",
+ "url",
+]
+
 [[package]]
 name = "alloy-primitives"
 version = "0.4.2"
@@ -93,6 +235,59 @@ dependencies = [
  "tiny-keccak",
 ]
 
+[[package]]
+name = "alloy-primitives"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "525448f6afc1b70dd0f9d0a8145631bf2f5e434678ab23ab18409ca264cae6b3"
+dependencies = [
+ "alloy-rlp",
+ "bytes",
+ "cfg-if",
+ "const-hex",
+ "derive_more",
+ "getrandom",
+ "hex-literal",
+ "itoa",
+ "k256",
+ "keccak-asm",
+ "proptest",
+ "rand 0.8.5",
+ "ruint",
+ "serde",
+ "tiny-keccak",
+]
+
+[[package]]
+name = "alloy-provider"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-eips",
+ "alloy-json-rpc",
+ "alloy-network",
+ "alloy-node-bindings",
+ "alloy-primitives 0.7.2",
+ "alloy-rpc-client",
+ "alloy-rpc-types",
+ "alloy-rpc-types-trace",
+ "alloy-signer-wallet",
+ "alloy-transport",
+ "alloy-transport-http",
+ "async-stream",
+ "async-trait",
+ "auto_impl",
+ "dashmap",
+ "futures",
+ "futures-utils-wasm",
+ "lru",
+ "reqwest",
+ "serde_json",
+ "tokio",
+ "tracing",
+ "url",
+]
+
 [[package]]
 name = "alloy-rlp"
 version = "0.3.4"
@@ -115,6 +310,184 @@ dependencies = [
  "syn 2.0.53",
 ]
 
+[[package]]
+name = "alloy-rpc-client"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-json-rpc",
+ "alloy-transport",
+ "alloy-transport-http",
+ "futures",
+ "pin-project",
+ "reqwest",
+ "serde",
+ "serde_json",
+ "tokio",
+ "tokio-stream",
+ "tower",
+ "tracing",
+ "url",
+]
+
+[[package]]
+name = "alloy-rpc-types"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-consensus",
+ "alloy-eips",
+ "alloy-genesis",
+ "alloy-primitives 0.7.2",
+ "alloy-rlp",
+ "alloy-serde",
+ "alloy-sol-types",
+ "itertools 0.12.1",
+ "serde",
+ "serde_json",
+ "thiserror",
+]
+
+[[package]]
+name = "alloy-rpc-types-trace"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "alloy-rpc-types",
+ "alloy-serde",
+ "serde",
+ "serde_json",
+]
+
+[[package]]
+name = "alloy-serde"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "serde",
+ "serde_json",
+]
+
+[[package]]
+name = "alloy-signer"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-primitives 0.7.2",
+ "async-trait",
+ "auto_impl",
+ "elliptic-curve",
+ "k256",
+ "thiserror",
+]
+
+[[package]]
+name = "alloy-signer-wallet"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-consensus",
+ "alloy-network",
+ "alloy-primitives 0.7.2",
+ "alloy-signer",
+ "async-trait",
+ "k256",
+ "rand 0.8.5",
+ "thiserror",
+]
+
+[[package]]
+name = "alloy-sol-macro"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "89c80a2cb97e7aa48611cbb63950336f9824a174cdf670527cc6465078a26ea1"
+dependencies = [
+ "alloy-json-abi",
+ "alloy-sol-macro-input",
+ "const-hex",
+ "heck 0.4.1",
+ "indexmap",
+ "proc-macro-error",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.53",
+ "syn-solidity",
+ "tiny-keccak",
+]
+
+[[package]]
+name = "alloy-sol-macro-input"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c58894b58ac50979eeac6249661991ac40b9d541830d9a725f7714cc9ef08c23"
+dependencies = [
+ "alloy-json-abi",
+ "const-hex",
+ "dunce",
+ "heck 0.5.0",
+ "proc-macro2",
+ "quote",
+ "serde_json",
+ "syn 2.0.53",
+ "syn-solidity",
+]
+
+[[package]]
+name = "alloy-sol-type-parser"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7da8e71ea68e780cc203919e03f69f59e7afe92d2696fb1dcb6662f61e4031b6"
+dependencies = [
+ "winnow 0.6.5",
+]
+
+[[package]]
+name = "alloy-sol-types"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "399287f68d1081ed8b1f4903c49687658b95b142207d7cb4ae2f4813915343ef"
+dependencies = [
+ "alloy-json-abi",
+ "alloy-primitives 0.7.2",
+ "alloy-sol-macro",
+ "const-hex",
+ "serde",
+]
+
+[[package]]
+name = "alloy-transport"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-json-rpc",
+ "base64 0.22.1",
+ "futures-util",
+ "futures-utils-wasm",
+ "serde",
+ "serde_json",
+ "thiserror",
+ "tokio",
+ "tower",
+ "url",
+ "wasm-bindgen-futures",
+]
+
+[[package]]
+name = "alloy-transport-http"
+version = "0.1.0"
+source = "git+https://github.com/alloy-rs/alloy#e60d64cff86d995657f0acea85c2bbd52f9bd810"
+dependencies = [
+ "alloy-json-rpc",
+ "alloy-transport",
+ "reqwest",
+ "serde_json",
+ "tower",
+ "tracing",
+ "url",
+]
+
 [[package]]
 name = "android-tzdata"
 version = "0.1.1"
@@ -209,6 +582,15 @@ version = "0.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d301b3b94cb4b2f23d7917810addbbaff90738e0ca2be692bd027e70d7e0330c"
 
+[[package]]
+name = "arbitrary"
+version = "1.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7d5a26814d8dcb93b0e5a0ff3c6d80a8843bafb21b39e8e18a6f05471870e110"
+dependencies = [
+ "derive_arbitrary",
+]
+
 [[package]]
 name = "arc-swap"
 version = "1.7.0"
@@ -482,10 +864,21 @@ dependencies = [
 ]
 
 [[package]]
-name = "async-trait"
-version = "0.1.78"
+name = "async-stream"
+version = "0.3.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "461abc97219de0eaaf81fe3ef974a540158f3d079c2ab200f891f1a2ef201e85"
+checksum = "cd56dd203fef61ac097dd65721a419ddccb106b2d2b70ba60a6b529f03961a51"
+dependencies = [
+ "async-stream-impl",
+ "futures-core",
+ "pin-project-lite",
+]
+
+[[package]]
+name = "async-stream-impl"
+version = "0.3.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -493,14 +886,14 @@ dependencies = [
 ]
 
 [[package]]
-name = "async_io_stream"
-version = "0.3.3"
+name = "async-trait"
+version = "0.1.78"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b6d7b9decdf35d8908a7e3ef02f64c5e9b1695e230154c0e8de3969142d9b94c"
+checksum = "461abc97219de0eaaf81fe3ef974a540158f3d079c2ab200f891f1a2ef201e85"
 dependencies = [
- "futures",
- "pharos",
- "rustc_version 0.4.0",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.53",
 ]
 
 [[package]]
@@ -554,15 +947,15 @@ checksum = "4c7f02d4ea65f2c1853089ffd8d2787bdbc63de2f0d29dedbcf8ccdfa0ccd4cf"
 
 [[package]]
 name = "base64"
-version = "0.13.1"
+version = "0.21.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9e1b586273c5702936fe7b7d6896644d8be71e6314cfe09d3167c95f712589e8"
+checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567"
 
 [[package]]
 name = "base64"
-version = "0.21.7"
+version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567"
+checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
 
 [[package]]
 name = "base64ct"
@@ -579,12 +972,6 @@ dependencies = [
  "serde",
 ]
 
-[[package]]
-name = "bech32"
-version = "0.9.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d86b93f97252c47b41663388e6d155714a9d0c398b99f1005cbc5f978b29f445"
-
 [[package]]
 name = "bigdecimal"
 version = "0.3.1"
@@ -687,15 +1074,23 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8d696c370c750c948ada61c69a0ee2cbbb9c50b1019ddb86d9317157a99c2cae"
 
 [[package]]
-name = "bs58"
-version = "0.5.1"
+name = "blst"
+version = "0.3.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf88ba1141d185c399bee5288d850d63b8369520c1eafc32a0430b5b6c287bf4"
+checksum = "c94087b935a822949d3291a9989ad2b2051ea141eda0fd4e478a75f6aa3e604b"
 dependencies = [
- "sha2",
- "tinyvec",
+ "cc",
+ "glob",
+ "threadpool",
+ "zeroize",
 ]
 
+[[package]]
+name = "build_const"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b4ae4235e6dac0694637c763029ecea1a2ec9e4e06ec2729bd21ba4d9c863eb7"
+
 [[package]]
 name = "bumpalo"
 version = "3.15.4"
@@ -730,35 +1125,17 @@ dependencies = [
 ]
 
 [[package]]
-name = "camino"
-version = "1.1.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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-dependencies = [
- "serde",
-]
-
-[[package]]
-name = "cargo_metadata"
-version = "0.18.1"
+name = "c-kzg"
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 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2d886547e41f740c616ae73108f6eb70afe6d940c7bc697cb30f13daec073037"
+checksum = "3130f3d8717cc02e668a896af24984d5d5d4e8bf12e278e982e0f1bd88a0f9af"
 dependencies = [
- "camino",
- "cargo-platform",
- "semver 1.0.22",
+ "blst",
+ "cc",
+ "glob",
+ "hex",
+ "libc",
  "serde",
- "serde_json",
- "thiserror",
 ]
 
 [[package]]
@@ -797,16 +1174,6 @@ dependencies = [
  "windows-targets 0.52.4",
 ]
 
-[[package]]
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-version = "0.4.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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-dependencies = [
- "crypto-common",
- "inout",
-]
-
 [[package]]
 name = "clap"
 version = "2.34.0"
@@ -867,58 +1234,6 @@ dependencies = [
  "cc",
 ]
 
-[[package]]
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-version = "0.8.7"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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-dependencies = [
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- "coins-core",
- "digest 0.10.7",
- "hmac",
- "k256",
- "serde",
- "sha2",
- "thiserror",
-]
-
-[[package]]
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-dependencies = [
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- "coins-bip32",
- "hmac",
- "once_cell",
- "pbkdf2 0.12.2",
- "rand 0.8.5",
- "sha2",
- "thiserror",
-]
-
-[[package]]
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- "bech32",
- "bs58",
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- "generic-array",
- "hex",
- "ripemd",
- "serde",
- "serde_derive",
- "sha2",
- "sha3 0.10.8",
- "thiserror",
-]
-
 [[package]]
 name = "colorchoice"
 version = "1.0.0"
@@ -944,7 +1259,7 @@ dependencies = [
  "is-terminal",
  "serde",
  "serde_json",
- "yansi",
+ "yansi 0.5.1",
 ]
 
 [[package]]
@@ -1165,15 +1480,6 @@ dependencies = [
  "memchr",
 ]
 
-[[package]]
-name = "ctr"
-version = "0.9.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0369ee1ad671834580515889b80f2ea915f23b8be8d0daa4bbaf2ac5c7590835"
-dependencies = [
- "cipher",
-]
-
 [[package]]
 name = "cuda-config"
 version = "0.1.0"
@@ -1192,6 +1498,19 @@ dependencies = [
  "cuda-config",
 ]
 
+[[package]]
+name = "dashmap"
+version = "5.5.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "978747c1d849a7d2ee5e8adc0159961c48fb7e5db2f06af6723b80123bb53856"
+dependencies = [
+ "cfg-if",
+ "hashbrown 0.14.3",
+ "lock_api",
+ "once_cell",
+ "parking_lot_core",
+]
+
 [[package]]
 name = "der"
 version = "0.7.8"
@@ -1224,6 +1543,17 @@ dependencies = [
  "syn 1.0.109",
 ]
 
+[[package]]
+name = "derive_arbitrary"
+version = "1.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "67e77553c4162a157adbf834ebae5b415acbecbeafc7a74b0e886657506a7611"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.53",
+]
+
 [[package]]
 name = "derive_more"
 version = "0.99.17"
@@ -1300,6 +1630,17 @@ dependencies = [
  "winapi",
 ]
 
+[[package]]
+name = "displaydoc"
+version = "0.2.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "487585f4d0c6655fe74905e2504d8ad6908e4db67f744eb140876906c2f3175d"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.53",
+]
+
 [[package]]
 name = "doc-comment"
 version = "0.3.3"
@@ -1390,39 +1731,12 @@ checksum = "c533630cf40e9caa44bd91aadc88a75d75a4c3a12b4cfde353cbed41daa1e1f1"
 dependencies = [
  "log",
 ]
-
-[[package]]
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-version = "0.3.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f"
-
-[[package]]
-name = "encoding_rs"
-version = "0.8.33"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7268b386296a025e474d5140678f75d6de9493ae55a5d709eeb9dd08149945e1"
-dependencies = [
- "cfg-if",
-]
-
-[[package]]
-name = "enr"
-version = "0.10.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2a3d8dc56e02f954cac8eb489772c552c473346fc34f67412bb6244fd647f7e4"
-dependencies = [
- "base64 0.21.7",
- "bytes",
- "hex",
- "k256",
- "log",
- "rand 0.8.5",
- "rlp",
- "serde",
- "sha3 0.10.8",
- "zeroize",
-]
+
+[[package]]
+name = "encode_unicode"
+version = "0.3.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f"
 
 [[package]]
 name = "enumn"
@@ -1464,28 +1778,6 @@ dependencies = [
  "windows-sys 0.52.0",
 ]
 
-[[package]]
-name = "eth-keystore"
-version = "0.5.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1fda3bf123be441da5260717e0661c25a2fd9cb2b2c1d20bf2e05580047158ab"
-dependencies = [
- "aes",
- "ctr",
- "digest 0.10.7",
- "hex",
- "hmac",
- "pbkdf2 0.11.0",
- "rand 0.8.5",
- "scrypt",
- "serde",
- "serde_json",
- "sha2",
- "sha3 0.10.8",
- "thiserror",
- "uuid",
-]
-
 [[package]]
 name = "ethabi"
 version = "18.0.0"
@@ -1511,10 +1803,8 @@ checksum = "c22d4b5885b6aa2fe5e8b9329fb8d232bf739e434e6b87347c63bdd00c120f60"
 dependencies = [
  "crunchy",
  "fixed-hash",
- "impl-codec",
  "impl-rlp",
  "impl-serde",
- "scale-info",
  "tiny-keccak",
 ]
 
@@ -1526,272 +1816,17 @@ checksum = "02d215cbf040552efcbe99a38372fe80ab9d00268e20012b79fcd0f073edd8ee"
 dependencies = [
  "ethbloom",
  "fixed-hash",
- "impl-codec",
  "impl-rlp",
  "impl-serde",
  "primitive-types",
- "scale-info",
  "uint",
 ]
 
-[[package]]
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-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "816841ea989f0c69e459af1cf23a6b0033b19a55424a1ea3a30099becdb8dec0"
-dependencies = [
- "ethers-addressbook",
- "ethers-contract",
- "ethers-core",
- "ethers-etherscan",
- "ethers-middleware",
- "ethers-providers",
- "ethers-signers",
- "ethers-solc",
-]
-
-[[package]]
-name = "ethers-addressbook"
-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5495afd16b4faa556c3bba1f21b98b4983e53c1755022377051a975c3b021759"
-dependencies = [
- "ethers-core",
- "once_cell",
- "serde",
- "serde_json",
-]
-
-[[package]]
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-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6fceafa3578c836eeb874af87abacfb041f92b4da0a78a5edd042564b8ecdaaa"
-dependencies = [
- "const-hex",
- "ethers-contract-abigen",
- "ethers-contract-derive",
- "ethers-core",
- "ethers-providers",
- "futures-util",
- "once_cell",
- "pin-project",
- "serde",
- "serde_json",
- "thiserror",
-]
-
-[[package]]
-name = "ethers-contract-abigen"
-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "04ba01fbc2331a38c429eb95d4a570166781f14290ef9fdb144278a90b5a739b"
-dependencies = [
- "Inflector",
- "const-hex",
- "dunce",
- "ethers-core",
- "eyre",
- "prettyplease",
- "proc-macro2",
- "quote",
- "regex",
- "serde",
- "serde_json",
- "syn 2.0.53",
- "toml",
- "walkdir",
-]
-
-[[package]]
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-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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-dependencies = [
- "Inflector",
- "const-hex",
- "ethers-contract-abigen",
- "ethers-core",
- "proc-macro2",
- "quote",
- "serde_json",
- "syn 2.0.53",
-]
-
-[[package]]
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- "cargo_metadata",
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- "generic-array",
- "k256",
- "num_enum",
- "once_cell",
- "open-fastrlp",
- "rand 0.8.5",
- "rlp",
- "serde",
- "serde_json",
- "strum",
- "syn 2.0.53",
- "tempfile",
- "thiserror",
- "tiny-keccak",
- "unicode-xid",
-]
-
-[[package]]
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-source = "registry+https://github.com/rust-lang/crates.io-index"
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- "ethers-core",
- "ethers-solc",
- "reqwest",
- "semver 1.0.22",
- "serde",
- "serde_json",
- "thiserror",
- "tracing",
-]
-
-[[package]]
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-dependencies = [
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- "ethers-core",
- "ethers-providers",
- "ethers-signers",
- "futures-channel",
- "futures-locks",
- "futures-util",
- "instant",
- "reqwest",
- "serde",
- "serde_json",
- "thiserror",
- "tokio",
- "tracing",
- "tracing-futures",
- "url",
-]
-
-[[package]]
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-dependencies = [
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- "bytes",
- "const-hex",
- "enr",
- "ethers-core",
- "futures-core",
- "futures-timer",
- "futures-util",
- "hashers",
- "http",
- "instant",
- "jsonwebtoken",
- "once_cell",
- "pin-project",
- "reqwest",
- "serde",
- "serde_json",
- "thiserror",
- "tokio",
- "tracing",
- "tracing-futures",
- "url",
- "wasm-bindgen",
- "wasm-bindgen-futures",
- "web-sys",
- "ws_stream_wasm",
-]
-
-[[package]]
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-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
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-dependencies = [
- "async-trait",
- "coins-bip32",
- "coins-bip39",
- "const-hex",
- "elliptic-curve",
- "eth-keystore",
- "ethers-core",
- "rand 0.8.5",
- "sha2",
- "thiserror",
- "tracing",
-]
-
-[[package]]
-name = "ethers-solc"
-version = "2.0.14"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "66244a771d9163282646dbeffe0e6eca4dda4146b6498644e678ac6089b11edd"
-dependencies = [
- "cfg-if",
- "const-hex",
- "dirs",
- "dunce",
- "ethers-core",
- "glob",
- "home",
- "md-5",
- "num_cpus",
- "once_cell",
- "path-slash",
- "rayon",
- "regex",
- "semver 1.0.22",
- "serde",
- "serde_json",
- "solang-parser",
- "thiserror",
- "tiny-keccak",
- "tokio",
- "tracing",
- "walkdir",
- "yansi",
-]
-
-[[package]]
-name = "eyre"
-version = "0.6.12"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7cd915d99f24784cdc19fd37ef22b97e3ff0ae756c7e492e9fbfe897d61e2aec"
-dependencies = [
- "indenter",
- "once_cell",
-]
-
 [[package]]
 name = "ezkl"
 version = "0.0.0"
 dependencies = [
+ "alloy",
  "ark-std 0.3.0",
  "bincode",
  "chrono",
@@ -1802,7 +1837,9 @@ dependencies = [
  "criterion",
  "ecc",
  "env_logger",
- "ethers",
+ "ethabi",
+ "foundry-compilers",
+ "futures-util",
  "gag",
  "getrandom",
  "halo2_gadgets",
@@ -1824,7 +1861,6 @@ dependencies = [
  "pg_bigdecimal",
  "plotters",
  "portable-atomic",
- "postgres",
  "pyo3",
  "pyo3-asyncio",
  "pyo3-log",
@@ -1844,6 +1880,7 @@ dependencies = [
  "test-case",
  "thiserror",
  "tokio",
+ "tokio-postgres",
  "tokio-util",
  "tosubcommand",
  "tract-onnx",
@@ -2002,6 +2039,50 @@ dependencies = [
  "percent-encoding",
 ]
 
+[[package]]
+name = "foundry-compilers"
+version = "0.4.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f97f9de33410d0daf13834f818a8594c0ed277c848af750e40a9f28e67d62e3a"
+dependencies = [
+ "alloy-json-abi",
+ "alloy-primitives 0.7.2",
+ "auto_impl",
+ "cfg-if",
+ "dirs",
+ "dunce",
+ "home",
+ "itertools 0.12.1",
+ "md-5",
+ "memmap2",
+ "once_cell",
+ "path-slash",
+ "rayon",
+ "regex",
+ "semver 1.0.22",
+ "serde",
+ "serde_json",
+ "sha2",
+ "solang-parser",
+ "svm-rs",
+ "svm-rs-builds",
+ "thiserror",
+ "tokio",
+ "tracing",
+ "walkdir",
+ "yansi 1.0.1",
+]
+
+[[package]]
+name = "fs4"
+version = "0.8.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "21dabded2e32cd57ded879041205c60a4a4c4bab47bd0fd2fa8b01f30849f02b"
+dependencies = [
+ "rustix",
+ "windows-sys 0.52.0",
+]
+
 [[package]]
 name = "fuchsia-cprng"
 version = "0.1.1"
@@ -2062,16 +2143,6 @@ version = "0.3.30"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "a44623e20b9681a318efdd71c299b6b222ed6f231972bfe2f224ebad6311f0c1"
 
-[[package]]
-name = "futures-locks"
-version = "0.7.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "45ec6fe3675af967e67c5536c0b9d44e34e6c52f86bedc4ea49c5317b8e94d06"
-dependencies = [
- "futures-channel",
- "futures-task",
-]
-
 [[package]]
 name = "futures-macro"
 version = "0.3.30"
@@ -2095,16 +2166,6 @@ version = "0.3.30"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "38d84fa142264698cdce1a9f9172cf383a0c82de1bddcf3092901442c4097004"
 
-[[package]]
-name = "futures-timer"
-version = "3.0.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f288b0a4f20f9a56b5d1da57e2227c661b7b16168e2f72365f57b63326e29b24"
-dependencies = [
- "gloo-timers",
- "send_wrapper 0.4.0",
-]
-
 [[package]]
 name = "futures-util"
 version = "0.3.30"
@@ -2124,13 +2185,10 @@ dependencies = [
 ]
 
 [[package]]
-name = "fxhash"
-version = "0.2.1"
+name = "futures-utils-wasm"
+version = "0.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c31b6d751ae2c7f11320402d34e41349dd1016f8d5d45e48c4312bc8625af50c"
-dependencies = [
- "byteorder",
-]
+checksum = "42012b0f064e01aa58b545fe3727f90f7dd4020f4a3ea735b50344965f5a57e9"
 
 [[package]]
 name = "gag"
@@ -2178,18 +2236,6 @@ version = "0.3.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d2fabcfbdc87f4758337ca535fb41a6d701b65693ce38287d856d1674551ec9b"
 
-[[package]]
-name = "gloo-timers"
-version = "0.2.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9b995a66bb87bebce9a0f4a95aed01daca4872c050bfcb21653361c03bc35e5c"
-dependencies = [
- "futures-channel",
- "futures-core",
- "js-sys",
- "wasm-bindgen",
-]
-
 [[package]]
 name = "group"
 version = "0.13.0"
@@ -2201,25 +2247,6 @@ dependencies = [
  "subtle",
 ]
 
-[[package]]
-name = "h2"
-version = "0.3.25"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4fbd2820c5e49886948654ab546d0688ff24530286bdcf8fca3cefb16d4618eb"
-dependencies = [
- "bytes",
- "fnv",
- "futures-core",
- "futures-sink",
- "futures-util",
- "http",
- "indexmap",
- "slab",
- "tokio",
- "tokio-util",
- "tracing",
-]
-
 [[package]]
 name = "half"
 version = "1.8.3"
@@ -2405,15 +2432,6 @@ dependencies = [
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 ]
 
-[[package]]
-name = "hashers"
-version = "1.0.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b2bca93b15ea5a746f220e56587f71e73c6165eab783df9e26590069953e3c30"
-dependencies = [
- "fxhash",
-]
-
 [[package]]
 name = "heck"
 version = "0.4.1"
@@ -2476,9 +2494,9 @@ dependencies = [
 
 [[package]]
 name = "http"
-version = "0.2.12"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "601cbb57e577e2f5ef5be8e7b83f0f63994f25aa94d673e54a92d5c516d101f1"
+checksum = "21b9ddb458710bc376481b842f5da65cdf31522de232c1ca8146abce2a358258"
 dependencies = [
  "bytes",
  "fnv",
@@ -2487,12 +2505,24 @@ dependencies = [
 
 [[package]]
 name = "http-body"
-version = "0.4.6"
+version = "1.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1cac85db508abc24a2e48553ba12a996e87244a0395ce011e62b37158745d643"
+dependencies = [
+ "bytes",
+ "http",
+]
+
+[[package]]
+name = "http-body-util"
+version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ceab25649e9960c0311ea418d17bee82c0dcec1bd053b5f9a66e265a693bed2"
+checksum = "0475f8b2ac86659c21b64320d5d653f9efe42acd2a4e560073ec61a155a34f1d"
 dependencies = [
  "bytes",
+ "futures-core",
  "http",
+ "http-body",
  "pin-project-lite",
 ]
 
@@ -2502,12 +2532,6 @@ version = "1.8.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d897f394bad6a705d5f4104762e116a75639e470d80901eed05a860a95cb1904"
 
-[[package]]
-name = "httpdate"
-version = "1.0.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "df3b46402a9d5adb4c86a0cf463f42e19994e3ee891101b1841f30a545cb49a9"
-
 [[package]]
 name = "humantime"
 version = "2.1.0"
@@ -2516,39 +2540,74 @@ checksum = "9a3a5bfb195931eeb336b2a7b4d761daec841b97f947d34394601737a7bba5e4"
 
 [[package]]
 name = "hyper"
-version = "0.14.28"
+version = "1.3.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf96e135eb83a2a8ddf766e426a841d8ddd7449d5f00d34ea02b41d2f19eef80"
+checksum = "fe575dd17d0862a9a33781c8c4696a55c320909004a67a00fb286ba8b1bc496d"
 dependencies = [
  "bytes",
  "futures-channel",
- "futures-core",
  "futures-util",
- "h2",
  "http",
  "http-body",
  "httparse",
- "httpdate",
  "itoa",
  "pin-project-lite",
- "socket2",
+ "smallvec",
  "tokio",
- "tower-service",
- "tracing",
  "want",
 ]
 
+[[package]]
+name = "hyper-rustls"
+version = "0.26.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a0bea761b46ae2b24eb4aef630d8d1c398157b6fc29e6350ecf090a0b70c952c"
+dependencies = [
+ "futures-util",
+ "http",
+ "hyper",
+ "hyper-util",
+ "rustls",
+ "rustls-pki-types",
+ "tokio",
+ "tokio-rustls",
+ "tower-service",
+]
+
 [[package]]
 name = "hyper-tls"
-version = "0.5.0"
+version = "0.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d6183ddfa99b85da61a140bea0efc93fdf56ceaa041b37d553518030827f9905"
+checksum = "70206fc6890eaca9fde8a0bf71caa2ddfc9fe045ac9e5c70df101a7dbde866e0"
 dependencies = [
  "bytes",
+ "http-body-util",
  "hyper",
+ "hyper-util",
  "native-tls",
  "tokio",
  "tokio-native-tls",
+ "tower-service",
+]
+
+[[package]]
+name = "hyper-util"
+version = "0.1.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ca38ef113da30126bbff9cd1705f9273e15d45498615d138b0c20279ac7a76aa"
+dependencies = [
+ "bytes",
+ "futures-channel",
+ "futures-util",
+ "http",
+ "http-body",
+ "hyper",
+ "pin-project-lite",
+ "socket2",
+ "tokio",
+ "tower",
+ "tower-service",
+ "tracing",
 ]
 
 [[package]]
@@ -2646,12 +2705,6 @@ dependencies = [
  "syn 1.0.109",
 ]
 
-[[package]]
-name = "indenter"
-version = "0.3.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ce23b50ad8242c51a442f3ff322d56b02f08852c77e4c0b4d3fd684abc89c683"
-
 [[package]]
 name = "indexmap"
 version = "2.2.5"
@@ -2682,15 +2735,6 @@ version = "2.0.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1e186cfbae8084e513daff4240b4797e342f988cecda4fb6c939150f96315fd8"
 
-[[package]]
-name = "inout"
-version = "0.1.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a0c10553d664a4d0bcff9f4215d0aac67a639cc68ef660840afe309b807bc9f5"
-dependencies = [
- "generic-array",
-]
-
 [[package]]
 name = "instant"
 version = "0.1.12"
@@ -2784,20 +2828,6 @@ dependencies = [
  "wasm-bindgen",
 ]
 
-[[package]]
-name = "jsonwebtoken"
-version = "8.3.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6971da4d9c3aa03c3d8f3ff0f4155b534aad021292003895a469716b2a230378"
-dependencies = [
- "base64 0.21.7",
- "pem",
- "ring",
- "serde",
- "serde_json",
- "simple_asn1",
-]
-
 [[package]]
 name = "k256"
 version = "0.13.3"
@@ -2809,7 +2839,6 @@ dependencies = [
  "elliptic-curve",
  "once_cell",
  "sha2",
- "signature",
 ]
 
 [[package]]
@@ -2821,6 +2850,16 @@ dependencies = [
  "cpufeatures",
 ]
 
+[[package]]
+name = "keccak-asm"
+version = "0.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bb8515fff80ed850aea4a1595f2e519c003e2a00a82fe168ebf5269196caf444"
+dependencies = [
+ "digest 0.10.7",
+ "sha3-asm",
+]
+
 [[package]]
 name = "kstring"
 version = "2.0.0"
@@ -2867,7 +2906,7 @@ version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646"
 dependencies = [
- "spin",
+ "spin 0.5.2",
 ]
 
 [[package]]
@@ -2972,6 +3011,15 @@ version = "0.4.21"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c"
 
+[[package]]
+name = "lru"
+version = "0.12.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d3262e75e648fce39813cb56ac41f3c3e3f65217ebf3844d818d1f9398cfb0dc"
+dependencies = [
+ "hashbrown 0.14.3",
+]
+
 [[package]]
 name = "maingate"
 version = "0.1.0"
@@ -3316,31 +3364,6 @@ version = "0.3.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c08d65885ee38876c4f86fa503fb49d7b507c2b62552df7c70b2fce627e06381"
 
-[[package]]
-name = "open-fastrlp"
-version = "0.1.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "786393f80485445794f6043fd3138854dd109cc6c4bd1a6383db304c9ce9b9ce"
-dependencies = [
- "arrayvec 0.7.4",
- "auto_impl",
- "bytes",
- "ethereum-types",
- "open-fastrlp-derive",
-]
-
-[[package]]
-name = "open-fastrlp-derive"
-version = "0.1.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "003b2be5c6c53c1cfeb0a238b8a1c3915cd410feb684457a36c10038f764bb1c"
-dependencies = [
- "bytes",
- "proc-macro2",
- "quote",
- "syn 1.0.109",
-]
-
 [[package]]
 name = "openssl"
 version = "0.10.64"
@@ -3501,34 +3524,6 @@ version = "0.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1e91099d4268b0e11973f036e885d652fb0b21fedcf69738c627f94db6a44f42"
 
-[[package]]
-name = "pbkdf2"
-version = "0.11.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "83a0692ec44e4cf1ef28ca317f14f8f07da2d95ec3fa01f86e4467b725e60917"
-dependencies = [
- "digest 0.10.7",
-]
-
-[[package]]
-name = "pbkdf2"
-version = "0.12.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f8ed6a7761f76e3b9f92dfb0a60a6a6477c61024b775147ff0973a02653abaf2"
-dependencies = [
- "digest 0.10.7",
- "hmac",
-]
-
-[[package]]
-name = "pem"
-version = "1.1.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a8835c273a76a90455d7344889b0964598e3316e2a79ede8e36f16bdcf2228b8"
-dependencies = [
- "base64 0.13.1",
-]
-
 [[package]]
 name = "percent-encoding"
 version = "2.3.1"
@@ -3603,16 +3598,6 @@ dependencies = [
  "postgres",
 ]
 
-[[package]]
-name = "pharos"
-version = "0.5.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e9567389417feee6ce15dd6527a8a1ecac205ef62c2932bcf3d9f6fc5b78b414"
-dependencies = [
- "futures",
- "rustc_version 0.4.0",
-]
-
 [[package]]
 name = "phf"
 version = "0.11.2"
@@ -3810,16 +3795,6 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "925383efa346730478fb4838dbe9137d2a47675ad789c546d150a6e1dd4ab31c"
 
-[[package]]
-name = "prettyplease"
-version = "0.2.16"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a41cf62165e97c7f814d2221421dbb9afcbcdb0a88068e5ea206e19951c2cbb5"
-dependencies = [
- "proc-macro2",
- "syn 2.0.53",
-]
-
 [[package]]
 name = "primal-check"
 version = "0.3.3"
@@ -3839,20 +3814,9 @@ dependencies = [
  "impl-codec",
  "impl-rlp",
  "impl-serde",
- "scale-info",
  "uint",
 ]
 
-[[package]]
-name = "proc-macro-crate"
-version = "1.3.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7f4c021e1093a56626774e81216a4ce732a735e5bad4868a03f3ed65ca0c3919"
-dependencies = [
- "once_cell",
- "toml_edit 0.19.15",
-]
-
 [[package]]
 name = "proc-macro-crate"
 version = "2.0.0"
@@ -3910,6 +3874,8 @@ version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "31b476131c3c86cb68032fdc5cb6d5a1045e3e42d96b69fa599fd77701e1f5bf"
 dependencies = [
+ "bit-set",
+ "bit-vec",
  "bitflags 2.5.0",
  "lazy_static",
  "num-traits",
@@ -3917,6 +3883,8 @@ dependencies = [
  "rand_chacha",
  "rand_xorshift",
  "regex-syntax",
+ "rusty-fork",
+ "tempfile",
  "unarray",
 ]
 
@@ -3945,9 +3913,9 @@ dependencies = [
 
 [[package]]
 name = "pyo3"
-version = "0.20.3"
+version = "0.21.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "53bdbb96d49157e65d45cc287af5f32ffadd5f4761438b527b055fb0d4bb8233"
+checksum = "a5e00b96a521718e08e03b1a622f01c8a8deb50719335de3f60b3b3950f069d8"
 dependencies = [
  "cfg-if",
  "indoc",
@@ -3963,9 +3931,8 @@ dependencies = [
 
 [[package]]
 name = "pyo3-asyncio"
-version = "0.20.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6ea6b68e93db3622f3bb3bf363246cf948ed5375afe7abff98ccbdd50b184995"
+version = "0.21.0"
+source = "git+https://github.com/jopemachine/pyo3-asyncio/?branch=migration-pyo3-0.21#d1ec64076dd1b5c797db4b7b811f588466956d20"
 dependencies = [
  "futures",
  "once_cell",
@@ -3977,9 +3944,8 @@ dependencies = [
 
 [[package]]
 name = "pyo3-asyncio-macros"
-version = "0.20.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "56c467178e1da6252c95c29ecf898b133f742e9181dca5def15dc24e19d45a39"
+version = "0.21.0"
+source = "git+https://github.com/jopemachine/pyo3-asyncio/?branch=migration-pyo3-0.21#d1ec64076dd1b5c797db4b7b811f588466956d20"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -3988,9 +3954,9 @@ dependencies = [
 
 [[package]]
 name = "pyo3-build-config"
-version = "0.20.3"
+version = "0.21.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "deaa5745de3f5231ce10517a1f5dd97d53e5a2fd77aa6b5842292085831d48d7"
+checksum = "7883df5835fafdad87c0d888b266c8ec0f4c9ca48a5bed6bbb592e8dedee1b50"
 dependencies = [
  "once_cell",
  "target-lexicon",
@@ -3998,9 +3964,9 @@ dependencies = [
 
 [[package]]
 name = "pyo3-ffi"
-version = "0.20.3"
+version = "0.21.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "62b42531d03e08d4ef1f6e85a2ed422eb678b8cd62b762e53891c05faf0d4afa"
+checksum = "01be5843dc60b916ab4dad1dca6d20b9b4e6ddc8e15f50c47fe6d85f1fb97403"
 dependencies = [
  "libc",
  "pyo3-build-config",
@@ -4008,9 +3974,9 @@ dependencies = [
 
 [[package]]
 name = "pyo3-log"
-version = "0.9.0"
+version = "0.10.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4c10808ee7250403bedb24bc30c32493e93875fef7ba3e4292226fe924f398bd"
+checksum = "2af49834b8d2ecd555177e63b273b708dea75150abc6f5341d0a6e1a9623976c"
 dependencies = [
  "arc-swap",
  "log",
@@ -4019,9 +3985,9 @@ dependencies = [
 
 [[package]]
 name = "pyo3-macros"
-version = "0.20.3"
+version = "0.21.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7305c720fa01b8055ec95e484a6eca7a83c841267f0dd5280f0c8b8551d2c158"
+checksum = "77b34069fc0682e11b31dbd10321cbf94808394c56fd996796ce45217dfac53c"
 dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
@@ -4031,9 +3997,9 @@ dependencies = [
 
 [[package]]
 name = "pyo3-macros-backend"
-version = "0.20.3"
+version = "0.21.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7c7e9b68bb9c3149c5b0cade5d07f953d6d125eb4337723c4ccdb665f1f96185"
+checksum = "08260721f32db5e1a5beae69a55553f56b99bd0e1c3e6e0a5e8851a9d0f5a85c"
 dependencies = [
  "heck 0.4.1",
  "proc-macro2",
@@ -4042,6 +4008,12 @@ dependencies = [
  "syn 2.0.53",
 ]
 
+[[package]]
+name = "quick-error"
+version = "1.2.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a1d01941d82fa2ab50be1e79e6714289dd7cde78eba4c074bc5a4374f650dfe0"
+
 [[package]]
 name = "quote"
 version = "1.0.35"
@@ -4231,20 +4203,22 @@ dependencies = [
 
 [[package]]
 name = "reqwest"
-version = "0.11.27"
+version = "0.12.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "dd67538700a17451e7cba03ac727fb961abb7607553461627b97de0b89cf4a62"
+checksum = "566cafdd92868e0939d3fb961bd0dc25fcfaaed179291093b3d43e6b3150ea10"
 dependencies = [
- "base64 0.21.7",
+ "base64 0.22.1",
  "bytes",
- "encoding_rs",
+ "futures-channel",
  "futures-core",
  "futures-util",
- "h2",
  "http",
  "http-body",
+ "http-body-util",
  "hyper",
+ "hyper-rustls",
  "hyper-tls",
+ "hyper-util",
  "ipnet",
  "js-sys",
  "log",
@@ -4254,14 +4228,17 @@ dependencies = [
  "once_cell",
  "percent-encoding",
  "pin-project-lite",
+ "rustls",
+ "rustls-native-certs",
  "rustls-pemfile",
+ "rustls-pki-types",
  "serde",
  "serde_json",
  "serde_urlencoded",
  "sync_wrapper",
- "system-configuration",
  "tokio",
  "tokio-native-tls",
+ "tokio-rustls",
  "tokio-util",
  "tower-service",
  "url",
@@ -4269,6 +4246,7 @@ dependencies = [
  "wasm-bindgen-futures",
  "wasm-streams",
  "web-sys",
+ "webpki-roots",
  "winreg",
 ]
 
@@ -4313,7 +4291,7 @@ version = "1.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "51187b852d9e458816a2e19c81f1dd6c924077e1a8fccd16e4f044f865f299d7"
 dependencies = [
- "alloy-primitives",
+ "alloy-primitives 0.4.2",
  "alloy-rlp",
  "auto_impl",
  "bitflags 2.5.0",
@@ -4335,17 +4313,17 @@ dependencies = [
 
 [[package]]
 name = "ring"
-version = "0.16.20"
+version = "0.17.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3053cf52e236a3ed746dfc745aa9cacf1b791d846bdaf412f60a8d7d6e17c8fc"
+checksum = "c17fa4cb658e3583423e915b9f3acc01cceaee1860e33d59ebae66adc3a2dc0d"
 dependencies = [
  "cc",
+ "cfg-if",
+ "getrandom",
  "libc",
- "once_cell",
- "spin",
+ "spin 0.9.8",
  "untrusted",
- "web-sys",
- "winapi",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -4364,21 +4342,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "bb919243f34364b6bd2fc10ef797edbfa75f33c252e7998527479c6d6b47e1ec"
 dependencies = [
  "bytes",
- "rlp-derive",
  "rustc-hex",
 ]
 
-[[package]]
-name = "rlp-derive"
-version = "0.1.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e33d7b2abe0c340d8797fe2907d3f20d3b5ea5908683618bfe80df7f621f672a"
-dependencies = [
- "proc-macro2",
- "quote",
- "syn 1.0.109",
-]
-
 [[package]]
 name = "ruint"
 version = "1.12.1"
@@ -4497,66 +4463,90 @@ dependencies = [
 ]
 
 [[package]]
-name = "rustls-pemfile"
-version = "1.0.4"
+name = "rustls"
+version = "0.22.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1c74cae0a4cf6ccbbf5f359f08efdf8ee7e1dc532573bf0db71968cb56b1448c"
+checksum = "bf4ef73721ac7bcd79b2b315da7779d8fc09718c6b3d2d1b2d94850eb8c18432"
 dependencies = [
- "base64 0.21.7",
+ "log",
+ "ring",
+ "rustls-pki-types",
+ "rustls-webpki",
+ "subtle",
+ "zeroize",
 ]
 
 [[package]]
-name = "rustversion"
-version = "1.0.14"
+name = "rustls-native-certs"
+version = "0.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ffc183a10b4478d04cbbbfc96d0873219d962dd5accaff2ffbd4ceb7df837f4"
+checksum = "8f1fb85efa936c42c6d5fc28d2629bb51e4b2f4b8a5211e297d599cc5a093792"
+dependencies = [
+ "openssl-probe",
+ "rustls-pemfile",
+ "rustls-pki-types",
+ "schannel",
+ "security-framework",
+]
 
 [[package]]
-name = "ryu"
-version = "1.0.17"
+name = "rustls-pemfile"
+version = "2.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e86697c916019a8588c99b5fac3cead74ec0b4b819707a682fd4d23fa0ce1ba1"
+checksum = "29993a25686778eb88d4189742cd713c9bce943bc54251a33509dc63cbacf73d"
+dependencies = [
+ "base64 0.22.1",
+ "rustls-pki-types",
+]
 
 [[package]]
-name = "salsa20"
-version = "0.10.2"
+name = "rustls-pki-types"
+version = "1.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "976295e77ce332211c0d24d92c0e83e50f5c5f046d11082cea19f3df13a3562d"
+
+[[package]]
+name = "rustls-webpki"
+version = "0.102.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "97a22f5af31f73a954c10289c93e8a50cc23d971e80ee446f1f6f7137a088213"
+checksum = "f3bce581c0dd41bce533ce695a1437fa16a7ab5ac3ccfa99fe1a620a7885eabf"
 dependencies = [
- "cipher",
+ "ring",
+ "rustls-pki-types",
+ "untrusted",
 ]
 
 [[package]]
-name = "same-file"
-version = "1.0.6"
+name = "rustversion"
+version = "1.0.14"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7ffc183a10b4478d04cbbbfc96d0873219d962dd5accaff2ffbd4ceb7df837f4"
+
+[[package]]
+name = "rusty-fork"
+version = "0.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "93fc1dc3aaa9bfed95e02e6eadabb4baf7e3078b0bd1b4d7b6b0b68378900502"
+checksum = "cb3dcc6e454c328bb824492db107ab7c0ae8fcffe4ad210136ef014458c1bc4f"
 dependencies = [
- "winapi-util",
+ "fnv",
+ "quick-error",
+ "tempfile",
+ "wait-timeout",
 ]
 
 [[package]]
-name = "scale-info"
-version = "2.11.0"
+name = "ryu"
+version = "1.0.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2ef2175c2907e7c8bc0a9c3f86aeb5ec1f3b275300ad58a44d0c3ae379a5e52e"
-dependencies = [
- "cfg-if",
- "derive_more",
- "parity-scale-codec",
- "scale-info-derive",
-]
+checksum = "e86697c916019a8588c99b5fac3cead74ec0b4b819707a682fd4d23fa0ce1ba1"
 
 [[package]]
-name = "scale-info-derive"
-version = "2.11.0"
+name = "same-file"
+version = "1.0.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "634d9b8eb8fd61c5cdd3390d9b2132300a7e7618955b98b8416f118c1b4e144f"
+checksum = "93fc1dc3aaa9bfed95e02e6eadabb4baf7e3078b0bd1b4d7b6b0b68378900502"
 dependencies = [
- "proc-macro-crate 1.3.1",
- "proc-macro2",
- "quote",
- "syn 1.0.109",
+ "winapi-util",
 ]
 
 [[package]]
@@ -4589,18 +4579,6 @@ version = "1.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49"
 
-[[package]]
-name = "scrypt"
-version = "0.10.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9f9e24d2b632954ded8ab2ef9fea0a0c769ea56ea98bddbafbad22caeeadf45d"
-dependencies = [
- "hmac",
- "pbkdf2 0.11.0",
- "salsa20",
- "sha2",
-]
-
 [[package]]
 name = "sec1"
 version = "0.7.3"
@@ -4665,18 +4643,6 @@ dependencies = [
  "pest",
 ]
 
-[[package]]
-name = "send_wrapper"
-version = "0.4.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f638d531eccd6e23b980caf34876660d38e265409d8e99b397ab71eb3612fad0"
-
-[[package]]
-name = "send_wrapper"
-version = "0.6.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cd0b0ec5f1c1ca621c432a25813d8d60c88abe6d3e08a3eb9cf37d97a0fe3d73"
-
 [[package]]
 name = "seq-macro"
 version = "0.3.5"
@@ -4744,15 +4710,6 @@ dependencies = [
  "serde",
 ]
 
-[[package]]
-name = "serde_spanned"
-version = "0.6.5"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eb3622f419d1296904700073ea6cc23ad690adbd66f13ea683df73298736f0c1"
-dependencies = [
- "serde",
-]
-
 [[package]]
 name = "serde_urlencoded"
 version = "0.7.1"
@@ -4811,6 +4768,16 @@ dependencies = [
  "keccak",
 ]
 
+[[package]]
+name = "sha3-asm"
+version = "0.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bac61da6b35ad76b195eb4771210f947734321a8d81d7738e1580d953bc7a15e"
+dependencies = [
+ "cc",
+ "cfg-if",
+]
+
 [[package]]
 name = "shellexpand"
 version = "3.1.0"
@@ -4830,18 +4797,6 @@ dependencies = [
  "rand_core 0.6.4",
 ]
 
-[[package]]
-name = "simple_asn1"
-version = "0.6.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "adc4e5204eb1910f40f9cfa375f6f05b68c3abac4b6fd879c8ff5e7ae8a0a085"
-dependencies = [
- "num-bigint",
- "num-traits",
- "thiserror",
- "time",
-]
-
 [[package]]
 name = "siphasher"
 version = "0.3.11"
@@ -4914,6 +4869,12 @@ version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6e63cff320ae2c57904679ba7cb63280a3dc4613885beafb148ee7bf9aa9042d"
 
+[[package]]
+name = "spin"
+version = "0.9.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67"
+
 [[package]]
 name = "spki"
 version = "0.7.3"
@@ -4977,28 +4938,6 @@ version = "0.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5ee073c9e4cd00e28217186dbe12796d692868f432bf2e97ee73bed0c56dfa01"
 
-[[package]]
-name = "strum"
-version = "0.26.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5d8cec3501a5194c432b2b7976db6b7d10ec95c253208b45f83f7136aa985e29"
-dependencies = [
- "strum_macros",
-]
-
-[[package]]
-name = "strum_macros"
-version = "0.26.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c6cf59daf282c0a494ba14fd21610a0325f9f90ec9d1231dea26bcb1d696c946"
-dependencies = [
- "heck 0.4.1",
- "proc-macro2",
- "quote",
- "rustversion",
- "syn 2.0.53",
-]
-
 [[package]]
 name = "substrate-bn"
 version = "0.6.0"
@@ -5018,6 +4957,39 @@ version = "2.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "81cdd64d312baedb58e21336b31bc043b77e01cc99033ce76ef539f78e965ebc"
 
+[[package]]
+name = "svm-rs"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f7c7a55b859b986daa02c731cd07758d84b1db852665e45c5cfa6698c41d17cb"
+dependencies = [
+ "const-hex",
+ "dirs",
+ "fs4",
+ "once_cell",
+ "reqwest",
+ "semver 1.0.22",
+ "serde",
+ "serde_json",
+ "sha2",
+ "thiserror",
+ "url",
+ "zip",
+]
+
+[[package]]
+name = "svm-rs-builds"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f1bb4806c96207e7cde40fc238f9a1d570f3090f850a742e1e96665440615a31"
+dependencies = [
+ "build_const",
+ "const-hex",
+ "semver 1.0.22",
+ "serde_json",
+ "svm-rs",
+]
+
 [[package]]
 name = "syn"
 version = "1.0.109"
@@ -5041,31 +5013,22 @@ dependencies = [
 ]
 
 [[package]]
-name = "sync_wrapper"
-version = "0.1.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160"
-
-[[package]]
-name = "system-configuration"
-version = "0.5.1"
+name = "syn-solidity"
+version = "0.7.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ba3a3adc5c275d719af8cb4272ea1c4a6d668a777f37e115f6d11ddbc1c8e0e7"
+checksum = "5aa0cefd02f532035d83cfec82647c6eb53140b0485220760e669f4bad489e36"
 dependencies = [
- "bitflags 1.3.2",
- "core-foundation",
- "system-configuration-sys",
+ "paste",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.53",
 ]
 
 [[package]]
-name = "system-configuration-sys"
-version = "0.5.0"
+name = "sync_wrapper"
+version = "0.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a75fb188eb626b924683e3b95e3a48e63551fcfb51949de2f06a9d91dbee93c9"
-dependencies = [
- "core-foundation-sys",
- "libc",
-]
+checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160"
 
 [[package]]
 name = "tabled"
@@ -5209,6 +5172,15 @@ dependencies = [
  "syn 2.0.53",
 ]
 
+[[package]]
+name = "threadpool"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d050e60b33d41c19108b32cea32164033a9013fe3b46cbd4457559bfbf77afaa"
+dependencies = [
+ "num_cpus",
+]
+
 [[package]]
 name = "time"
 version = "0.3.23"
@@ -5334,6 +5306,29 @@ dependencies = [
  "whoami",
 ]
 
+[[package]]
+name = "tokio-rustls"
+version = "0.25.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "775e0c0f0adb3a2f22a00c4745d728b479985fc15ee7ca6a2608388c5569860f"
+dependencies = [
+ "rustls",
+ "rustls-pki-types",
+ "tokio",
+]
+
+[[package]]
+name = "tokio-stream"
+version = "0.1.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "267ac89e0bec6e691e5813911606935d77c476ff49024f98abcea3e7b15e37af"
+dependencies = [
+ "futures-core",
+ "pin-project-lite",
+ "tokio",
+ "tokio-util",
+]
+
 [[package]]
 name = "tokio-util"
 version = "0.7.10"
@@ -5348,37 +5343,11 @@ dependencies = [
  "tracing",
 ]
 
-[[package]]
-name = "toml"
-version = "0.8.12"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e9dd1545e8208b4a5af1aa9bbd0b4cf7e9ea08fabc5d0a5c67fcaafa17433aa3"
-dependencies = [
- "serde",
- "serde_spanned",
- "toml_datetime",
- "toml_edit 0.22.9",
-]
-
 [[package]]
 name = "toml_datetime"
 version = "0.6.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3550f4e9685620ac18a50ed434eb3aec30db8ba93b0287467bca5826ea25baf1"
-dependencies = [
- "serde",
-]
-
-[[package]]
-name = "toml_edit"
-version = "0.19.15"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1b5bb770da30e5cbfde35a2d7b9b8a2c4b8ef89548a7a6aeab5c9a576e3e7421"
-dependencies = [
- "indexmap",
- "toml_datetime",
- "winnow 0.5.40",
-]
 
 [[package]]
 name = "toml_edit"
@@ -5402,19 +5371,6 @@ dependencies = [
  "winnow 0.5.40",
 ]
 
-[[package]]
-name = "toml_edit"
-version = "0.22.9"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8e40bb779c5187258fd7aad0eb68cb8706a0a81fa712fbea808ab43c4b8374c4"
-dependencies = [
- "indexmap",
- "serde",
- "serde_spanned",
- "toml_datetime",
- "winnow 0.6.5",
-]
-
 [[package]]
 name = "tosubcommand"
 version = "0.1.0"
@@ -5433,6 +5389,28 @@ dependencies = [
  "syn 2.0.53",
 ]
 
+[[package]]
+name = "tower"
+version = "0.4.13"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b8fa9be0de6cf49e536ce1851f987bd21a43b771b09473c3549a6c853db37c1c"
+dependencies = [
+ "futures-core",
+ "futures-util",
+ "pin-project",
+ "pin-project-lite",
+ "tokio",
+ "tower-layer",
+ "tower-service",
+ "tracing",
+]
+
+[[package]]
+name = "tower-layer"
+version = "0.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c20c8dbed6283a09604c3e69b4b7eeb54e298b8a600d4d5ecb5ad39de609f1d0"
+
 [[package]]
 name = "tower-service"
 version = "0.3.2"
@@ -5445,6 +5423,7 @@ version = "0.1.40"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c3523ab5a71916ccf420eebdf5521fcef02141234bbc0b8a49f2fdc4544364ef"
 dependencies = [
+ "log",
  "pin-project-lite",
  "tracing-attributes",
  "tracing-core",
@@ -5470,16 +5449,6 @@ dependencies = [
  "once_cell",
 ]
 
-[[package]]
-name = "tracing-futures"
-version = "0.2.5"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "97d095ae15e245a057c8e8451bab9b3ee1e1f68e9ba2b4fbc18d0ac5237835f2"
-dependencies = [
- "pin-project",
- "tracing",
-]
-
 [[package]]
 name = "tract-core"
 version = "0.21.5-pre"
@@ -5716,9 +5685,9 @@ dependencies = [
 
 [[package]]
 name = "untrusted"
-version = "0.7.1"
+version = "0.9.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a156c684c91ea7d62626509bce3cb4e1d9ed5c4d978f7b4352658f96a4c26b4a"
+checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1"
 
 [[package]]
 name = "unzip-n"
@@ -5748,16 +5717,6 @@ version = "0.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "711b9620af191e0cdc7468a8d14e709c3dcdb115b36f838e601583af800a370a"
 
-[[package]]
-name = "uuid"
-version = "0.8.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bc5cf98d8186244414c848017f0e2676b3fcb46807f6668a97dfe67359a3c4b7"
-dependencies = [
- "getrandom",
- "serde",
-]
-
 [[package]]
 name = "valuable"
 version = "0.1.0"
@@ -5797,6 +5756,15 @@ dependencies = [
  "quote",
 ]
 
+[[package]]
+name = "wait-timeout"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9f200f5b12eb75f8c1ed65abd4b2db8a6e1b138a20de009dacee265a2498f3f6"
+dependencies = [
+ "libc",
+]
+
 [[package]]
 name = "walkdir"
 version = "2.5.0"
@@ -5977,6 +5945,15 @@ dependencies = [
  "wasm-bindgen",
 ]
 
+[[package]]
+name = "webpki-roots"
+version = "0.26.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b3de34ae270483955a94f4b21bdaaeb83d508bb84a01435f393818edb0012009"
+dependencies = [
+ "rustls-pki-types",
+]
+
 [[package]]
 name = "whoami"
 version = "1.5.1"
@@ -6180,33 +6157,14 @@ dependencies = [
 
 [[package]]
 name = "winreg"
-version = "0.50.0"
+version = "0.52.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "524e57b2c537c0f9b1e69f1965311ec12182b4122e45035b1508cd24d2adadb1"
+checksum = "a277a57398d4bfa075df44f501a17cfdf8542d224f0d36095a2adc7aee4ef0a5"
 dependencies = [
  "cfg-if",
  "windows-sys 0.48.0",
 ]
 
-[[package]]
-name = "ws_stream_wasm"
-version = "0.7.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7999f5f4217fe3818726b66257a4475f71e74ffd190776ad053fa159e50737f5"
-dependencies = [
- "async_io_stream",
- "futures",
- "js-sys",
- "log",
- "pharos",
- "rustc_version 0.4.0",
- "send_wrapper 0.6.0",
- "thiserror",
- "wasm-bindgen",
- "wasm-bindgen-futures",
- "web-sys",
-]
-
 [[package]]
 name = "wyz"
 version = "0.5.1"
@@ -6233,6 +6191,12 @@ version = "0.5.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "09041cd90cf85f7f8b2df60c646f853b7f535ce68f85244eb6731cf89fa498ec"
 
+[[package]]
+name = "yansi"
+version = "1.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cfe53a6657fd280eaa890a3bc59152892ffa3e30101319d168b781ed6529b049"
+
 [[package]]
 name = "zerocopy"
 version = "0.7.32"
@@ -6272,3 +6236,19 @@ dependencies = [
  "quote",
  "syn 2.0.53",
 ]
+
+[[package]]
+name = "zip"
+version = "1.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "006d078b7b6fc587bb25e022ad39e7086f44e5c4fef6076964ea601533241beb"
+dependencies = [
+ "arbitrary",
+ "crc32fast",
+ "crossbeam-utils",
+ "displaydoc",
+ "flate2",
+ "indexmap",
+ "num_enum",
+ "thiserror",
+]
diff --git a/Cargo.toml b/Cargo.toml
index 7604376b1..160269d45 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -45,42 +45,42 @@ portable-atomic = "1.6.0"
 tosubcommand = { git = "https://github.com/zkonduit/enum_to_subcommand", package = "tosubcommand" }
 metal = { git = "https://github.com/gfx-rs/metal-rs", optional = true }
 
-
 # evm related deps
 [target.'cfg(not(target_arch = "wasm32"))'.dependencies]
-ethers = { version = "2.0.11", default_features = false, features = [
-    "ethers-solc",
-] }
+alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", features = ["provider-http", "signers", "contract", "rpc-types-eth", "signer-wallet", "node-bindings"] }
+foundry-compilers = {version = "0.4.1", features = ["svm-solc"]}
+ethabi = "18"
 indicatif = { version = "0.17.5", features = ["rayon"] }
 gag = { version = "1.0.0", default_features = false }
 instant = { version = "0.1" }
-reqwest = { version = "0.11.14", default-features = false, features = [
+reqwest = { version = "0.12.4", default-features = false, features = [
     "default-tls",
     "multipart",
     "stream",
 ] }
 openssl = { version = "0.10.55", features = ["vendored"] }
-postgres = "0.19.5"
+tokio-postgres = "0.7.10"
 pg_bigdecimal = "0.1.5"
+futures-util = "0.3.30"
 lazy_static = "1.4.0"
 colored_json = { version = "3.0.1", default_features = false, optional = true }
 plotters = { version = "0.3.0", default_features = false, optional = true }
 regex = { version = "1", default_features = false }
-tokio = { version = "1.26.0", default_features = false, features = [
+tokio = { version = "1.35", default_features = false, features = [
     "macros",
-    "rt",
+    "rt-multi-thread"
 ] }
 tokio-util = { version = "0.7.9", features = ["codec"] }
-pyo3 = { version = "0.20.2", features = [
+pyo3 = { version = "0.21.2", features = [
     "extension-module",
     "abi3-py37",
     "macros",
 ], default_features = false, optional = true }
-pyo3-asyncio = { version = "0.20.0", features = [
+pyo3-asyncio = { git = "https://github.com/jopemachine/pyo3-asyncio/", branch="migration-pyo3-0.21", features = [
     "attributes",
     "tokio-runtime",
 ], default_features = false, optional = true }
-pyo3-log = { version = "0.9.0", default_features = false, optional = true }
+pyo3-log = { version = "0.10.0", default_features = false, optional = true }
 tract-onnx = { git = "https://github.com/sonos/tract/", rev = "05ebf550aa9922b221af4635c21a67a8d2af12a9", default_features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
 
@@ -178,7 +178,7 @@ required-features = ["ezkl"]
 
 [features]
 web = ["wasm-bindgen-rayon"]
-default = ["ezkl", "mv-lookup"]
+default = ["ezkl", "mv-lookup", "no-banner"]
 onnx = ["dep:tract-onnx"]
 python-bindings = ["pyo3", "pyo3-log", "pyo3-asyncio"]
 ezkl = [
diff --git a/README.md b/README.md
index 21ba7d4bc..056f760cf 100644
--- a/README.md
+++ b/README.md
@@ -91,9 +91,9 @@ You can install the library from source
 cargo install --locked --path .
 ```
 
-You will need a functioning installation of `solc` in order to run `ezkl` properly.
-[solc-select](https://github.com/crytic/solc-select) is recommended.
-Follow the instructions on [solc-select](https://github.com/crytic/solc-select) to activate `solc` in your environment.
+`ezkl` now auto-manages solc installation for you.
+
+
 
 
 #### building python bindings
diff --git a/contracts/AttestData.sol b/contracts/AttestData.sol
index e542075e5..a5c08c660 100644
--- a/contracts/AttestData.sol
+++ b/contracts/AttestData.sol
@@ -1,6 +1,97 @@
 // SPDX-License-Identifier: MIT
 pragma solidity ^0.8.20;
-import './LoadInstances.sol';
+contract LoadInstances {
+    /**
+     * @dev Parse the instances array from the Halo2Verifier encoded calldata.
+     * @notice must pass encoded bytes from memory
+     * @param encoded - verifier calldata
+     */
+    function getInstancesMemory(
+        bytes memory encoded
+    ) internal pure returns (uint256[] memory instances) {
+        bytes4 funcSig;
+        uint256 instances_offset;
+        uint256 instances_length;
+        assembly {
+            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
+            funcSig := mload(add(encoded, 0x20))
+
+            // Fetch instances offset which is 4 + 32 + 32 bytes away from
+            // start of encoded for `verifyProof(bytes,uint256[])`,
+            // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
+
+            instances_offset := mload(
+                add(encoded, add(0x44, mul(0x20, eq(funcSig, 0xaf83a18d))))
+            )
+
+            instances_length := mload(add(add(encoded, 0x24), instances_offset))
+        }
+        instances = new uint256[](instances_length); // Allocate memory for the instances array.
+        assembly {
+            // Now instances points to the start of the array data
+            // (right after the length field).
+            for {
+                let i := 0x20
+            } lt(i, add(mul(instances_length, 0x20), 0x20)) {
+                i := add(i, 0x20)
+            } {
+                mstore(
+                    add(instances, i),
+                    mload(add(add(encoded, add(i, 0x24)), instances_offset))
+                )
+            }
+        }
+    }
+    /**
+     * @dev Parse the instances array from the Halo2Verifier encoded calldata.
+     * @notice must pass encoded bytes from calldata
+     * @param encoded - verifier calldata
+     */
+    function getInstancesCalldata(
+        bytes calldata encoded
+    ) internal pure returns (uint256[] memory instances) {
+        bytes4 funcSig;
+        uint256 instances_offset;
+        uint256 instances_length;
+        assembly {
+            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
+            funcSig := calldataload(encoded.offset)
+
+            // Fetch instances offset which is 4 + 32 + 32 bytes away from
+            // start of encoded for `verifyProof(bytes,uint256[])`,
+            // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
+
+            instances_offset := calldataload(
+                add(
+                    encoded.offset,
+                    add(0x24, mul(0x20, eq(funcSig, 0xaf83a18d)))
+                )
+            )
+
+            instances_length := calldataload(
+                add(add(encoded.offset, 0x04), instances_offset)
+            )
+        }
+        instances = new uint256[](instances_length); // Allocate memory for the instances array.
+        assembly {
+            // Now instances points to the start of the array data
+            // (right after the length field).
+
+            for {
+                let i := 0x20
+            } lt(i, add(mul(instances_length, 0x20), 0x20)) {
+                i := add(i, 0x20)
+            } {
+                mstore(
+                    add(instances, i),
+                    calldataload(
+                        add(add(encoded.offset, add(i, 0x04)), instances_offset)
+                    )
+                )
+            }
+        }
+    }
+}
 
 // This contract serves as a Data Attestation Verifier for the EZKL model.
 // It is designed to read and attest to instances of proofs generated from a specified circuit.
@@ -34,11 +125,14 @@ contract DataAttestation is LoadInstances {
     address public admin;
 
     /**
-     * @notice EZKL P value 
+     * @notice EZKL P value
      * @dev In order to prevent the verifier from accepting two version of the same pubInput, n and the quantity (n + P),  where n + P <= 2^256, we require that all instances are stricly less than P. a
      * @dev The reason for this is that the assmebly code of the verifier performs all arithmetic operations modulo P and as a consequence can't distinguish between n and n + P.
      */
-    uint256 constant ORDER = uint256(0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001); 
+    uint256 constant ORDER =
+        uint256(
+            0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001
+        );
 
     uint256 constant INPUT_CALLS = 0;
 
@@ -69,7 +163,7 @@ contract DataAttestation is LoadInstances {
 
     function updateAdmin(address _admin) external {
         require(msg.sender == admin, "Only admin can update admin");
-        if(_admin == address(0)) {
+        if (_admin == address(0)) {
             revert();
         }
         admin = _admin;
@@ -80,7 +174,7 @@ contract DataAttestation is LoadInstances {
         bytes[][] memory _callData,
         uint256[][] memory _decimals
     ) external {
-        require(msg.sender == admin, "Only admin can update instanceOffset");
+        require(msg.sender == admin, "Only admin can update account calls");
         populateAccountCalls(_contractAddresses, _callData, _decimals);
     }
 
@@ -111,7 +205,10 @@ contract DataAttestation is LoadInstances {
             // count the total number of storage reads across all of the accounts
             counter += _callData[i].length;
         }
-        require(counter == INPUT_CALLS + OUTPUT_CALLS, "Invalid number of calls");
+        require(
+            counter == INPUT_CALLS + OUTPUT_CALLS,
+            "Invalid number of calls"
+        );
     }
 
     function mulDiv(
@@ -167,7 +264,7 @@ contract DataAttestation is LoadInstances {
      * @dev Quantize the data returned from the account calls to the scale used by the EZKL model.
      * @param data - The data returned from the account calls.
      * @param decimals - The number of decimals the data returned from the account calls has (for floating point representation).
-     * @param scale - The scale used to convert the floating point value into a fixed point value. 
+     * @param scale - The scale used to convert the floating point value into a fixed point value.
      */
     function quantizeData(
         bytes memory data,
@@ -181,7 +278,7 @@ contract DataAttestation is LoadInstances {
         if (mulmod(uint256(x), scale, decimals) * 2 >= decimals) {
             output += 1;
         }
-        quantized_data = neg ? -int256(output): int256(output);
+        quantized_data = neg ? -int256(output) : int256(output);
     }
     /**
      * @dev Make a static call to the account to fetch the data that EZKL reads from.
@@ -211,7 +308,9 @@ contract DataAttestation is LoadInstances {
      * @param x - The quantized data.
      * @return field_element - The field element.
      */
-    function toFieldElement(int256 x) internal pure returns (uint256 field_element) {
+    function toFieldElement(
+        int256 x
+    ) internal pure returns (uint256 field_element) {
         // The casting down to uint256 is safe because the order is about 2^254, and the value
         // of x ranges of -2^127 to 2^127, so x + int(ORDER) is always positive.
         return uint256(x + int(ORDER)) % ORDER;
@@ -251,12 +350,11 @@ contract DataAttestation is LoadInstances {
         }
     }
 
-
     function verifyWithDataAttestation(
         address verifier,
         bytes calldata encoded
     ) public view returns (bool) {
-        require(verifier.code.length > 0,"Address: call to non-contract");
+        require(verifier.code.length > 0, "Address: call to non-contract");
         attestData(getInstancesCalldata(encoded));
         // static call the verifier contract to verify the proof
         (bool success, bytes memory returndata) = verifier.staticcall(encoded);
diff --git a/contracts/LoadInstances.sol b/contracts/LoadInstances.sol
deleted file mode 100644
index 41fe8298a..000000000
--- a/contracts/LoadInstances.sol
+++ /dev/null
@@ -1,92 +0,0 @@
-// SPDX-License-Identifier: MIT
-pragma solidity ^0.8.20;
-contract LoadInstances {
-    /**
-     * @dev Parse the instances array from the Halo2Verifier encoded calldata. 
-     * @notice must pass encoded bytes from memory
-     * @param encoded - verifier calldata
-     */
-    function getInstancesMemory(
-        bytes memory encoded
-    ) internal pure returns (uint256[] memory instances) {
-        bytes4 funcSig;
-        uint256 instances_offset;
-        uint256 instances_length;
-        assembly {
-            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
-            funcSig := mload(add(encoded, 0x20))
-
-            // Fetch instances offset which is 4 + 32 + 32 bytes away from
-            // start of encoded for `verifyProof(bytes,uint256[])`,
-            // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
-
-            instances_offset := mload(
-                add(encoded, add(0x44, mul(0x20, eq(funcSig, 0xaf83a18d))))
-            )
-
-            instances_length := mload(add(add(encoded, 0x24), instances_offset))
-        }
-        instances = new uint256[](instances_length); // Allocate memory for the instances array.
-        assembly {
-            // Now instances points to the start of the array data
-            // (right after the length field).
-            for {
-                let i := 0x20
-            } lt(i, add(mul(instances_length, 0x20), 0x20)) {
-                i := add(i, 0x20)
-            } {
-                mstore(
-                    add(instances, i),
-                    mload(add(add(encoded, add(i, 0x24)), instances_offset))
-                )
-            }
-        }
-    }
-    /**
-     * @dev Parse the instances array from the Halo2Verifier encoded calldata. 
-     * @notice must pass encoded bytes from calldata
-     * @param encoded - verifier calldata
-     */
-    function getInstancesCalldata(
-        bytes calldata encoded
-    ) internal pure returns (uint256[] memory instances) {
-        bytes4 funcSig;
-        uint256 instances_offset;
-        uint256 instances_length;
-        assembly {
-            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
-            funcSig := calldataload(encoded.offset)
-
-            // Fetch instances offset which is 4 + 32 + 32 bytes away from
-            // start of encoded for `verifyProof(bytes,uint256[])`,
-            // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
-
-            instances_offset := calldataload(
-                add(
-                    encoded.offset,
-                    add(0x24, mul(0x20, eq(funcSig, 0xaf83a18d)))
-                )
-            )
-
-            instances_length := calldataload(add(add(encoded.offset, 0x04), instances_offset))
-        }
-        instances = new uint256[](instances_length); // Allocate memory for the instances array.
-        assembly{
-            // Now instances points to the start of the array data
-            // (right after the length field).
-
-            for {
-                let i := 0x20
-            } lt(i, add(mul(instances_length, 0x20), 0x20)) {
-                i := add(i, 0x20)
-            } {
-                mstore(
-                    add(instances, i),
-                    calldataload(
-                        add(add(encoded.offset, add(i, 0x04)), instances_offset)
-                    )
-                )
-            }
-        }
-    }
-}
\ No newline at end of file
diff --git a/contracts/QuantizeData.sol b/contracts/QuantizeData.sol
deleted file mode 100644
index babbdf8e9..000000000
--- a/contracts/QuantizeData.sol
+++ /dev/null
@@ -1,135 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0
-
-pragma solidity ^0.8.17;
-
-contract QuantizeData {
-    /**
-     * @notice EZKL P value
-     * @dev In order to prevent the verifier from accepting two version of the same instance, n and the quantity (n + P),  where n + P <= 2^256, we require that all instances are stricly less than P. a
-     * @dev The reason for this is that the assmebly code of the verifier performs all arithmetic operations modulo P and as a consequence can't distinguish between n and n + P.
-     */
-    uint256 constant ORDER =
-        uint256(
-            0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001
-        );
-
-    /**
-     * @notice Calculates floor(x * y / denominator) with full precision. Throws if result overflows a uint256 or denominator == 0
-     * @dev Original credit to Remco Bloemen under MIT license (https://xn--2-umb.com/21/muldiv)
-     * with further edits by Uniswap Labs also under MIT license.
-     */
-    function mulDiv(
-        uint256 x,
-        uint256 y,
-        uint256 denominator
-    ) internal pure returns (uint256 result) {
-        unchecked {
-            // 512-bit multiply [prod1 prod0] = x * y. Compute the product mod 2^256 and mod 2^256 - 1, then use
-            // use the Chinese Remainder Theorem to reconstruct the 512 bit result. The result is stored in two 256
-            // variables such that product = prod1 * 2^256 + prod0.
-            uint256 prod0; // Least significant 256 bits of the product
-            uint256 prod1; // Most significant 256 bits of the product
-            assembly {
-                let mm := mulmod(x, y, not(0))
-                prod0 := mul(x, y)
-                prod1 := sub(sub(mm, prod0), lt(mm, prod0))
-            }
-
-            // Handle non-overflow cases, 256 by 256 division.
-            if (prod1 == 0) {
-                // Solidity will revert if denominator == 0, unlike the div opcode on its own.
-                // The surrounding unchecked block does not change this fact.
-                // See https://docs.soliditylang.org/en/latest/control-structures.html#checked-or-unchecked-arithmetic.
-                return prod0 / denominator;
-            }
-
-            // Make sure the result is less than 2^256. Also prevents denominator == 0.
-            require(denominator > prod1, "Math: mulDiv overflow");
-
-            ///////////////////////////////////////////////
-            // 512 by 256 division.
-            ///////////////////////////////////////////////
-
-            // Make division exact by subtracting the remainder from [prod1 prod0].
-            uint256 remainder;
-            assembly {
-                // Compute remainder using mulmod.
-                remainder := mulmod(x, y, denominator)
-
-                // Subtract 256 bit number from 512 bit number.
-                prod1 := sub(prod1, gt(remainder, prod0))
-                prod0 := sub(prod0, remainder)
-            }
-
-            // Factor powers of two out of denominator and compute largest power of two divisor of denominator. Always >= 1.
-            // See https://cs.stackexchange.com/q/138556/92363.
-
-            // Does not overflow because the denominator cannot be zero at this stage in the function.
-            uint256 twos = denominator & (~denominator + 1);
-            assembly {
-                // Divide denominator by twos.
-                denominator := div(denominator, twos)
-
-                // Divide [prod1 prod0] by twos.
-                prod0 := div(prod0, twos)
-
-                // Flip twos such that it is 2^256 / twos. If twos is zero, then it becomes one.
-                twos := add(div(sub(0, twos), twos), 1)
-            }
-
-            // Shift in bits from prod1 into prod0.
-            prod0 |= prod1 * twos;
-
-            // Invert denominator mod 2^256. Now that denominator is an odd number, it has an inverse modulo 2^256 such
-            // that denominator * inv = 1 mod 2^256. Compute the inverse by starting with a seed that is correct for
-            // four bits. That is, denominator * inv = 1 mod 2^4.
-            uint256 inverse = (3 * denominator) ^ 2;
-
-            // Use the Newton-Raphson iteration to improve the precision. Thanks to Hensel's lifting lemma, this also works
-            // in modular arithmetic, doubling the correct bits in each step.
-            inverse *= 2 - denominator * inverse; // inverse mod 2^8
-            inverse *= 2 - denominator * inverse; // inverse mod 2^16
-            inverse *= 2 - denominator * inverse; // inverse mod 2^32
-            inverse *= 2 - denominator * inverse; // inverse mod 2^64
-            inverse *= 2 - denominator * inverse; // inverse mod 2^128
-            inverse *= 2 - denominator * inverse; // inverse mod 2^256
-
-            // Because the division is now exact we can divide by multiplying with the modular inverse of denominator.
-            // This will give us the correct result modulo 2^256. Since the preconditions guarantee that the outcome is
-            // less than 2^256, this is the final result. We don't need to compute the high bits of the result and prod1
-            // is no longer required.
-            result = prod0 * inverse;
-            return result;
-        }
-    }
-
-    function quantize_data(
-        bytes[] memory data,
-        uint256[] memory decimals,
-        uint256[] memory scales
-    ) external pure returns (int256[] memory quantized_data) {
-        quantized_data = new int256[](data.length);
-        for (uint i; i < data.length; i++) {
-            int x = abi.decode(data[i], (int256));
-            bool neg = x < 0;
-            if (neg) x = -x;
-            uint denom = 10 ** decimals[i];
-            uint scale = 1 << scales[i];
-            uint output = mulDiv(uint256(x), scale, denom);
-            if (mulmod(uint256(x), scale, denom) * 2 >= denom) {
-                output += 1;
-            }
-
-            quantized_data[i] = neg ? -int256(output) : int256(output);
-        }
-    }
-
-    function to_field_element(
-        int64[] memory quantized_data
-    ) public pure returns (uint256[] memory output) {
-        output = new uint256[](quantized_data.length);
-        for (uint i; i < quantized_data.length; i++) {
-            output[i] = uint256(quantized_data[i] + int(ORDER)) % ORDER;
-        }
-    }
-}
diff --git a/contracts/TestReads.sol b/contracts/TestReads.sol
deleted file mode 100644
index 2a263f028..000000000
--- a/contracts/TestReads.sol
+++ /dev/null
@@ -1,12 +0,0 @@
-// SPDX-License-Identifier: UNLICENSED
-pragma solidity ^0.8.17;
-
-contract TestReads {
-    int[] public arr;
-
-    constructor(int256[] memory _numbers) {
-        for (uint256 i = 0; i < _numbers.length; i++) {
-            arr.push(_numbers[i]);
-        }
-    }
-}
diff --git a/examples/notebooks/data_attest.ipynb b/examples/notebooks/data_attest.ipynb
index 7803272b7..6cc3759b6 100644
--- a/examples/notebooks/data_attest.ipynb
+++ b/examples/notebooks/data_attest.ipynb
@@ -251,7 +251,7 @@
                 "with open(cal_path, \"w\") as f:\n",
                 "    json.dump(cal_data, f)\n",
                 "\n",
-                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -307,7 +307,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "ezkl.setup_test_evm_witness(\n",
+                "await ezkl.setup_test_evm_witness(\n",
                 "    data_path,\n",
                 "    compiled_model_path,\n",
                 "    # we write the call data to the same file as the input data\n",
@@ -333,7 +333,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "res = ezkl.get_srs( settings_path)\n"
+                "res = await ezkl.get_srs( settings_path)\n"
             ]
         },
         {
@@ -354,7 +354,7 @@
                 "\n",
                 "witness_path = \"witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
             ]
         },
         {
@@ -462,7 +462,7 @@
                 "abi_path = 'test.abi'\n",
                 "sol_code_path = 'test.sol'\n",
                 "\n",
-                "res = ezkl.create_evm_verifier(\n",
+                "res = await ezkl.create_evm_verifier(\n",
                 "        vk_path,\n",
                 "        \n",
                 "        settings_path,\n",
@@ -482,7 +482,7 @@
                 "\n",
                 "addr_path_verifier = \"addr_verifier.txt\"\n",
                 "\n",
-                "res = ezkl.deploy_evm(\n",
+                "res = await ezkl.deploy_evm(\n",
                 "    addr_path_verifier,\n",
                 "    sol_code_path,\n",
                 "    'http://127.0.0.1:3030'\n",
@@ -510,7 +510,7 @@
                 "sol_code_path = 'test.sol'\n",
                 "input_path = 'input.json'\n",
                 "\n",
-                "res = ezkl.create_evm_data_attestation(\n",
+                "res = await ezkl.create_evm_data_attestation(\n",
                 "        input_path,\n",
                 "        settings_path,\n",
                 "        sol_code_path,\n",
@@ -535,7 +535,7 @@
             "source": [
                 "addr_path_da = \"addr_da.txt\"\n",
                 "\n",
-                "res = ezkl.deploy_da_evm(\n",
+                "res = await ezkl.deploy_da_evm(\n",
                 "        addr_path_da,\n",
                 "        input_path,\n",
                 "        settings_path,\n",
@@ -567,7 +567,7 @@
                 "with open(addr_path_da, 'r') as f:\n",
                 "    addr_da = f.read()\n",
                 "\n",
-                "res = ezkl.verify_evm(\n",
+                "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
                 "    proof_path,\n",
                 "    RPC_URL,\n",
@@ -592,7 +592,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         },
         "orig_nbformat": 4
     },
diff --git a/examples/notebooks/data_attest_hashed.ipynb b/examples/notebooks/data_attest_hashed.ipynb
index 5744174d6..bd6979dd4 100644
--- a/examples/notebooks/data_attest_hashed.ipynb
+++ b/examples/notebooks/data_attest_hashed.ipynb
@@ -249,7 +249,7 @@
                 "with open(cal_path, \"w\") as f:\n",
                 "    json.dump(cal_data, f)\n",
                 "\n",
-                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -278,7 +278,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "res = ezkl.get_srs( settings_path)\n"
+                "res = await ezkl.get_srs( settings_path)\n"
             ]
         },
         {
@@ -299,7 +299,7 @@
                 "\n",
                 "witness_path = \"witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "\n"
             ]
         },
@@ -518,7 +518,7 @@
                 "abi_path = 'test.abi'\n",
                 "sol_code_path = 'test.sol'\n",
                 "\n",
-                "res = ezkl.create_evm_verifier(\n",
+                "res = await ezkl.create_evm_verifier(\n",
                 "        vk_path,\n",
                 "        \n",
                 "        settings_path,\n",
@@ -538,7 +538,7 @@
                 "\n",
                 "addr_path_verifier = \"addr_verifier.txt\"\n",
                 "\n",
-                "res = ezkl.deploy_evm(\n",
+                "res = await ezkl.deploy_evm(\n",
                 "    addr_path_verifier,\n",
                 "    sol_code_path,\n",
                 "    'http://127.0.0.1:3030'\n",
@@ -566,7 +566,7 @@
                 "sol_code_path = 'test.sol'\n",
                 "input_path = 'input.json'\n",
                 "\n",
-                "res = ezkl.create_evm_data_attestation(\n",
+                "res = await ezkl.create_evm_data_attestation(\n",
                 "        input_path,\n",
                 "        settings_path,\n",
                 "        sol_code_path,\n",
@@ -591,7 +591,7 @@
             "source": [
                 "addr_path_da = \"addr_da.txt\"\n",
                 "\n",
-                "res = ezkl.deploy_da_evm(\n",
+                "res = await ezkl.deploy_da_evm(\n",
                 "        addr_path_da,\n",
                 "        input_path,\n",
                 "        settings_path,\n",
@@ -623,7 +623,7 @@
                 "with open(addr_path_da, 'r') as f:\n",
                 "    addr_da = f.read()\n",
                 "\n",
-                "res = ezkl.verify_evm(\n",
+                "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
                 "    proof_path,\n",
                 "    RPC_URL,\n",
@@ -654,4 +654,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/decision_tree.ipynb b/examples/notebooks/decision_tree.ipynb
index dbe52a358..b8381cfda 100644
--- a/examples/notebooks/decision_tree.ipynb
+++ b/examples/notebooks/decision_tree.ipynb
@@ -150,7 +150,7 @@
                 "res = ezkl.gen_settings(model_path, settings_path)\n",
                 "assert res == True\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True"
             ]
         },
@@ -170,7 +170,7 @@
                 "with open(cal_path, \"w\") as f:\n",
                 "    json.dump(cal_data, f)\n",
                 "\n",
-                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -192,7 +192,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -204,7 +204,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -303,4 +303,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/ezkl_demo.ipynb b/examples/notebooks/ezkl_demo.ipynb
index 4cb7b6174..65e24b926 100644
--- a/examples/notebooks/ezkl_demo.ipynb
+++ b/examples/notebooks/ezkl_demo.ipynb
@@ -352,14 +352,8 @@
         "# Specify all the files we need\n",
         "\n",
         "model_path = os.path.join('network.onnx')\n",
-        "compiled_model_path = os.path.join('network.ezkl')\n",
-        "pk_path = os.path.join('test.pk')\n",
-        "vk_path = os.path.join('test.vk')\n",
-        "settings_path = os.path.join('settings.json')\n",
-        "\n",
-        "witness_path = os.path.join('witness.json')\n",
         "data_path = os.path.join('input.json')\n",
-        "cal_data_path = os.path.join('cal_data.json')"
+        "cal_data_path = os.path.join('calibration.json')"
       ]
     },
     {
@@ -424,7 +418,7 @@
       "source": [
         "!RUST_LOG=trace\n",
         "# TODO: Dictionary outputs\n",
-        "res = ezkl.gen_settings(model_path, settings_path)\n",
+        "res = ezkl.gen_settings()\n",
         "assert res == True\n",
         "\n"
       ]
@@ -443,7 +437,7 @@
         "\n",
         "# Optimize for resources, we cap logrows at 12 to reduce setup and proving time, at the expense of accuracy\n",
         "# You may want to increase the max logrows if accuracy is a concern\n",
-        "res = ezkl.calibrate_settings(cal_data_path, model_path, settings_path, \"resources\", max_logrows = 12, scales = [2])"
+        "res = await ezkl.calibrate_settings(target = \"resources\", max_logrows = 12, scales = [2])"
       ]
     },
     {
@@ -463,7 +457,7 @@
       },
       "outputs": [],
       "source": [
-        "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
+        "res = ezkl.compile_circuit()\n",
         "assert res == True"
       ]
     },
@@ -484,7 +478,7 @@
       },
       "outputs": [],
       "source": [
-        "res = ezkl.get_srs( settings_path)"
+        "res = await ezkl.get_srs()"
       ]
     },
     {
@@ -504,17 +498,10 @@
       },
       "outputs": [],
       "source": [
-        "res = ezkl.setup(\n",
-        "        compiled_model_path,\n",
-        "        vk_path,\n",
-        "        pk_path,\n",
-        "    )\n",
+        "res = ezkl.setup()\n",
         "\n",
         "\n",
-        "assert res == True\n",
-        "assert os.path.isfile(vk_path)\n",
-        "assert os.path.isfile(pk_path)\n",
-        "assert os.path.isfile(settings_path)"
+        "assert res == True"
       ]
     },
     {
@@ -539,7 +526,7 @@
         "# now generate the witness file\n",
         "witness_path = os.path.join('witness.json')\n",
         "\n",
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = await ezkl.gen_witness()\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
@@ -559,13 +546,7 @@
         "\n",
         "proof_path = os.path.join('proof.json')\n",
         "\n",
-        "proof = ezkl.prove(\n",
-        "        witness_path,\n",
-        "        compiled_model_path,\n",
-        "        pk_path,\n",
-        "        proof_path,\n",
-        "        \"single\",\n",
-        "    )\n",
+        "proof = ezkl.prove(proof_type=\"single\", proof_path=proof_path)\n",
         "\n",
         "print(proof)\n",
         "assert os.path.isfile(proof_path)"
@@ -585,11 +566,7 @@
       "source": [
         "# verify our proof\n",
         "\n",
-        "res = ezkl.verify(\n",
-        "        proof_path,\n",
-        "        settings_path,\n",
-        "        vk_path,\n",
-        "    )\n",
+        "res = ezkl.verify()\n",
         "\n",
         "assert res == True\n",
         "print(\"verified\")"
@@ -664,12 +641,9 @@
         "sol_code_path = os.path.join('Verifier.sol')\n",
         "abi_path = os.path.join('Verifier.abi')\n",
         "\n",
-        "res = ezkl.create_evm_verifier(\n",
-        "        vk_path,\n",
-        "        \n",
-        "        settings_path,\n",
-        "        sol_code_path,\n",
-        "        abi_path\n",
+        "res = await ezkl.create_evm_verifier(\n",
+        "        sol_code_path=sol_code_path,\n",
+        "        abi_path=abi_path, \n",
         "    )\n",
         "\n",
         "assert res == True\n",
@@ -757,7 +731,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.15"
+      "version": "3.12.2"
     }
   },
   "nbformat": 4,
diff --git a/examples/notebooks/gcn.ipynb b/examples/notebooks/gcn.ipynb
index 410342194..e6c66bcb7 100644
--- a/examples/notebooks/gcn.ipynb
+++ b/examples/notebooks/gcn.ipynb
@@ -467,7 +467,7 @@
       "outputs": [],
       "source": [
         "\n",
-        "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+        "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
         "assert res == True"
       ]
     },
@@ -494,7 +494,7 @@
       "outputs": [],
       "source": [
         "# srs path\n",
-        "res = ezkl.get_srs( settings_path)"
+        "res = await ezkl.get_srs( settings_path)"
       ]
     },
     {
@@ -508,7 +508,7 @@
       "source": [
         "# now generate the witness file\n",
         "\n",
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
@@ -625,4 +625,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/generalized_inverse.ipynb b/examples/notebooks/generalized_inverse.ipynb
index a71555ba4..962cf606a 100644
--- a/examples/notebooks/generalized_inverse.ipynb
+++ b/examples/notebooks/generalized_inverse.ipynb
@@ -195,7 +195,7 @@
         "json.dump(data, open(cal_path, 'w'))\n",
         "\n",
         "\n",
-        "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+        "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
         "assert res == True\n"
       ]
     },
@@ -222,7 +222,7 @@
       "outputs": [],
       "source": [
         "# srs path\n",
-        "res = ezkl.get_srs( settings_path)"
+        "res = await ezkl.get_srs( settings_path)"
       ]
     },
     {
@@ -236,7 +236,7 @@
       "source": [
         "# now generate the witness file\n",
         "\n",
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
diff --git a/examples/notebooks/gradient_boosted_trees.ipynb b/examples/notebooks/gradient_boosted_trees.ipynb
index a608f9974..c319c2a14 100644
--- a/examples/notebooks/gradient_boosted_trees.ipynb
+++ b/examples/notebooks/gradient_boosted_trees.ipynb
@@ -179,7 +179,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -202,7 +202,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -214,7 +214,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -313,4 +313,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/hashed_vis.ipynb b/examples/notebooks/hashed_vis.ipynb
index e41cd0eb0..88922b820 100644
--- a/examples/notebooks/hashed_vis.ipynb
+++ b/examples/notebooks/hashed_vis.ipynb
@@ -241,7 +241,7 @@
                 "with open(cal_path, \"w\") as f:\n",
                 "    json.dump(cal_data, f)\n",
                 "\n",
-                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -270,7 +270,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "res = ezkl.get_srs( settings_path)\n"
+                "res = await ezkl.get_srs( settings_path)\n"
             ]
         },
         {
@@ -291,7 +291,7 @@
                 "\n",
                 "witness_path = \"witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
             ]
         },
         {
@@ -420,7 +420,7 @@
                 "abi_path = 'test.abi'\n",
                 "sol_code_path = 'test.sol'\n",
                 "\n",
-                "res = ezkl.create_evm_verifier(\n",
+                "res = await ezkl.create_evm_verifier(\n",
                 "        vk_path,\n",
                 "        \n",
                 "        settings_path,\n",
@@ -451,7 +451,7 @@
                 "\n",
                 "address_path = os.path.join(\"address.json\")\n",
                 "\n",
-                "res = ezkl.deploy_evm(\n",
+                "res = await ezkl.deploy_evm(\n",
                 "    address_path,\n",
                 "    sol_code_path,\n",
                 "    'http://127.0.0.1:3030'\n",
@@ -472,7 +472,7 @@
                 "# make sure anvil is running locally\n",
                 "# $ anvil -p 3030\n",
                 "\n",
-                "res = ezkl.verify_evm(\n",
+                "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
                 "    proof_path,\n",
                 "    \"http://127.0.0.1:3030\"\n",
diff --git a/examples/notebooks/keras_simple_demo.ipynb b/examples/notebooks/keras_simple_demo.ipynb
index 356a9f4f6..bd0fa98c5 100644
--- a/examples/notebooks/keras_simple_demo.ipynb
+++ b/examples/notebooks/keras_simple_demo.ipynb
@@ -152,7 +152,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -175,7 +175,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs(settings_path = settings_path)"
+                "res = await ezkl.get_srs(settings_path = settings_path)"
             ]
         },
         {
@@ -188,7 +188,7 @@
                 "# now generate the witness file \n",
                 "witness_path = \"witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -284,4 +284,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/kmeans.ipynb b/examples/notebooks/kmeans.ipynb
index a2d96a6be..fd2ba00e9 100644
--- a/examples/notebooks/kmeans.ipynb
+++ b/examples/notebooks/kmeans.ipynb
@@ -155,7 +155,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -178,7 +178,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -190,7 +190,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -289,4 +289,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/kzg_vis.ipynb b/examples/notebooks/kzg_vis.ipynb
index 7d298c11a..568ad89b6 100644
--- a/examples/notebooks/kzg_vis.ipynb
+++ b/examples/notebooks/kzg_vis.ipynb
@@ -233,7 +233,7 @@
                 "with open(cal_path, \"w\") as f:\n",
                 "    json.dump(cal_data, f)\n",
                 "\n",
-                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -262,7 +262,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "res = ezkl.get_srs( settings_path)\n"
+                "res = await ezkl.get_srs( settings_path)\n"
             ]
         },
         {
@@ -315,7 +315,7 @@
                 "\n",
                 "witness_path = \"witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n"
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n"
             ]
         },
         {
@@ -429,7 +429,7 @@
                 "abi_path = 'test.abi'\n",
                 "sol_code_path = 'test.sol'\n",
                 "\n",
-                "res = ezkl.create_evm_verifier(\n",
+                "res = await ezkl.create_evm_verifier(\n",
                 "        vk_path,\n",
                 "        \n",
                 "        settings_path,\n",
@@ -460,7 +460,7 @@
                 "\n",
                 "address_path = os.path.join(\"address.json\")\n",
                 "\n",
-                "res = ezkl.deploy_evm(\n",
+                "res = await ezkl.deploy_evm(\n",
                 "    address_path,\n",
                 "    sol_code_path,\n",
                 "    'http://127.0.0.1:3030'\n",
@@ -481,7 +481,7 @@
                 "# make sure anvil is running locally\n",
                 "# $ anvil -p 3030\n",
                 "\n",
-                "res = ezkl.verify_evm(\n",
+                "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
                 "    proof_path,\n",
                 "    \"http://127.0.0.1:3030\"\n",
diff --git a/examples/notebooks/lightgbm.ipynb b/examples/notebooks/lightgbm.ipynb
index 3bbbf9197..eaf02ba44 100644
--- a/examples/notebooks/lightgbm.ipynb
+++ b/examples/notebooks/lightgbm.ipynb
@@ -193,7 +193,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -216,7 +216,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -228,7 +228,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -347,4 +347,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/linear_regression.ipynb b/examples/notebooks/linear_regression.ipynb
index b5eeeb25b..570b3272f 100644
--- a/examples/notebooks/linear_regression.ipynb
+++ b/examples/notebooks/linear_regression.ipynb
@@ -142,7 +142,7 @@
                 "# Serialize data into file:\n",
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -165,7 +165,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -177,7 +177,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -276,4 +276,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/little_transformer.ipynb b/examples/notebooks/little_transformer.ipynb
index 9bd3deb78..fac9cf7fa 100644
--- a/examples/notebooks/little_transformer.ipynb
+++ b/examples/notebooks/little_transformer.ipynb
@@ -347,7 +347,7 @@
                 "# Serialize data into file:\n",
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -370,7 +370,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -383,7 +383,7 @@
                 "# now generate the witness file \n",
                 "witness_path = \"gan_witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -490,4 +490,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/logistic_regression.ipynb b/examples/notebooks/logistic_regression.ipynb
index e53af934d..0bdd16820 100644
--- a/examples/notebooks/logistic_regression.ipynb
+++ b/examples/notebooks/logistic_regression.ipynb
@@ -142,7 +142,7 @@
                 "# Serialize data into file:\n",
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -165,7 +165,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -177,7 +177,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
diff --git a/examples/notebooks/lstm.ipynb b/examples/notebooks/lstm.ipynb
index 272d69a60..b1e07e57c 100644
--- a/examples/notebooks/lstm.ipynb
+++ b/examples/notebooks/lstm.ipynb
@@ -139,7 +139,7 @@
                 "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
                 "assert res == True\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n"
             ]
         },
@@ -180,7 +180,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -193,7 +193,7 @@
                 "# now generate the witness file \n",
                 "witness_path = \"lstmwitness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -300,4 +300,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/mean_postgres.ipynb b/examples/notebooks/mean_postgres.ipynb
index acf0b830a..cf96590b8 100644
--- a/examples/notebooks/mean_postgres.ipynb
+++ b/examples/notebooks/mean_postgres.ipynb
@@ -91,11 +91,14 @@
         "os.system(\"echo shovel is now installed. starting:\")\n",
         "\n",
         "command = [\"./shovel\", \"-config\", \"config.json\"]\n",
-        "subprocess.Popen(command)\n",
+        "proc = subprocess.Popen(command)\n",
         "\n",
         "os.system(\"echo shovel started.\")\n",
         "\n",
-        "time.sleep(5)\n",
+        "time.sleep(10)\n",
+        "\n",
+        "# after we've fetched some data -- kill the process\n",
+        "proc.terminate()\n",
         "\n"
       ]
     },
@@ -310,7 +313,7 @@
         "\n",
         "assert res == True\n",
         "\n",
-        "res = ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
+        "res = await ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
         "\n",
         "assert res == True"
       ]
@@ -387,7 +390,7 @@
         "witness_path = \"witness.json\"\n",
         "\n",
         "# generate the witness\n",
-        "res = ezkl.gen_witness(\n",
+        "res = await ezkl.gen_witness(\n",
         "        input_filename,\n",
         "        compiled_filename,\n",
         "        witness_path\n",
@@ -425,16 +428,6 @@
         "assert os.path.isfile(proof_path)\n",
         "\n"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# kill all shovel process \n",
-        "os.system(\"pkill -f shovel\")"
-      ]
     }
   ],
   "metadata": {
diff --git a/examples/notebooks/mnist_classifier.ipynb b/examples/notebooks/mnist_classifier.ipynb
index e93ca3769..3ee2482ab 100644
--- a/examples/notebooks/mnist_classifier.ipynb
+++ b/examples/notebooks/mnist_classifier.ipynb
@@ -323,7 +323,7 @@
         "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
         "assert res == True\n",
         "\n",
-        "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[2,7])\n",
+        "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[2,7])\n",
         "assert res == True"
       ]
     },
@@ -348,7 +348,7 @@
       "outputs": [],
       "source": [
         "# srs path\n",
-        "res = ezkl.get_srs(settings_path)"
+        "res = await ezkl.get_srs(settings_path)"
       ]
     },
     {
@@ -362,7 +362,7 @@
         "# now generate the witness file\n",
         "witness_path = \"witness.json\"\n",
         "\n",
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
@@ -469,7 +469,7 @@
         "abi_path = 'test.abi'\n",
         "sol_code_path = 'test_1.sol'\n",
         "\n",
-        "res = ezkl.create_evm_verifier(\n",
+        "res = await ezkl.create_evm_verifier(\n",
         "        vk_path,\n",
         "        settings_path,\n",
         "        sol_code_path,\n",
@@ -502,7 +502,7 @@
         "\n",
         "address_path = os.path.join(\"address.json\")\n",
         "\n",
-        "res = ezkl.deploy_evm(\n",
+        "res = await ezkl.deploy_evm(\n",
         "    address_path,\n",
         "    sol_code_path,\n",
         "    'http://127.0.0.1:3030'\n",
@@ -525,7 +525,7 @@
         "# make sure anvil is running locally\n",
         "# $ anvil -p 3030\n",
         "\n",
-        "res = ezkl.verify_evm(\n",
+        "res = await ezkl.verify_evm(\n",
         "    addr,\n",
         "    proof_path,\n",
         "    \"http://127.0.0.1:3030\"\n",
@@ -558,4 +558,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/mnist_gan.ipynb b/examples/notebooks/mnist_gan.ipynb
index 69deaa76f..0d413f411 100644
--- a/examples/notebooks/mnist_gan.ipynb
+++ b/examples/notebooks/mnist_gan.ipynb
@@ -289,7 +289,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[0,6])"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[0,6])"
             ]
         },
         {
@@ -309,7 +309,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -321,7 +321,7 @@
                 "# now generate the witness file \n",
                 "witness_path = \"gan_witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
diff --git a/examples/notebooks/mnist_gan_proof_splitting.ipynb b/examples/notebooks/mnist_gan_proof_splitting.ipynb
index 5f150b45b..53f892d75 100644
--- a/examples/notebooks/mnist_gan_proof_splitting.ipynb
+++ b/examples/notebooks/mnist_gan_proof_splitting.ipynb
@@ -395,13 +395,13 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "   0: dloss:  0.8063   gloss:  0.6461\n",
-                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n"
+                        "   0: dloss:  0.8383   gloss:  2.3304\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n"
                     ]
                 },
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 1600x1600 with 1 Axes>"
                         ]
@@ -453,7 +453,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "<matplotlib.legend.Legend at 0x34f625160>"
+                            "<matplotlib.legend.Legend at 0x3439b0800>"
                         ]
                     },
                     "execution_count": 5,
@@ -488,22 +488,22 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
-                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
-                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
                         "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
                     ]
                 },
                 {
                     "data": {
                         "text/plain": [
-                            "<matplotlib.image.AxesImage at 0x34f32bd70>"
+                            "<matplotlib.image.AxesImage at 0x34c4293a0>"
                         ]
                     },
                     "execution_count": 6,
@@ -512,7 +512,7 @@
                 },
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -879,7 +879,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "True"
+                            "<Future pending cb=[<builtins.PyDoneCallback object at 0x34fc48390>()]>"
                         ]
                     },
                     "execution_count": 8,
@@ -916,29 +916,11 @@
             "execution_count": 9,
             "metadata": {},
             "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "\n",
-                        " <------------- Numerical Fidelity Report (input_scale: 0, param_scale: 0, scale_input_multiplier: 10) ------------->\n",
-                        "\n",
-                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
-                        "| mean_error     | median_error | max_error   | min_error    | mean_abs_error | median_abs_error | max_abs_error | min_abs_error   | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
-                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
-                        "| -0.00045216593 | 0.0071961936 | 0.059581105 | -0.051913798 | 0.011681631    | 0.0071961936     | 0.059581105   | 0.0000062934123 | 0.0002161761       | 1                  | 1                      |\n",
-                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
-                        "\n",
-                        "\n"
-                    ]
-                },
                 {
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Setting up split model 0\n",
-                        "Setting up split model 1\n"
+                        "Setting up split model 0\n"
                     ]
                 },
                 {
@@ -949,11 +931,11 @@
                         "\n",
                         " <------------- Numerical Fidelity Report (input_scale: 0, param_scale: 0, scale_input_multiplier: 10) ------------->\n",
                         "\n",
-                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
-                        "| mean_error     | median_error | max_error   | min_error    | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
-                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
-                        "| -0.00008474619 | -0.002256453 | 0.003519658 | -0.003081262 | 0.0018818051   | 0.002256453      | 0.003519658   | 0.00017167516 | 0.000003900568     | 1                  | 1                      |\n",
-                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "+---------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
+                        "| mean_error    | median_error | max_error   | min_error    | mean_abs_error | median_abs_error | max_abs_error | min_abs_error   | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
+                        "+---------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
+                        "| 0.00004752255 | -0.012598574 | 0.056053344 | -0.048946142 | 0.012820076    | 0.012598574      | 0.056053344   | 0.0000010610092 | 0.0002609586       | 1                  | 1                      |\n",
+                        "+---------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
                         "\n",
                         "\n"
                     ]
@@ -962,31 +944,14 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Setting up split model 2\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "\n",
-                        " <------------- Numerical Fidelity Report (input_scale: 0, param_scale: 0, scale_input_multiplier: 10) ------------->\n",
-                        "\n",
-                        "+-------------+--------------+-------------+-------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
-                        "| mean_error  | median_error | max_error   | min_error   | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
-                        "+-------------+--------------+-------------+-------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
-                        "| -0.49951223 | -0.49951398  | -0.49951398 | -0.49951398 | 0.49951223     | 0.49951398       | 0.49951398    | 0.49951398    | 0.24951272         | -0.9980509         | 0.9980509              |\n",
-                        "+-------------+--------------+-------------+-------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
-                        "\n",
-                        "\n"
+                        "Setting up split model 1\n"
                     ]
                 }
             ],
             "source": [
                 "# iterate over each submodel gen-settings, compile circuit and setup zkSNARK\n",
                 "\n",
-                "def setup(i):\n",
+                "async def setup(i):\n",
                 "    print(\"Setting up split model \"+str(i))\n",
                 "    # file names\n",
                 "    model_path = os.path.join('network_split_'+str(i)+'.onnx')\n",
@@ -1008,7 +973,7 @@
                 "\n",
                 "    # generate settings for the current model\n",
                 "    res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "    res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
+                "    res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
                 "    assert res == True\n",
                 "\n",
                 "    # load settings and print them to the console\n",
@@ -1028,11 +993,11 @@
                 "    assert res == True\n",
                 "    assert os.path.isfile(vk_path)\n",
                 "    assert os.path.isfile(pk_path)\n",
-                "    res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
+                "    res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
                 "    run_args.input_scale = settings[\"model_output_scales\"][0]\n",
                 "\n",
                 "for i in range(3):\n",
-                "    setup(i)\n"
+                "    await setup(i)\n"
             ]
         },
         {
diff --git a/examples/notebooks/mnist_vae.ipynb b/examples/notebooks/mnist_vae.ipynb
index 69ac6e5e9..1d8fd1bcb 100644
--- a/examples/notebooks/mnist_vae.ipynb
+++ b/examples/notebooks/mnist_vae.ipynb
@@ -215,7 +215,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -235,7 +235,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -247,7 +247,7 @@
                 "# now generate the witness file\n",
                 "witness_path = \"ae_witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -451,7 +451,7 @@
                 "res = ezkl.gen_settings(model_path, settings_path)\n",
                 "assert res == True\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True\n",
                 "print(\"verified\")"
             ]
@@ -473,7 +473,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -485,7 +485,7 @@
                 "# now generate the witness file \n",
                 "witness_path = \"vae_witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
diff --git a/examples/notebooks/nbeats_timeseries_forecasting.ipynb b/examples/notebooks/nbeats_timeseries_forecasting.ipynb
index b9a1f702d..97fa0f32d 100644
--- a/examples/notebooks/nbeats_timeseries_forecasting.ipynb
+++ b/examples/notebooks/nbeats_timeseries_forecasting.ipynb
@@ -845,7 +845,7 @@
         "res = ezkl.gen_settings(model_path, settings_path)\n",
         "assert res == True\n",
         "\n",
-        "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", max_logrows = 20, scales = [3])\n",
+        "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", max_logrows = 20, scales = [3])\n",
         "assert res == True"
       ]
     },
@@ -870,7 +870,7 @@
       "outputs": [],
       "source": [
         "# srs path\n",
-        "res = ezkl.get_srs( settings_path)"
+        "res = await ezkl.get_srs( settings_path)"
       ]
     },
     {
@@ -881,7 +881,7 @@
       },
       "outputs": [],
       "source": [
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
@@ -993,4 +993,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/proof_splitting.ipynb b/examples/notebooks/proof_splitting.ipynb
index 99075fbf7..a02fd8f99 100644
--- a/examples/notebooks/proof_splitting.ipynb
+++ b/examples/notebooks/proof_splitting.ipynb
@@ -261,7 +261,7 @@
             "source": [
                 "# iterate over each submodel gen-settings, compile circuit and setup zkSNARK\n",
                 "\n",
-                "def setup(i):\n",
+                "async def setup(i):\n",
                 "    # file names\n",
                 "    model_path = os.path.join('network_split_'+str(i)+'.onnx')\n",
                 "    settings_path = os.path.join('settings_split_'+str(i)+'.json')\n",
@@ -282,7 +282,7 @@
                 "\n",
                 "    # generate settings for the current model\n",
                 "    res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "    res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
+                "    res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
                 "    assert res == True\n",
                 "\n",
                 "    # load settings and print them to the console\n",
@@ -303,11 +303,11 @@
                 "    assert os.path.isfile(vk_path)\n",
                 "    assert os.path.isfile(pk_path)\n",
                 "\n",
-                "    res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
+                "    res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
                 "    run_args.input_scale = settings[\"model_output_scales\"][0]\n",
                 "\n",
                 "for i in range(2):\n",
-                "    setup(i)\n"
+                "    await setup(i)\n"
             ]
         },
         {
@@ -414,7 +414,7 @@
             "outputs": [],
             "source": [
                 "for i in range(2):\n",
-                "    setup(i)"
+                "    await setup(i)"
             ]
         },
         {
@@ -466,7 +466,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         },
         "orig_nbformat": 4
     },
diff --git a/examples/notebooks/random_forest.ipynb b/examples/notebooks/random_forest.ipynb
index 316f5a2a5..1d3182901 100644
--- a/examples/notebooks/random_forest.ipynb
+++ b/examples/notebooks/random_forest.ipynb
@@ -174,7 +174,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -196,7 +196,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -208,7 +208,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
diff --git a/examples/notebooks/set_membership.ipynb b/examples/notebooks/set_membership.ipynb
index 9800c2fbd..8b4022a18 100644
--- a/examples/notebooks/set_membership.ipynb
+++ b/examples/notebooks/set_membership.ipynb
@@ -215,7 +215,7 @@
       "outputs": [],
       "source": [
         "# srs path\n",
-        "res = ezkl.get_srs( settings_path)"
+        "res = await ezkl.get_srs( settings_path)"
       ]
     },
     {
@@ -229,7 +229,7 @@
       "source": [
         "# now generate the witness file\n",
         "\n",
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
@@ -265,7 +265,7 @@
         "    # Serialize data into file:\n",
         "json.dump( data, open(data_path_faulty, 'w' ))\n",
         "\n",
-        "res = ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path_faulty)\n",
+        "res = await ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path_faulty)\n",
         "assert os.path.isfile(witness_path_faulty)"
       ]
     },
@@ -310,7 +310,7 @@
         "# Serialize data into file:\n",
         "json.dump( data, open(data_path_truthy, 'w' ))\n",
         "\n",
-        "res = ezkl.gen_witness(data_path_truthy, compiled_model_path, witness_path_truthy)\n",
+        "res = await ezkl.gen_witness(data_path_truthy, compiled_model_path, witness_path_truthy)\n",
         "assert os.path.isfile(witness_path_truthy)"
       ]
     },
@@ -519,4 +519,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/simple_demo_aggregated_proofs.ipynb b/examples/notebooks/simple_demo_aggregated_proofs.ipynb
index 75ac50ffd..014723d0a 100644
--- a/examples/notebooks/simple_demo_aggregated_proofs.ipynb
+++ b/examples/notebooks/simple_demo_aggregated_proofs.ipynb
@@ -193,7 +193,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -205,7 +205,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -290,7 +290,7 @@
             "source": [
                 "# Generate a larger SRS. This is needed for the aggregated proof\n",
                 "\n",
-                "res = ezkl.get_srs(settings_path=None, logrows=21, commitment=ezkl.PyCommitments.KZG)"
+                "res = await ezkl.get_srs(settings_path=None, logrows=21, commitment=ezkl.PyCommitments.KZG)"
             ]
         },
         {
@@ -374,7 +374,7 @@
                 "sol_code_path = os.path.join(\"Verifier.sol\")\n",
                 "abi_path = os.path.join(\"Verifier_ABI.json\")\n",
                 "\n",
-                "res = ezkl.create_evm_verifier_aggr(\n",
+                "res = await ezkl.create_evm_verifier_aggr(\n",
                 "    [settings_path],\n",
                 "    aggregate_vk_path,\n",
                 "    sol_code_path,\n",
@@ -404,4 +404,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/simple_demo_all_public.ipynb b/examples/notebooks/simple_demo_all_public.ipynb
index c17239db4..c863eccd9 100644
--- a/examples/notebooks/simple_demo_all_public.ipynb
+++ b/examples/notebooks/simple_demo_all_public.ipynb
@@ -170,7 +170,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -192,7 +192,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -204,7 +204,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
diff --git a/examples/notebooks/simple_demo_public_input_output.ipynb b/examples/notebooks/simple_demo_public_input_output.ipynb
index 7a1e3a277..06255357c 100644
--- a/examples/notebooks/simple_demo_public_input_output.ipynb
+++ b/examples/notebooks/simple_demo_public_input_output.ipynb
@@ -191,7 +191,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -203,7 +203,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -302,4 +302,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/simple_demo_public_network_output.ipynb b/examples/notebooks/simple_demo_public_network_output.ipynb
index 488c083da..6c8242373 100644
--- a/examples/notebooks/simple_demo_public_network_output.ipynb
+++ b/examples/notebooks/simple_demo_public_network_output.ipynb
@@ -192,7 +192,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -204,7 +204,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
diff --git a/examples/notebooks/sklearn_mlp.ipynb b/examples/notebooks/sklearn_mlp.ipynb
index dd7949253..95fb8a7c6 100644
--- a/examples/notebooks/sklearn_mlp.ipynb
+++ b/examples/notebooks/sklearn_mlp.ipynb
@@ -149,7 +149,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -171,7 +171,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -183,7 +183,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -282,4 +282,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/solvency.ipynb b/examples/notebooks/solvency.ipynb
index e1e2755db..4249249fb 100644
--- a/examples/notebooks/solvency.ipynb
+++ b/examples/notebooks/solvency.ipynb
@@ -250,7 +250,7 @@
       "outputs": [],
       "source": [
         "# srs path\n",
-        "res = ezkl.get_srs( settings_path)"
+        "res = await ezkl.get_srs( settings_path)"
       ]
     },
     {
@@ -297,7 +297,7 @@
         "\n",
         "witness_path = \"witness.json\"\n",
         "\n",
-        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
+        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
         "assert os.path.isfile(witness_path)\n",
         "\n",
         "# we force the output to be 1 this corresponds to the solvency test being true -- and we set this to a fixed vis output\n",
@@ -411,7 +411,7 @@
       "source": [
         "# now generate the witness file\n",
         "\n",
-        "res = ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path, vk_path)\n",
+        "res = await ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path, vk_path)\n",
         "assert os.path.isfile(witness_path)\n",
         "\n",
         "# we force the output to be 1 this corresponds to the solvency test being true -- and we set this to a fixed vis output\n",
diff --git a/examples/notebooks/stacked_regression.ipynb b/examples/notebooks/stacked_regression.ipynb
index 68167164a..921fed84c 100644
--- a/examples/notebooks/stacked_regression.ipynb
+++ b/examples/notebooks/stacked_regression.ipynb
@@ -167,7 +167,7 @@
                 "res = ezkl.gen_settings(model_path, settings_path)\n",
                 "assert res == True\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True"
             ]
         },
@@ -187,7 +187,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -209,7 +209,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -221,7 +221,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -320,4 +320,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/svm.ipynb b/examples/notebooks/svm.ipynb
index b733138d0..e90458028 100644
--- a/examples/notebooks/svm.ipynb
+++ b/examples/notebooks/svm.ipynb
@@ -180,7 +180,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -202,7 +202,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -214,7 +214,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -420,7 +420,7 @@
                 "res = ezkl.gen_settings(model_path, settings_path)\n",
                 "assert res == True\n",
                 "\n",
-                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                 "assert res == True"
             ]
         }
@@ -446,4 +446,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/tictactoe_autoencoder.ipynb b/examples/notebooks/tictactoe_autoencoder.ipynb
index d8e4ce11c..72340f1f2 100644
--- a/examples/notebooks/tictactoe_autoencoder.ipynb
+++ b/examples/notebooks/tictactoe_autoencoder.ipynb
@@ -13,7 +13,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 1,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -57,7 +57,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 2,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -119,7 +119,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 3,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -163,7 +163,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 4,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -217,7 +217,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 6,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -637,7 +637,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [11])"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [11])"
             ]
         },
         {
@@ -646,7 +646,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "ezkl.get_srs( settings_path)"
+                "await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -683,7 +683,7 @@
                 "    data = json.load(f)\n",
                 "    print(len(data['input_data'][0]))\n",
                 "\n",
-                "ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
             ]
         },
         {
diff --git a/examples/notebooks/tictactoe_binary_classification.ipynb b/examples/notebooks/tictactoe_binary_classification.ipynb
index c7369cf3e..0c021e94a 100644
--- a/examples/notebooks/tictactoe_binary_classification.ipynb
+++ b/examples/notebooks/tictactoe_binary_classification.ipynb
@@ -525,7 +525,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [4])"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [4])"
             ]
         },
         {
@@ -572,7 +572,7 @@
                 "    data = json.load(f)\n",
                 "    print(len(data['input_data'][0]))\n",
                 "\n",
-                "ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
             ]
         },
         {
diff --git a/examples/notebooks/variance.ipynb b/examples/notebooks/variance.ipynb
index 175e14203..21b28e1a0 100644
--- a/examples/notebooks/variance.ipynb
+++ b/examples/notebooks/variance.ipynb
@@ -24,7 +24,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "9Byiv2Nc2MsK"
       },
@@ -49,7 +49,11 @@
         "import pandas as pd\n",
         "import requests\n",
         "import json\n",
-        "import os"
+        "import os\n",
+        "\n",
+        "import logging\n",
+        "\n",
+        "logging.basicConfig(level=logging.INFO)"
       ]
     },
     {
@@ -63,7 +67,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -71,7 +75,15 @@
         "id": "x1vl9ZXF3EEW",
         "outputId": "bda21d02-fe5f-4fb2-8106-f51a8e2e67aa"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "cpu\n"
+          ]
+        }
+      ],
       "source": [
         "from torch import nn\n",
         "import torch\n",
@@ -133,7 +145,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -141,7 +153,18 @@
         "id": "6RAMplxk5xPk",
         "outputId": "bd2158fe-0c00-44fd-e632-6a3f70cdb7c9"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1715422870\n",
+            "1714818070\n",
+            "https://api.coingecko.com/api/v3/coins/ethereum/market_chart/range?vs_currency=usd&from=1714818070&to=1715422870\n",
+            "<Response [200]>\n"
+          ]
+        }
+      ],
       "source": [
         "\n",
         "def get_url(coin, currency, start, end):\n",
@@ -174,7 +197,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -183,7 +206,115 @@
         "id": "WSj1Uxln65vf",
         "outputId": "51422d71-9680-4b51-c4df-e400d20f988b"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>time</th>\n",
+              "      <th>prices</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1714820485367</td>\n",
+              "      <td>3146.785806</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1714824033868</td>\n",
+              "      <td>3127.968728</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1714828058243</td>\n",
+              "      <td>3156.141681</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1714831650751</td>\n",
+              "      <td>3124.834064</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1714834972229</td>\n",
+              "      <td>3133.115333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>163</th>\n",
+              "      <td>1715407579346</td>\n",
+              "      <td>2918.049749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>164</th>\n",
+              "      <td>1715411090715</td>\n",
+              "      <td>2920.330834</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>165</th>\n",
+              "      <td>1715414554830</td>\n",
+              "      <td>2923.986611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>166</th>\n",
+              "      <td>1715418419843</td>\n",
+              "      <td>2910.537671</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>167</th>\n",
+              "      <td>1715421675338</td>\n",
+              "      <td>2907.702307</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>168 rows × 2 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              time       prices\n",
+              "0    1714820485367  3146.785806\n",
+              "1    1714824033868  3127.968728\n",
+              "2    1714828058243  3156.141681\n",
+              "3    1714831650751  3124.834064\n",
+              "4    1714834972229  3133.115333\n",
+              "..             ...          ...\n",
+              "163  1715407579346  2918.049749\n",
+              "164  1715411090715  2920.330834\n",
+              "165  1715414554830  2923.986611\n",
+              "166  1715418419843  2910.537671\n",
+              "167  1715421675338  2907.702307\n",
+              "\n",
+              "[168 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "df = pd.DataFrame(new_data)\n",
         "df\n"
@@ -200,7 +331,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -217,7 +348,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -225,7 +356,98 @@
         "id": "4MmE9SX66_Il",
         "outputId": "16403639-66a4-4280-ac7f-6966b75de5a3"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:ezkl.execute:SRS already exists at that path\n",
+            "INFO:ezkl.execute:num calibration batches: 1\n",
+            "INFO:ezkl.execute:read 16777476 bytes from file (vector of len = 16777476)\n",
+            "WARNING:ezkl.execute:\n",
+            "\n",
+            " <------------- Numerical Fidelity Report (input_scale: 4, param_scale: 4, scale_input_multiplier: 10) ------------->\n",
+            "\n",
+            "+------------+--------------+-----------+-----------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+            "| mean_error | median_error | max_error | min_error | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
+            "+------------+--------------+-----------+-----------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+            "| -727.9929  | -727.9929    | -727.9929 | -727.9929 | 727.9929       | 727.9929         | 727.9929      | 727.9929      | 529973.7           | -0.24999964        | 0.24999964             |\n",
+            "+------------+--------------+-----------+-----------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+            "\n",
+            "\n",
+            "INFO:ezkl.execute:file hash: 41509f380362a8d14401c5ae92073154922fe23e45459ce6f696f58607655db7\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "  \"run_args\": {\n",
+            "    \"tolerance\": {\n",
+            "      \"val\": 0.0,\n",
+            "      \"scale\": 1.0\n",
+            "    },\n",
+            "    \"input_scale\": 4,\n",
+            "    \"param_scale\": 4,\n",
+            "    \"scale_rebase_multiplier\": 10,\n",
+            "    \"lookup_range\": [\n",
+            "      0,\n",
+            "      0\n",
+            "    ],\n",
+            "    \"logrows\": 6,\n",
+            "    \"num_inner_cols\": 2,\n",
+            "    \"variables\": [\n",
+            "      [\n",
+            "        \"batch_size\",\n",
+            "        1\n",
+            "      ]\n",
+            "    ],\n",
+            "    \"input_visibility\": \"Private\",\n",
+            "    \"output_visibility\": \"Public\",\n",
+            "    \"param_visibility\": \"Private\",\n",
+            "    \"div_rebasing\": false,\n",
+            "    \"rebase_frac_zero_constants\": false,\n",
+            "    \"check_mode\": \"UNSAFE\",\n",
+            "    \"commitment\": \"KZG\"\n",
+            "  },\n",
+            "  \"num_rows\": 21,\n",
+            "  \"total_assignments\": 42,\n",
+            "  \"total_const_size\": 0,\n",
+            "  \"total_dynamic_col_size\": 0,\n",
+            "  \"num_dynamic_lookups\": 0,\n",
+            "  \"num_shuffles\": 0,\n",
+            "  \"total_shuffle_col_size\": 0,\n",
+            "  \"model_instance_shapes\": [\n",
+            "    [\n",
+            "      1\n",
+            "    ]\n",
+            "  ],\n",
+            "  \"model_output_scales\": [\n",
+            "    8\n",
+            "  ],\n",
+            "  \"model_input_scales\": [\n",
+            "    4\n",
+            "  ],\n",
+            "  \"module_sizes\": {\n",
+            "    \"polycommit\": [],\n",
+            "    \"poseidon\": [\n",
+            "      0,\n",
+            "      [\n",
+            "        0\n",
+            "      ]\n",
+            "    ]\n",
+            "  },\n",
+            "  \"required_lookups\": [],\n",
+            "  \"required_range_checks\": [],\n",
+            "  \"check_mode\": \"UNSAFE\",\n",
+            "  \"version\": \"0.0.0\",\n",
+            "  \"num_blinding_factors\": null,\n",
+            "  \"timestamp\": 1715422871248\n",
+            "}\n"
+          ]
+        }
+      ],
       "source": [
         "# generate settings\n",
         "onnx_filename = os.path.join('lol.onnx')\n",
@@ -236,9 +458,9 @@
         "\n",
         "\n",
         "ezkl.gen_settings(onnx_filename, settings_filename)\n",
-        "ezkl.calibrate_settings(\n",
+        "await ezkl.calibrate_settings(\n",
         "    input_filename, onnx_filename, settings_filename, \"resources\", scales = [4])\n",
-        "res = ezkl.get_srs(settings_filename)\n",
+        "res = await ezkl.get_srs(settings_filename)\n",
         "ezkl.compile_circuit(onnx_filename, compiled_filename, settings_filename)\n",
         "\n",
         "# show the settings.json\n",
@@ -259,7 +481,24 @@
       "metadata": {
         "id": "fULvvnK7_CMb"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:ezkl.pfsys.srs:loading srs from \"/Users/dante/.ezkl/srs/kzg6.srs\"\n",
+            "INFO:ezkl.execute:downsizing params to 6 logrows\n",
+            "INFO:ezkl.graph.model:model layout...\n",
+            "INFO:ezkl.pfsys:VK took 0.8\n",
+            "INFO:ezkl.graph.model:model layout...\n",
+            "INFO:ezkl.pfsys:PK took 0.2\n",
+            "INFO:ezkl.pfsys:saving verification key 💾\n",
+            "INFO:ezkl.pfsys:done saving verification key ✅\n",
+            "INFO:ezkl.pfsys:saving proving key 💾\n",
+            "INFO:ezkl.pfsys:done saving proving key ✅\n"
+          ]
+        }
+      ],
       "source": [
         "pk_path = os.path.join('test.pk')\n",
         "vk_path = os.path.join('test.vk')\n",
@@ -281,20 +520,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {},
       "outputs": [],
       "source": [
         "\n",
         "witness_path = \"witness.json\"\n",
         "\n",
-        "res = ezkl.gen_witness(input_filename, compiled_filename, witness_path)\n",
+        "res = await ezkl.gen_witness(input_filename, compiled_filename, witness_path)\n",
         "assert os.path.isfile(witness_path)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -302,7 +541,34 @@
         "id": "Oog3j6Kd-Wed",
         "outputId": "5839d0c1-5b43-476e-c2f8-6707de562260"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:ezkl.pfsys:loading proving key from \"test.pk\"\n",
+            "INFO:ezkl.pfsys:done loading proving key ✅\n",
+            "INFO:ezkl.pfsys.srs:loading srs from \"/Users/dante/.ezkl/srs/kzg6.srs\"\n",
+            "INFO:ezkl.execute:downsizing params to 6 logrows\n",
+            "INFO:ezkl.pfsys:proof started...\n",
+            "INFO:ezkl.graph.model:model layout...\n",
+            "INFO:ezkl.pfsys:proof took 0.15\n",
+            "INFO:ezkl.pfsys.srs:loading srs from \"/Users/dante/.ezkl/srs/kzg6.srs\"\n",
+            "INFO:ezkl.execute:downsizing params to 6 logrows\n",
+            "INFO:ezkl.pfsys:loading verification key from \"test.vk\"\n",
+            "INFO:ezkl.pfsys:done loading verification key ✅\n",
+            "INFO:ezkl.execute:verify took 0.2\n",
+            "INFO:ezkl.execute:verified: true\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "verified\n"
+          ]
+        }
+      ],
       "source": [
         "# prove the zk circuit\n",
         "# GENERATE A PROOF\n",
@@ -351,7 +617,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -360,7 +626,26 @@
         "id": "fodkNgwS70FM",
         "outputId": "827b5efd-f74f-44de-c114-861b3a86daf2"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:ezkl.pfsys.srs:loading srs from \"/Users/dante/.ezkl/srs/kzg6.srs\"\n",
+            "INFO:ezkl.execute:downsizing params to 6 logrows\n",
+            "INFO:ezkl.pfsys:loading verification key from \"test.vk\"\n",
+            "INFO:ezkl.pfsys:done loading verification key ✅\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "test.vk\n",
+            "settings.json\n"
+          ]
+        }
+      ],
       "source": [
         "# first we need to create evm verifier\n",
         "print(vk_path)\n",
@@ -370,7 +655,7 @@
         "abi_path = 'test.abi'\n",
         "sol_code_path = 'test.sol'\n",
         "\n",
-        "res = ezkl.create_evm_verifier(\n",
+        "res = await ezkl.create_evm_verifier(\n",
         "        vk_path,\n",
         "        settings_filename,\n",
         "        sol_code_path,\n",
@@ -381,9 +666,18 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:ezkl.eth:using chain 31337\n",
+            "INFO:ezkl.execute:Contract deployed at: 0x998abeb3e57409262ae5b751f60747921b33613e\n"
+          ]
+        }
+      ],
       "source": [
         "# Make sure anvil is running locally first\n",
         "# run with $ anvil -p 3030\n",
@@ -391,8 +685,9 @@
         "import json\n",
         "\n",
         "address_path = os.path.join(\"address.json\")\n",
-        "\n",
-        "res = ezkl.deploy_evm(\n",
+        "sol_code_path = 'test.sol'\n",
+        "# await\n",
+        "res = await ezkl.deploy_evm(\n",
         "    address_path,\n",
         "    sol_code_path,\n",
         "    'http://127.0.0.1:3030'\n",
@@ -406,16 +701,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:ezkl.eth:using chain 31337\n",
+            "INFO:ezkl.eth:estimated verify gas cost: 399775\n",
+            "INFO:ezkl.execute:Solidity verification result: true\n"
+          ]
+        }
+      ],
       "source": [
         "# read the address from addr_path\n",
         "addr = None\n",
         "with open(address_path, 'r') as f:\n",
         "    addr = f.read()\n",
         "\n",
-        "res = ezkl.verify_evm(\n",
+        "res = await ezkl.verify_evm(\n",
         "    addr,\n",
         "    proof_path,\n",
         "    \"http://127.0.0.1:3030\"\n",
@@ -451,7 +756,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.15"
+      "version": "3.12.2"
     }
   },
   "nbformat": 4,
diff --git a/examples/notebooks/verifier_abi.json b/examples/notebooks/verifier_abi.json
new file mode 100644
index 000000000..c34d319a1
--- /dev/null
+++ b/examples/notebooks/verifier_abi.json
@@ -0,0 +1 @@
+[{"type":"function","name":"verifyProof","inputs":[{"name":"proof","type":"bytes","internalType":"bytes"},{"name":"instances","type":"uint256[]","internalType":"uint256[]"}],"outputs":[{"name":"","type":"bool","internalType":"bool"}],"stateMutability":"nonpayable"}]
\ No newline at end of file
diff --git a/examples/notebooks/voice_judge.ipynb b/examples/notebooks/voice_judge.ipynb
index 947e49c17..0c60a0d6d 100644
--- a/examples/notebooks/voice_judge.ipynb
+++ b/examples/notebooks/voice_judge.ipynb
@@ -629,7 +629,7 @@
             "source": [
                 "\n",
                 "\n",
-                "res = ezkl.calibrate_settings(val_data, model_path, settings_path, \"resources\", scales = [4])\n",
+                "res = await ezkl.calibrate_settings(val_data, model_path, settings_path, \"resources\", scales = [4])\n",
                 "assert res == True\n",
                 "print(\"verified\")\n"
             ]
@@ -660,7 +660,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "res = ezkl.get_srs(settings_path)"
+                "res = await ezkl.get_srs(settings_path)"
             ]
         },
         {
@@ -680,7 +680,7 @@
                 "\n",
                 "witness_path = \"witness.json\"\n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -807,7 +807,7 @@
                 "settings_path = os.path.join('settings.json')\n",
                 "\n",
                 "\n",
-                "res = ezkl.create_evm_verifier(\n",
+                "res = await ezkl.create_evm_verifier(\n",
                 "        vk_path,\n",
                 "        \n",
                 "        settings_path,\n",
@@ -847,7 +847,7 @@
                 "\n",
                 "address_path = os.path.join(\"address.json\")\n",
                 "\n",
-                "res = ezkl.deploy_evm(\n",
+                "res = await ezkl.deploy_evm(\n",
                 "    address_path,\n",
                 "    sol_code_path,\n",
                 "    'http://127.0.0.1:3030'\n",
@@ -868,7 +868,7 @@
                 "# make sure anvil is running locally\n",
                 "# $ anvil -p 3030\n",
                 "\n",
-                "res = ezkl.verify_evm(\n",
+                "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
                 "    proof_path,\n",
                 "    \"http://127.0.0.1:3030\"\n",
@@ -905,4 +905,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/world_rotation.ipynb b/examples/notebooks/world_rotation.ipynb
index a966bf861..c5521cc3d 100644
--- a/examples/notebooks/world_rotation.ipynb
+++ b/examples/notebooks/world_rotation.ipynb
@@ -242,6 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "2007dc77",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,6 +258,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ab993958",
    "metadata": {},
    "source": [
     "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
@@ -272,7 +274,7 @@
    "outputs": [],
    "source": [
     "# srs path\n",
-    "res = ezkl.get_srs( settings_path)"
+    "res = await ezkl.get_srs( settings_path)"
    ]
   },
   {
@@ -284,12 +286,13 @@
    "source": [
     "# now generate the witness file \n",
     "\n",
-    "witness = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+    "witness = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
     "assert os.path.isfile(witness_path)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ad58432e",
    "metadata": {},
    "source": [
     "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
@@ -317,6 +320,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1746c8d1",
    "metadata": {},
    "source": [
     "We can now create an EVM verifier contract from our circuit. This contract will be deployed to the chain we are using. In this case we are using a local anvil instance."
@@ -325,15 +329,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "d1920c0f",
    "metadata": {},
    "outputs": [],
    "source": [
     "abi_path = 'test.abi'\n",
     "sol_code_path = 'test.sol'\n",
     "\n",
-    "res = ezkl.create_evm_verifier(\n",
+    "res = await ezkl.create_evm_verifier(\n",
     "        vk_path,\n",
-    "        \n",
     "        settings_path,\n",
     "        sol_code_path,\n",
     "        abi_path,\n",
@@ -344,6 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "0fd7f22b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +356,7 @@
     "\n",
     "addr_path_verifier = \"addr_verifier.txt\"\n",
     "\n",
-    "res = ezkl.deploy_evm(\n",
+    "res = await ezkl.deploy_evm(\n",
     "    addr_path_verifier,\n",
     "    sol_code_path,\n",
     "    'http://127.0.0.1:3030'\n",
@@ -362,6 +367,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9c0dffab",
    "metadata": {},
    "source": [
     "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. \n",
@@ -371,6 +377,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "cc888848",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -378,6 +385,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "c2db14d7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +393,7 @@
     "sol_code_path = 'test.sol'\n",
     "input_path = 'input.json'\n",
     "\n",
-    "res = ezkl.create_evm_data_attestation(\n",
+    "res = await ezkl.create_evm_data_attestation(\n",
     "        input_path,\n",
     "        settings_path,\n",
     "        sol_code_path,\n",
@@ -396,12 +404,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "5a018ba6",
    "metadata": {},
    "outputs": [],
    "source": [
     "addr_path_da = \"addr_da.txt\"\n",
     "\n",
-    "res = ezkl.deploy_da_evm(\n",
+    "res = await ezkl.deploy_da_evm(\n",
     "        addr_path_da,\n",
     "        input_path,\n",
     "        settings_path,\n",
@@ -412,6 +421,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2adad845",
    "metadata": {},
    "source": [
     "Now we can pull in the data from the contract and calculate a new set of coordinates. We then rotate the world by 1 transform and submit the proof to the contract. The contract could then update the world rotation (logic not inserted here). For demo purposes we do this repeatedly, rotating the world by 1 transform. "
@@ -444,6 +454,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "90eda56e",
    "metadata": {},
    "source": [
     "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
@@ -528,7 +539,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/xgboost.ipynb b/examples/notebooks/xgboost.ipynb
index b3bd3a000..27c6e1225 100644
--- a/examples/notebooks/xgboost.ipynb
+++ b/examples/notebooks/xgboost.ipynb
@@ -193,7 +193,7 @@
                 "with open(cal_path, \"w\") as f:\n",
                 "    json.dump(cal_data, f)\n",
                 "\n",
-                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -215,7 +215,7 @@
             "outputs": [],
             "source": [
                 "# srs path\n",
-                "res = ezkl.get_srs( settings_path)"
+                "res = await ezkl.get_srs( settings_path)"
             ]
         },
         {
@@ -227,7 +227,7 @@
             "source": [
                 "# now generate the witness file \n",
                 "\n",
-                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                 "assert os.path.isfile(witness_path)"
             ]
         },
@@ -346,4 +346,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/onnx/multihead_attention/gen.py b/examples/onnx/multihead_attention/gen.py
index 773d1c8ef..3446697c5 100644
--- a/examples/onnx/multihead_attention/gen.py
+++ b/examples/onnx/multihead_attention/gen.py
@@ -104,5 +104,5 @@ def forward(self, x):
 # ezkl.gen_settings("network.onnx", "settings.json")
 
 # !RUST_LOG = full
-# res = ezkl.calibrate_settings(
+# res = await ezkl.calibrate_settings(
 #     "input.json", "network.onnx", "settings.json", "resources")
diff --git a/install_ezkl_cli.sh b/install_ezkl_cli.sh
index 47ddbf8da..b0f3dd774 100644
--- a/install_ezkl_cli.sh
+++ b/install_ezkl_cli.sh
@@ -143,7 +143,7 @@ elif [ "$PLATFORM" == "macos" ]; then
     fi
 
 elif [ "$PLATFORM" == "linux" ]; then
-    if [ "${ARCHITECTURE}" = "amd64" ]; then
+    if [ "$ARCHITECTURE" == "amd64" ]; then
         JSON_RESPONSE=$(curl -s "$RELEASE_URL")
         FILE_URL=$(echo "$JSON_RESPONSE" | grep -o 'https://github.com[^"]*' | grep "build-artifacts.ezkl-linux-gnu.tar.gz")
 
@@ -155,9 +155,20 @@ elif [ "$PLATFORM" == "linux" ]; then
 
         echo "Cleaning up"
         rm "$EZKL_DIR/build-artifacts.ezkl-linux-gnu.tar.gz"
+    else if [ "$ARCHITECTURE" == "aarch64" ]; then
+        JSON_RESPONSE=$(curl -s "$RELEASE_URL")
+        FILE_URL=$(echo "$JSON_RESPONSE" | grep -o 'https://github.com[^"]*' | grep "build-artifacts.ezkl-linux-aarch64.tar.gz")
+
+        echo "Downloading package"
+        curl -L "$FILE_URL" -o "$EZKL_DIR/build-artifacts.ezkl-linux-aarch64.tar.gz"
 
+        echo "Unpacking package"
+        tar -xzf "$EZKL_DIR/build-artifacts.ezkl-linux-aarch64.tar.gz" -C "$EZKL_DIR"
+
+        echo "Cleaning up"
+        rm "$EZKL_DIR/build-artifacts.ezkl-linux-aarch64.tar.gz"
     else
-        echo "ARM architectures are not supported for Linux at the moment. If you would need support for the ARM architectures on linux please submit an issue https://github.com/zkonduit/ezkl/issues/new/choose"
+        echo "Non aarch ARM architectures are not supported for Linux at the moment. If you would need support for the ARM architectures on linux please submit an issue https://github.com/zkonduit/ezkl/issues/new/choose"
         exit 1
     fi
 else
diff --git a/pyproject.toml b/pyproject.toml
index 949b97e6b..b880c3ece 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ addopts = "-rfEX -p pytester --strict-markers"
 testpaths = [
     "tests/python/*_tests.py",
 ]
+asyncio_mode = "auto"
 
 [project]
 name = "ezkl"
diff --git a/src/bin/ezkl.rs b/src/bin/ezkl.rs
index 902aabb61..0e9b8dbf2 100644
--- a/src/bin/ezkl.rs
+++ b/src/bin/ezkl.rs
@@ -11,7 +11,7 @@ use ezkl::execute::run;
 #[cfg(not(target_arch = "wasm32"))]
 use ezkl::logger::init_logger;
 #[cfg(not(target_arch = "wasm32"))]
-use log::{debug, error, info};
+use log::{error, info};
 #[cfg(not(any(target_arch = "wasm32", feature = "no-banner")))]
 use rand::prelude::SliceRandom;
 #[cfg(not(target_arch = "wasm32"))]
@@ -33,7 +33,7 @@ pub async fn main() -> Result<(), Box<dyn Error>> {
     } else {
         info!("Running with CPU");
     }
-    debug!("command: \n {}", &args.as_json()?.to_colored_json_auto()?);
+    info!("command: \n {}", &args.as_json()?.to_colored_json_auto()?);
     let res = run(args.command).await;
     match &res {
         Ok(_) => info!("succeeded"),
diff --git a/src/commands.rs b/src/commands.rs
index b30cba923..c58896f68 100644
--- a/src/commands.rs
+++ b/src/commands.rs
@@ -1,6 +1,6 @@
-use clap::{Parser, Subcommand};
 #[cfg(not(target_arch = "wasm32"))]
-use ethers::types::H160;
+use alloy::primitives::Address as H160;
+use clap::{Parser, Subcommand};
 #[cfg(feature = "python-bindings")]
 use pyo3::{
     conversion::{FromPyObject, PyTryFrom},
diff --git a/src/eth.rs b/src/eth.rs
index b12a36784..f9215e90d 100644
--- a/src/eth.rs
+++ b/src/eth.rs
@@ -5,74 +5,254 @@ use crate::graph::DataSource;
 use crate::graph::GraphSettings;
 use crate::pfsys::evm::EvmVerificationError;
 use crate::pfsys::Snark;
-use ethers::abi::Contract;
-use ethers::contract::abigen;
-use ethers::contract::ContractFactory;
-use ethers::core::k256::ecdsa::SigningKey;
-use ethers::middleware::SignerMiddleware;
-use ethers::prelude::ContractInstance;
+use alloy::contract::CallBuilder;
+use alloy::core::primitives::Address as H160;
+use alloy::core::primitives::Bytes;
+use alloy::core::primitives::U256;
+use alloy::dyn_abi::abi::token::{DynSeqToken, PackedSeqToken, WordToken};
+use alloy::dyn_abi::abi::TokenSeq;
 #[cfg(target_arch = "wasm32")]
-use ethers::prelude::Wallet;
-use ethers::providers::Middleware;
-use ethers::providers::{Http, Provider};
-use ethers::signers::Signer;
-use ethers::solc::{CompilerInput, Solc};
-use ethers::types::transaction::eip2718::TypedTransaction;
-use ethers::types::TransactionRequest;
-use ethers::types::H160;
-use ethers::types::U256;
-use ethers::types::{Bytes, I256};
-#[cfg(not(target_arch = "wasm32"))]
-use ethers::{
-    prelude::{LocalWallet, Wallet},
-    utils::{Anvil, AnvilInstance},
+use alloy::prelude::Wallet;
+// use alloy::providers::Middleware;
+use alloy::json_abi::JsonAbi;
+use alloy::node_bindings::Anvil;
+use alloy::primitives::{B256, I256};
+use alloy::providers::fillers::{
+    ChainIdFiller, FillProvider, GasFiller, JoinFill, NonceFiller, SignerFiller,
 };
+use alloy::providers::network::{Ethereum, EthereumSigner};
+use alloy::providers::ProviderBuilder;
+use alloy::providers::{Identity, Provider, RootProvider};
+use alloy::rpc::types::eth::BlockId;
+use alloy::rpc::types::eth::TransactionInput;
+use alloy::rpc::types::eth::TransactionRequest;
+use alloy::signers::wallet::LocalWallet;
+use alloy::sol as abigen;
+use alloy::transports::http::Http;
+use foundry_compilers::artifacts::Settings as SolcSettings;
+use foundry_compilers::Solc;
 use halo2_solidity_verifier::encode_calldata;
 use halo2curves::bn256::{Fr, G1Affine};
 use halo2curves::group::ff::PrimeField;
+use itertools::Itertools;
 use log::{debug, info, warn};
+use reqwest::Client;
 use std::error::Error;
 use std::path::PathBuf;
-#[cfg(not(target_arch = "wasm32"))]
-use std::time::Duration;
-use std::{convert::TryFrom, sync::Arc};
+use std::str::FromStr;
+use std::sync::Arc;
+
+const ANVIL_DEFAULT_PRIVATE_KEY: &str =
+    "ac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80";
 
-/// A local ethers-rs based client
-pub type EthersClient = Arc<SignerMiddleware<Provider<Http>, Wallet<SigningKey>>>;
+pub const DEFAULT_ANVIL_ENDPOINT: &str = "http://localhost:8545";
 
 // Generate contract bindings OUTSIDE the functions so they are part of library
-abigen!(TestReads, "./abis/TestReads.json");
-abigen!(DataAttestation, "./abis/DataAttestation.json");
-abigen!(QuantizeData, "./abis/QuantizeData.json");
+abigen!(
+    #[allow(missing_docs)]
+    #[sol(rpc, bytecode="60806040523461012d576102008038038061001981610131565b9283398101602090818382031261012d5782516001600160401b039384821161012d57019080601f8301121561012d5781519384116100fb5760059184831b908480610066818501610131565b80988152019282010192831161012d5784809101915b83831061011d57505050505f5b835181101561010f578281831b850101515f54680100000000000000008110156100fb5760018101805f558110156100e7575f8052845f2001555f1981146100d357600101610089565b634e487b7160e01b5f52601160045260245ffd5b634e487b7160e01b5f52603260045260245ffd5b634e487b7160e01b5f52604160045260245ffd5b60405160a990816101578239f35b825181529181019185910161007c565b5f80fd5b6040519190601f01601f191682016001600160401b038111838210176100fb5760405256fe60808060405260043610156011575f80fd5b5f90813560e01c6371e5ee5f146025575f80fd5b34606f576020366003190112606f576004358254811015606b5782602093527f290decd9548b62a8d60345a988386fc84ba6bc95484008f6362f93160ef3e56301548152f35b8280fd5b5080fdfea2646970667358221220dc28d7ff0d25a49f74c6b97a87c7c6039ee98d715c0f61be72cc4d180d40a41e64736f6c63430008140033")]
+    contract TestReads {
+        int[] public arr;
+
+        constructor(int256[] memory _numbers) {
+            for (uint256 i = 0; i < _numbers.length; i++) {
+                arr.push(_numbers[i]);
+            }
+        }
+    }
+);
+abigen!(
+    #[allow(missing_docs)]
+    #[sol(rpc)]
+    DataAttestation,
+    "./abis/DataAttestation.json"
+);
+abigen!(
+    #[allow(missing_docs)]
+    #[sol(rpc, bytecode="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")]
+    contract QuantizeData {
+        /**
+         * @notice EZKL P value
+         * @dev In order to prevent the verifier from accepting two version of the same instance, n and the quantity (n + P),  where n + P <= 2^256, we require that all instances are stricly less than P. a
+         * @dev The reason for this is that the assmebly code of the verifier performs all arithmetic operations modulo P and as a consequence can't distinguish between n and n + P.
+         */
+        uint256 constant ORDER =
+            uint256(
+                0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001
+            );
+
+        // /**
+        //  * @notice Calculates floor(x * y / denominator) with full precision. Throws if result overflows a uint256 or denominator == 0
+        //  * @dev Original credit to Remco Bloemen under MIT license (https://xn--2-umb.com/21/muldiv)
+        //  * with further edits by Uniswap Labs also under MIT license.
+        //  */
+        function mulDiv(
+            uint256 x,
+            uint256 y,
+            uint256 denominator
+        ) internal pure returns (uint256 result) {
+            unchecked {
+                // 512-bit multiply [prod1 prod0] = x * y. Compute the product mod 2^256 and mod 2^256 - 1, then use
+                // use the Chinese Remainder Theorem to reconstruct the 512 bit result. The result is stored in two 256
+                // variables such that product = prod1 * 2^256 + prod0.
+                uint256 prod0; // Least significant 256 bits of the product
+                uint256 prod1; // Most significant 256 bits of the product
+                assembly {
+                    let mm := mulmod(x, y, not(0))
+                    prod0 := mul(x, y)
+                    prod1 := sub(sub(mm, prod0), lt(mm, prod0))
+                }
+
+                // Handle non-overflow cases, 256 by 256 division.
+                if (prod1 == 0) {
+                    // Solidity will revert if denominator == 0, unlike the div opcode on its own.
+                    // The surrounding unchecked block does not change this fact.
+                    // See https://docs.soliditylang.org/en/latest/control-structures.html#checked-or-unchecked-arithmetic.
+                    return prod0 / denominator;
+                }
+
+                // Make sure the result is less than 2^256. Also prevents denominator == 0.
+                require(denominator > prod1, "Math: mulDiv overflow");
+
+                ///////////////////////////////////////////////
+                // 512 by 256 division.
+                ///////////////////////////////////////////////
+
+                // Make division exact by subtracting the remainder from [prod1 prod0].
+                uint256 remainder;
+                assembly {
+                    // Compute remainder using mulmod.
+                    remainder := mulmod(x, y, denominator)
+
+                    // Subtract 256 bit number from 512 bit number.
+                    prod1 := sub(prod1, gt(remainder, prod0))
+                    prod0 := sub(prod0, remainder)
+                }
+
+                // Factor powers of two out of denominator and compute largest power of two divisor of denominator. Always >= 1.
+                // See https://cs.stackexchange.com/q/138556/92363.
+
+                // Does not overflow because the denominator cannot be zero at this stage in the function.
+                uint256 twos = denominator & (~denominator + 1);
+                assembly {
+                    // Divide denominator by twos.
+                    denominator := div(denominator, twos)
+
+                    // Divide [prod1 prod0] by twos.
+                    prod0 := div(prod0, twos)
+
+                    // Flip twos such that it is 2^256 / twos. If twos is zero, then it becomes one.
+                    twos := add(div(sub(0, twos), twos), 1)
+                }
+
+                // Shift in bits from prod1 into prod0.
+                prod0 |= prod1 * twos;
+
+                // Invert denominator mod 2^256. Now that denominator is an odd number, it has an inverse modulo 2^256 such
+                // that denominator * inv = 1 mod 2^256. Compute the inverse by starting with a seed that is correct for
+                // four bits. That is, denominator * inv = 1 mod 2^4.
+                uint256 inverse = (3 * denominator) ^ 2;
+
+                // Use the Newton-Raphson iteration to improve the precision. Thanks to Hensel's lifting lemma, this also works
+                // in modular arithmetic, doubling the correct bits in each step.
+                inverse *= 2 - denominator * inverse; // inverse mod 2^8
+                inverse *= 2 - denominator * inverse; // inverse mod 2^16
+                inverse *= 2 - denominator * inverse; // inverse mod 2^32
+                inverse *= 2 - denominator * inverse; // inverse mod 2^64
+                inverse *= 2 - denominator * inverse; // inverse mod 2^128
+                inverse *= 2 - denominator * inverse; // inverse mod 2^256
+
+                // Because the division is now exact we can divide by multiplying with the modular inverse of denominator.
+                // This will give us the correct result modulo 2^256. Since the preconditions guarantee that the outcome is
+                // less than 2^256, this is the final result. We don't need to compute the high bits of the result and prod1
+                // is no longer required.
+                result = prod0 * inverse;
+                return result;
+            }
+        }
+
+        function quantize_data(
+            bytes[] memory data,
+            uint256[] memory decimals,
+            uint256[] memory scales
+        ) external pure returns (int256[] memory quantized_data) {
+            quantized_data = new int256[](data.length);
+            for (uint i; i < data.length; i++) {
+                int x = abi.decode(data[i], (int256));
+                bool neg = x < 0;
+                if (neg) x = -x;
+                uint denom = 10 ** decimals[i];
+                uint scale = 1 << scales[i];
+                uint output = mulDiv(uint256(x), scale, denom);
+                if (mulmod(uint256(x), scale, denom) * 2 >= denom) {
+                    output += 1;
+                }
+
+                quantized_data[i] = neg ? -int256(output) : int256(output);
+            }
+        }
 
-const TESTREADS_SOL: &str = include_str!("../contracts/TestReads.sol");
-const QUANTIZE_DATA_SOL: &str = include_str!("../contracts/QuantizeData.sol");
+        function to_field_element(
+            int64[] memory quantized_data
+        ) public pure returns (uint256[] memory output) {
+            output = new uint256[](quantized_data.length);
+            for (uint i; i < quantized_data.length; i++) {
+                output[i] = uint256(quantized_data[i] + int(ORDER)) % ORDER;
+            }
+        }
+
+        function check_is_valid_field_element(
+            int256[] memory quantized_data
+        ) public pure returns (uint256[] memory output) {
+            output = new uint256[](quantized_data.length);
+            for (uint i; i < quantized_data.length; i++) {
+                // assert it is a positive number and less than ORDER
+                require(quantized_data[i] >= 0 && uint256(quantized_data[i]) <= ORDER, "Invalid field element");
+                output[i] = uint256(quantized_data[i]);
+            }
+        }
+    }
+);
+
+// we have to generate these two contract differently because they are generated dynamically ! and hence the static compilation from above does not suit
 const ATTESTDATA_SOL: &str = include_str!("../contracts/AttestData.sol");
-const LOADINSTANCES_SOL: &str = include_str!("../contracts/LoadInstances.sol");
+
+pub type EthersClient = Arc<
+    FillProvider<
+        JoinFill<
+            JoinFill<JoinFill<JoinFill<Identity, GasFiller>, NonceFiller>, ChainIdFiller>,
+            SignerFiller<EthereumSigner>,
+        >,
+        RootProvider<Http<Client>>,
+        Http<Client>,
+        Ethereum,
+    >,
+>;
+
+pub type ContractFactory<M> = CallBuilder<Http<Client>, Arc<M>, ()>;
 
 /// Return an instance of Anvil and a client for the given RPC URL. If none is provided, a local client is used.
 #[cfg(not(target_arch = "wasm32"))]
 pub async fn setup_eth_backend(
     rpc_url: Option<&str>,
     private_key: Option<&str>,
-) -> Result<(AnvilInstance, EthersClient), Box<dyn Error>> {
+) -> Result<(EthersClient, alloy::primitives::Address), Box<dyn Error>> {
     // Launch anvil
-    let anvil = Anvil::new()
-        .args(["--code-size-limit=41943040", "--disable-block-gas-limit"])
-        .spawn();
 
     let endpoint: String;
     if let Some(rpc_url) = rpc_url {
         endpoint = rpc_url.to_string();
     } else {
+        let anvil = Anvil::new()
+            .args([
+                "--code-size-limit=41943040",
+                "--disable-block-gas-limit",
+                "-p",
+                "8545",
+            ])
+            .spawn();
         endpoint = anvil.endpoint();
-    };
-
-    // Connect to the network
-    let provider = Provider::<Http>::try_from(endpoint)?.interval(Duration::from_millis(10u64));
-
-    let chain_id = provider.get_chainid().await?.as_u64();
-    info!("using chain {}", chain_id);
+    }
 
     // Instantiate the wallet
     let wallet: LocalWallet;
@@ -85,19 +265,25 @@ pub async fn setup_eth_backend(
             return Err(private_key_format_error.into());
         }
         let private_key_buffer = hex::decode(private_key)?;
-        let signing_key = SigningKey::from_slice(&private_key_buffer)?;
-        wallet = LocalWallet::from(signing_key);
+        wallet = LocalWallet::from_slice(&private_key_buffer)?;
     } else {
-        wallet = anvil.keys()[0].clone().into();
+        wallet = LocalWallet::from_str(ANVIL_DEFAULT_PRIVATE_KEY)?;
     }
 
-    // Instantiate the client with the signer
-    let client = Arc::new(SignerMiddleware::new(
-        provider,
-        wallet.with_chain_id(chain_id),
-    ));
+    let wallet_address = wallet.address();
 
-    Ok((anvil, client))
+    // Connect to the network
+    let client = Arc::new(
+        ProviderBuilder::new()
+            .with_recommended_fillers()
+            .signer(EthereumSigner::from(wallet))
+            .on_http(endpoint.parse()?),
+    );
+
+    let chain_id = client.get_chain_id().await?;
+    info!("using chain {}", chain_id);
+
+    Ok((client, wallet_address))
 }
 
 ///
@@ -107,19 +293,18 @@ pub async fn deploy_contract_via_solidity(
     runs: usize,
     private_key: Option<&str>,
     contract_name: &str,
-) -> Result<ethers::types::Address, Box<dyn Error>> {
+) -> Result<H160, Box<dyn Error>> {
     // anvil instance must be alive at least until the factory completes the deploy
-    let (anvil, client) = setup_eth_backend(rpc_url, private_key).await?;
+    let (client, _) = setup_eth_backend(rpc_url, private_key).await?;
 
     let (abi, bytecode, runtime_bytecode) =
-        get_contract_artifacts(sol_code_path, contract_name, runs)?;
+        get_contract_artifacts(sol_code_path, contract_name, runs).await?;
 
-    let factory = get_sol_contract_factory(abi, bytecode, runtime_bytecode, client.clone())?;
-    let contract = factory.deploy(())?.send().await?;
-    let addr = contract.address();
+    let factory =
+        get_sol_contract_factory(abi, bytecode, runtime_bytecode, client.clone(), None::<()>)?;
+    let contract = factory.deploy().await?;
 
-    drop(anvil);
-    Ok(addr)
+    Ok(contract)
 }
 
 ///
@@ -130,8 +315,8 @@ pub async fn deploy_da_verifier_via_solidity(
     rpc_url: Option<&str>,
     runs: usize,
     private_key: Option<&str>,
-) -> Result<ethers::types::Address, Box<dyn Error>> {
-    let (anvil, client) = setup_eth_backend(rpc_url, private_key).await?;
+) -> Result<H160, Box<dyn Error>> {
+    let (client, client_address) = setup_eth_backend(rpc_url, private_key).await?;
 
     let input = GraphData::from_path(input)?;
 
@@ -220,27 +405,65 @@ pub async fn deploy_da_verifier_via_solidity(
     };
 
     let (abi, bytecode, runtime_bytecode) =
-        get_contract_artifacts(sol_code_path, "DataAttestation", runs)?;
-    let factory = get_sol_contract_factory(abi, bytecode, runtime_bytecode, client.clone())?;
-
-    info!("call_data: {:#?}", call_data);
-    info!("contract_addresses: {:#?}", contract_addresses);
-    info!("decimals: {:#?}", decimals);
-
-    let contract = factory
-        .deploy((
-            contract_addresses,
-            call_data,
-            decimals,
-            scales,
-            contract_instance_offset as u32,
-            client.address(),
-        ))?
-        .send()
-        .await?;
+        get_contract_artifacts(sol_code_path, "DataAttestation", runs).await?;
+
+    let factory = get_sol_contract_factory(
+        abi,
+        bytecode,
+        runtime_bytecode,
+        client.clone(),
+        Some((
+            // address[] memory _contractAddresses,
+            DynSeqToken(
+                contract_addresses
+                    .iter()
+                    .map(|ca| WordToken(ca.into_word()))
+                    .collect_vec(),
+            ),
+            // bytes[][] memory _callData,
+            DynSeqToken(
+                call_data
+                    .iter()
+                    .map(|bytes| {
+                        DynSeqToken(
+                            bytes
+                                .iter()
+                                .map(|i| PackedSeqToken(i.as_ref()))
+                                .collect_vec(),
+                        )
+                    })
+                    .collect::<Vec<_>>(),
+            ),
+            // uint256[][] memory _decimals,
+            DynSeqToken(
+                decimals
+                    .iter()
+                    .map(|ints| {
+                        DynSeqToken(ints.iter().map(|i| WordToken(B256::from(*i))).collect_vec())
+                    })
+                    .collect::<Vec<_>>(),
+            ),
+            // uint[] memory _scales,
+            DynSeqToken(
+                scales
+                    .into_iter()
+                    .map(|i| WordToken(U256::from(i).into()))
+                    .collect_vec(),
+            ),
+            //  uint8 _instanceOffset,
+            WordToken(U256::from(contract_instance_offset as u32).into()),
+            //address _admin
+            WordToken(client_address.into_word()),
+        )),
+    )?;
+
+    debug!("call_data: {:#?}", call_data);
+    debug!("contract_addresses: {:#?}", contract_addresses);
+    debug!("decimals: {:#?}", decimals);
+
+    let contract = factory.deploy().await?;
 
-    drop(anvil);
-    Ok(contract.address())
+    Ok(contract)
 }
 
 type ParsedCallsToAccount = (Vec<H160>, Vec<Vec<Bytes>>, Vec<Vec<U256>>);
@@ -259,8 +482,8 @@ fn parse_calls_to_accounts(
         decimals.push(vec![]);
         for (call, decimal) in &val.call_data {
             let call_data_bytes = hex::decode(call)?;
-            call_data[i].push(ethers::types::Bytes::from(call_data_bytes));
-            decimals[i].push(ethers::types::U256::from_dec_str(&decimal.to_string())?);
+            call_data[i].push(Bytes::from(call_data_bytes));
+            decimals[i].push(I256::from_dec_str(&decimal.to_string())?.unsigned_abs());
         }
     }
     Ok((contract_addresses, call_data, decimals))
@@ -294,38 +517,38 @@ pub async fn update_account_calls(
         return Err("Data source for either input_data or output_data must be OnChain".into());
     };
 
-    let (anvil, client) = setup_eth_backend(rpc_url, None).await?;
+    let (client, client_address) = setup_eth_backend(rpc_url, None).await?;
 
     let contract = DataAttestation::new(addr, client.clone());
 
-    contract
-        .update_account_calls(
+    info!("contract_addresses: {:#?}", contract_addresses);
+
+    let _ = contract
+        .updateAccountCalls(
             contract_addresses.clone(),
             call_data.clone(),
             decimals.clone(),
         )
+        .from(client_address)
         .send()
         .await?;
 
-    // Instantiate a different wallet
-    let wallet: LocalWallet = anvil.keys()[1].clone().into();
-
-    let client = Arc::new(client.with_signer(wallet.with_chain_id(anvil.chain_id())));
-
     // update contract signer with non admin account
     let contract = DataAttestation::new(addr, client.clone());
 
+    info!("contract_addresses: {:#?}", contract_addresses);
+
     // call to update_account_calls should fail
 
     if (contract
-        .update_account_calls(contract_addresses, call_data, decimals)
+        .updateAccountCalls(contract_addresses, call_data, decimals)
         .send()
         .await)
         .is_err()
     {
-        info!("update_account_calls failed as expected");
+        info!("updateAccountCalls failed as expected");
     } else {
-        return Err("update_account_calls should have failed".into());
+        return Err("updateAccountCalls should have failed".into());
     }
 
     Ok(())
@@ -335,56 +558,54 @@ pub async fn update_account_calls(
 #[cfg(not(target_arch = "wasm32"))]
 pub async fn verify_proof_via_solidity(
     proof: Snark<Fr, G1Affine>,
-    addr: ethers::types::Address,
+    addr: H160,
     addr_vk: Option<H160>,
     rpc_url: Option<&str>,
 ) -> Result<bool, Box<dyn Error>> {
     let flattened_instances = proof.instances.into_iter().flatten();
 
     let encoded = encode_calldata(
-        addr_vk.as_ref().map(|x| x.0),
+        addr_vk.as_ref().map(|x| x.0).map(|x| x.0),
         &proof.proof,
         &flattened_instances.collect::<Vec<_>>(),
     );
 
-    info!("encoded: {:#?}", hex::encode(&encoded));
-    let (anvil, client) = setup_eth_backend(rpc_url, None).await?;
-    let tx: TypedTransaction = TransactionRequest::default()
-        .to(addr)
-        .from(client.address())
-        .data(encoded)
-        .into();
+    debug!("encoded: {:#?}", hex::encode(&encoded));
+
+    let input: TransactionInput = encoded.into();
+
+    let (client, _) = setup_eth_backend(rpc_url, None).await?;
+    let tx = TransactionRequest::default().to(addr).input(input);
     debug!("transaction {:#?}", tx);
 
-    let result = client.call(&tx, None).await;
+    let result = client.call(&tx).await;
 
     if result.is_err() {
         return Err(Box::new(EvmVerificationError::SolidityExecution));
     }
     let result = result?;
-    info!("result: {:#?}", result.to_vec());
+    debug!("result: {:#?}", result.to_vec());
     // decode return bytes value into uint8
     let result = result.to_vec().last().ok_or("no contract output")? == &1u8;
     if !result {
         return Err(Box::new(EvmVerificationError::InvalidProof));
     }
 
-    let gas = client.estimate_gas(&tx, None).await?;
+    let gas = client.estimate_gas(&tx, BlockId::default()).await?;
 
     info!("estimated verify gas cost: {:#?}", gas);
 
     // if gas is greater than 30 million warn the user that the gas cost is above ethereum's 30 million block gas limit
-    if gas > 30_000_000.into() {
+    if gas > 30_000_000_u128 {
         warn!(
             "Gas cost of verify transaction is greater than 30 million block gas limit. It will fail on mainnet."
         );
-    } else if gas > 15_000_000.into() {
+    } else if gas > 15_000_000_u128 {
         warn!(
             "Gas cost of verify transaction is greater than 15 million, the target block size for ethereum"
         );
     }
 
-    drop(anvil);
     Ok(true)
 }
 
@@ -403,20 +624,10 @@ fn count_decimal_places(num: f32) -> usize {
 }
 
 ///
-pub async fn setup_test_contract<M: 'static + Middleware>(
+pub async fn setup_test_contract<M: 'static + Provider<Http<Client>, Ethereum>>(
     client: Arc<M>,
     data: &[Vec<FileSourceInner>],
-) -> Result<(ContractInstance<Arc<M>, M>, Vec<u8>), Box<dyn Error>> {
-    // save the abi to a tmp file
-    let mut sol_path = std::env::temp_dir();
-    sol_path.push("testreads.sol");
-    std::fs::write(&sol_path, TESTREADS_SOL)?;
-
-    // Compile the contract
-    let (abi, bytecode, runtime_bytecode) = get_contract_artifacts(sol_path, "TestReads", 0)?;
-
-    let factory = get_sol_contract_factory(abi, bytecode, runtime_bytecode, client.clone())?;
-
+) -> Result<(TestReads::TestReadsInstance<Http<Client>, Arc<M>>, Vec<u8>), Box<dyn Error>> {
     let mut decimals = vec![];
     let mut scaled_by_decimals_data = vec![];
     for input in &data[0] {
@@ -424,17 +635,23 @@ pub async fn setup_test_contract<M: 'static + Middleware>(
             let input = input.to_float() as f32;
             let decimal_places = count_decimal_places(input) as u8;
             let scaled_by_decimals = input * f32::powf(10., decimal_places.into());
-            scaled_by_decimals_data.push(I256::from(scaled_by_decimals as i64));
+            scaled_by_decimals_data.push(I256::from_dec_str(
+                &(scaled_by_decimals as i32).to_string(),
+            )?);
             decimals.push(decimal_places);
         } else if input.is_field() {
             let input = input.to_field(0);
             let hex_str_fr = format!("{:?}", input);
-            scaled_by_decimals_data.push(I256::from_raw(U256::from_str_radix(&hex_str_fr, 16)?));
+            // remove the 0x prefix
+            let hex_str_fr = &hex_str_fr[2..];
+            scaled_by_decimals_data.push(I256::from_raw(U256::from_str_radix(hex_str_fr, 16)?));
             decimals.push(0);
         }
     }
 
-    let contract = factory.deploy(scaled_by_decimals_data)?.send().await?;
+    // Compile the contract
+    let contract = TestReads::deploy(client, scaled_by_decimals_data).await?;
+
     Ok((contract, decimals))
 }
 
@@ -443,35 +660,32 @@ pub async fn setup_test_contract<M: 'static + Middleware>(
 #[cfg(not(target_arch = "wasm32"))]
 pub async fn verify_proof_with_data_attestation(
     proof: Snark<Fr, G1Affine>,
-    addr_verifier: ethers::types::Address,
-    addr_da: ethers::types::Address,
+    addr_verifier: H160,
+    addr_da: H160,
     addr_vk: Option<H160>,
     rpc_url: Option<&str>,
 ) -> Result<bool, Box<dyn Error>> {
-    use ethers::abi::{Function, Param, ParamType, StateMutability, Token};
+    use ethabi::{Function, Param, ParamType, StateMutability, Token};
 
     let mut public_inputs: Vec<U256> = vec![];
     let flattened_instances = proof.instances.into_iter().flatten();
 
     for val in flattened_instances.clone() {
         let bytes = val.to_repr();
-        let u = U256::from_little_endian(bytes.as_slice());
+        let u = U256::from_le_slice(bytes.as_slice());
         public_inputs.push(u);
     }
 
     let encoded_verifier = encode_calldata(
-        addr_vk.as_ref().map(|x| x.0),
+        addr_vk.as_ref().map(|x| x.0).map(|x| x.0),
         &proof.proof,
         &flattened_instances.collect::<Vec<_>>(),
     );
 
-    info!("encoded: {:#?}", hex::encode(&encoded_verifier));
+    debug!("encoded: {:#?}", hex::encode(&encoded_verifier));
 
-    info!("public_inputs: {:#?}", public_inputs);
-    info!(
-        "proof: {:#?}",
-        ethers::types::Bytes::from(proof.proof.to_vec())
-    );
+    debug!("public_inputs: {:#?}", public_inputs);
+    debug!("proof: {:#?}", Bytes::from(proof.proof.to_vec()));
 
     #[allow(deprecated)]
     let func = Function {
@@ -498,68 +712,58 @@ pub async fn verify_proof_with_data_attestation(
     };
 
     let encoded = func.encode_input(&[
-        Token::Address(addr_verifier),
+        Token::Address(addr_verifier.0 .0.into()),
         Token::Bytes(encoded_verifier),
     ])?;
 
-    info!("encoded: {:#?}", hex::encode(&encoded));
-    let (anvil, client) = setup_eth_backend(rpc_url, None).await?;
-    let tx: TypedTransaction = TransactionRequest::default()
-        .to(addr_da)
-        .from(client.address())
-        .data(encoded)
-        .into();
+    debug!("encoded: {:#?}", hex::encode(&encoded));
+
+    let encoded: TransactionInput = encoded.into();
+
+    let (client, _) = setup_eth_backend(rpc_url, None).await?;
+    let tx = TransactionRequest::default().to(addr_da).input(encoded);
     debug!("transaction {:#?}", tx);
     info!(
         "estimated verify gas cost: {:#?}",
-        client.estimate_gas(&tx, None).await?
+        client.estimate_gas(&tx, BlockId::default()).await?
     );
 
-    let result = client.call(&tx, None).await;
+    let result = client.call(&tx).await;
     if result.is_err() {
         return Err(Box::new(EvmVerificationError::SolidityExecution));
     }
     let result = result?;
-    info!("result: {:#?}", result);
+    debug!("result: {:#?}", result);
     // decode return bytes value into uint8
     let result = result.to_vec().last().ok_or("no contract output")? == &1u8;
     if !result {
         return Err(Box::new(EvmVerificationError::InvalidProof));
     }
-    drop(anvil);
-    Ok(true)
-}
 
-/// get_provider returns a JSON RPC HTTP Provider
-pub fn get_provider(rpc_url: &str) -> Result<Provider<Http>, Box<dyn Error>> {
-    let provider = Provider::<Http>::try_from(rpc_url)?;
-    debug!("{:#?}", provider);
-    Ok(provider)
+    Ok(true)
 }
 
 /// Tests on-chain data storage by deploying a contract that stores the network input and or output
 /// data in its storage. It does this by converting the floating point values to integers and storing the
 /// the number of decimals of the floating point value on chain.
-pub async fn test_on_chain_data<M: 'static + Middleware>(
+pub async fn test_on_chain_data<M: 'static + Provider<Http<Client>, Ethereum>>(
     client: Arc<M>,
     data: &[Vec<FileSourceInner>],
 ) -> Result<Vec<CallsToAccount>, Box<dyn Error>> {
     let (contract, decimals) = setup_test_contract(client.clone(), data).await?;
 
-    let contract = TestReads::new(contract.address(), client.clone());
-
     // Get the encoded call data for each input
     let mut calldata = vec![];
     for (i, _) in data.iter().flatten().enumerate() {
-        let function = contract.method::<_, I256>("arr", i as u32)?;
-        let call = function.calldata().ok_or("could not get calldata")?;
+        let builder = contract.arr(U256::from(i));
+        let call = builder.calldata();
         // Push (call, decimals) to the calldata vector.
         calldata.push((hex::encode(call), decimals[i]));
     }
     // Instantiate a new CallsToAccount struct
     let calls_to_account = CallsToAccount {
         call_data: calldata,
-        address: hex::encode(contract.address().as_bytes()),
+        address: hex::encode(contract.address().0 .0),
     };
     info!("calls_to_account: {:#?}", calls_to_account);
     Ok(vec![calls_to_account])
@@ -567,12 +771,13 @@ pub async fn test_on_chain_data<M: 'static + Middleware>(
 
 /// Reads on-chain inputs, returning the raw encoded data returned from making all the calls in on_chain_input_data
 #[cfg(not(target_arch = "wasm32"))]
-pub async fn read_on_chain_inputs<M: 'static + Middleware>(
+pub async fn read_on_chain_inputs<M: 'static + Provider<Http<Client>, Ethereum>>(
     client: Arc<M>,
     address: H160,
     data: &Vec<CallsToAccount>,
 ) -> Result<(Vec<Bytes>, Vec<u8>), Box<dyn Error>> {
     // Iterate over all on-chain inputs
+
     let mut fetched_inputs = vec![];
     let mut decimals = vec![];
     for on_chain_data in data {
@@ -581,14 +786,15 @@ pub async fn read_on_chain_inputs<M: 'static + Middleware>(
         let contract_address = H160::from_slice(&contract_address_bytes);
         for (call_data, decimal) in &on_chain_data.call_data {
             let call_data_bytes = hex::decode(call_data.clone())?;
-            let tx: TypedTransaction = TransactionRequest::default()
+            let input: TransactionInput = call_data_bytes.into();
+
+            let tx = TransactionRequest::default()
                 .to(contract_address)
                 .from(address)
-                .data(call_data_bytes)
-                .into();
+                .input(input);
             debug!("transaction {:#?}", tx);
 
-            let result = client.call(&tx, None).await?;
+            let result = client.call(&tx).await?;
             debug!("return data {:#?}", result);
             fetched_inputs.push(result);
             decimals.push(*decimal);
@@ -599,67 +805,72 @@ pub async fn read_on_chain_inputs<M: 'static + Middleware>(
 
 ///
 #[cfg(not(target_arch = "wasm32"))]
-pub async fn evm_quantize<M: 'static + Middleware>(
+pub async fn evm_quantize<M: 'static + Provider<Http<Client>, Ethereum>>(
     client: Arc<M>,
     scales: Vec<crate::Scale>,
-    data: &(Vec<ethers::types::Bytes>, Vec<u8>),
+    data: &(Vec<Bytes>, Vec<u8>),
 ) -> Result<Vec<Fr>, Box<dyn Error>> {
-    // save the sol to a tmp file
-    let mut sol_path = std::env::temp_dir();
-    sol_path.push("quantizedata.sol");
-    std::fs::write(&sol_path, QUANTIZE_DATA_SOL)?;
-
-    let (abi, bytecode, runtime_bytecode) = get_contract_artifacts(sol_path, "QuantizeData", 0)?;
-    let factory = get_sol_contract_factory(abi, bytecode, runtime_bytecode, client.clone())?;
-
-    let contract = factory.deploy(())?.send().await?;
+    use alloy::primitives::ParseSignedError;
 
-    let contract = QuantizeData::new(contract.address(), client.clone());
+    let contract = QuantizeData::deploy(&client).await?;
 
     let fetched_inputs = data.0.clone();
     let decimals = data.1.clone();
 
     let fetched_inputs = fetched_inputs
         .iter()
-        .map(|x| Result::<_, std::convert::Infallible>::Ok(ethers::types::Bytes::from(x.to_vec())))
+        .map(|x| Result::<_, std::convert::Infallible>::Ok(Bytes::from(x.to_vec())))
         .collect::<Result<Vec<Bytes>, _>>()?;
 
     let decimals = decimals
         .iter()
-        .map(|x| U256::from_dec_str(&x.to_string()))
-        .collect::<Result<Vec<U256>, _>>()?;
+        .map(|x| Ok(I256::from_dec_str(&x.to_string())?.unsigned_abs()))
+        .collect::<Result<Vec<U256>, ParseSignedError>>()?;
 
     let scales = scales
         .iter()
-        .map(|x| U256::from_dec_str(&x.to_string()))
-        .collect::<Result<Vec<U256>, _>>()?;
+        .map(|x| Ok(I256::from_dec_str(&x.to_string())?.unsigned_abs()))
+        .collect::<Result<Vec<U256>, ParseSignedError>>()?;
 
-    info!("scales: {:#?}", scales);
-    info!("decimals: {:#?}", decimals);
-    info!("fetched_inputs: {:#?}", fetched_inputs);
+    debug!("scales: {:#?}", scales);
+    debug!("decimals: {:#?}", decimals);
+    debug!("fetched_inputs: {:#?}", fetched_inputs);
 
     let results = contract
         .quantize_data(fetched_inputs, decimals, scales)
         .call()
-        .await?;
-
-    let felts = contract.to_field_element(results.clone()).call().await?;
-    info!("evm quantization contract results: {:#?}", felts,);
+        .await?
+        .quantized_data;
+
+    debug!("evm quantization results: {:#?}", results);
+
+    let mut felts = vec![];
+
+    for x in results {
+        let felt = match i64::from_str(&x.to_string()) {
+            Ok(x) => contract.to_field_element(vec![x]).call().await?.output[0],
+            Err(_) => {
+                contract
+                    .check_is_valid_field_element(vec![x])
+                    .call()
+                    .await?
+                    .output[0]
+            }
+        };
+        felts.push(PrimeField::from_str_vartime(&felt.to_string()).unwrap());
+    }
 
-    let results = felts
-        .iter()
-        .map(|x| PrimeField::from_str_vartime(&x.to_string()).unwrap())
-        .collect::<Vec<Fr>>();
-    info!("evm quantization results: {:#?}", results,);
-    Ok(results.to_vec())
+    debug!("evm quantized felts: {:#?}", felts,);
+    Ok(felts)
 }
 
-/// Generates the contract factory for a solidity verifier, optionally compiling the code with optimizer runs set on the Solc compiler.
-fn get_sol_contract_factory<M: 'static + Middleware>(
-    abi: Contract,
+/// Generates the contract factory for a solidity verifier. The factory is used to deploy the contract
+fn get_sol_contract_factory<'a, M: 'static + Provider<Http<Client>, Ethereum>, T: TokenSeq<'a>>(
+    abi: JsonAbi,
     bytecode: Bytes,
     runtime_bytecode: Bytes,
     client: Arc<M>,
+    params: Option<T>,
 ) -> Result<ContractFactory<M>, Box<dyn Error>> {
     const MAX_RUNTIME_BYTECODE_SIZE: usize = 24577;
     let size = runtime_bytecode.len();
@@ -669,35 +880,76 @@ fn get_sol_contract_factory<M: 'static + Middleware>(
         warn!(
             "Solidity runtime bytecode size is: {:#?},
             which exceeds 24577 bytes spurious dragon limit.
-            Contract will fail to deploy on any chain with 
+            Contract will fail to deploy on any chain with
             EIP 140 enabled",
             size
         );
     }
-    Ok(ContractFactory::new(abi, bytecode, client))
+
+    // Encode the constructor args & concatenate with the bytecode if necessary
+    let data: Bytes = match (abi.constructor(), params.is_none()) {
+        (None, false) => {
+            return Err("Constructor arguments provided but no constructor found".into())
+        }
+        (None, true) => bytecode.clone(),
+        (Some(_), _) => {
+            let mut data = bytecode.to_vec();
+
+            if let Some(params) = params {
+                let params = alloy::sol_types::abi::encode_sequence(&params);
+                data.extend(params);
+            }
+            data.into()
+        }
+    };
+
+    Ok(CallBuilder::new_raw_deploy(client.clone(), data))
 }
 
 /// Compiles a solidity verifier contract and returns the abi, bytecode, and runtime bytecode
 #[cfg(not(target_arch = "wasm32"))]
-pub fn get_contract_artifacts(
+pub async fn get_contract_artifacts(
     sol_code_path: PathBuf,
     contract_name: &str,
     runs: usize,
-) -> Result<(Contract, Bytes, Bytes), Box<dyn Error>> {
+) -> Result<(JsonAbi, Bytes, Bytes), Box<dyn Error>> {
+    use foundry_compilers::{
+        artifacts::{output_selection::OutputSelection, Optimizer},
+        compilers::CompilerInput,
+        SolcInput, SHANGHAI_SOLC,
+    };
+
     if !sol_code_path.exists() {
-        return Err("sol_code_path does not exist".into());
+        return Err(format!("file not found: {:#?}", sol_code_path).into());
     }
-    // Create the compiler input, enabling the optimizer and setting the optimzer runs.
-    let input: CompilerInput = if runs > 0 {
-        let mut i = CompilerInput::new(sol_code_path)?[0]
-            .clone()
-            .optimizer(runs);
-        i.settings.optimizer.enable();
-        i
-    } else {
-        CompilerInput::new(sol_code_path)?[0].clone()
+
+    let mut settings = SolcSettings::default();
+    settings.optimizer = Optimizer {
+        enabled: Some(true),
+        runs: Some(runs),
+        details: None,
     };
-    let compiled = Solc::default().compile(&input)?;
+    settings.output_selection = OutputSelection::default_output_selection();
+
+    let input = SolcInput::build(
+        std::collections::BTreeMap::from([(
+            sol_code_path.clone(),
+            foundry_compilers::artifacts::Source::read(sol_code_path)?,
+        )]),
+        settings,
+        &SHANGHAI_SOLC,
+    );
+
+    let solc_opt = Solc::find_svm_installed_version(SHANGHAI_SOLC.to_string())?;
+    let solc = match solc_opt {
+        Some(solc) => solc,
+        None => {
+            info!("required solc version is missing ... installing");
+            Solc::install(&SHANGHAI_SOLC).await?
+        }
+    };
+
+    let compiled: foundry_compilers::CompilerOutput = solc.compile(&input[0])?;
 
     let (abi, bytecode, runtime_bytecode) = match compiled.find(contract_name) {
         Some(c) => c.into_parts_or_default(),
@@ -705,6 +957,7 @@ pub fn get_contract_artifacts(
             return Err("could not find contract".into());
         }
     };
+
     Ok((abi, bytecode, runtime_bytecode))
 }
 
@@ -715,15 +968,6 @@ pub fn fix_da_sol(
 ) -> Result<String, Box<dyn Error>> {
     let mut accounts_len = 0;
     let mut contract = ATTESTDATA_SOL.to_string();
-    let load_instances = LOADINSTANCES_SOL.to_string();
-    // replace the import statement with the load_instances contract, not including the
-    // `SPDX-License-Identifier: MIT pragma solidity ^0.8.20;` at the top of the file
-    contract = contract.replace(
-        "import './LoadInstances.sol';",
-        &load_instances[load_instances
-            .find("contract")
-            .ok_or("could not get load-instances contract")?..],
-    );
 
     // fill in the quantization params and total calls
     // as constants to the contract to save on gas
diff --git a/src/execute.rs b/src/execute.rs
index abbad9fbd..f50ea1fcd 100644
--- a/src/execute.rs
+++ b/src/execute.rs
@@ -21,6 +21,8 @@ use crate::pfsys::{
 };
 use crate::pfsys::{save_vk, srs::*};
 use crate::tensor::TensorError;
+#[cfg(not(target_arch = "wasm32"))]
+use crate::EZKL_BUF_CAPACITY;
 use crate::{Commitments, RunArgs};
 #[cfg(not(target_arch = "wasm32"))]
 use colored::Colorize;
@@ -64,49 +66,16 @@ use snark_verifier::system::halo2::Config;
 use std::error::Error;
 use std::fs::File;
 #[cfg(not(target_arch = "wasm32"))]
+use std::io::BufWriter;
+#[cfg(not(target_arch = "wasm32"))]
 use std::io::{Cursor, Write};
 use std::path::Path;
 use std::path::PathBuf;
-#[cfg(not(target_arch = "wasm32"))]
-use std::process::Command;
 use std::str::FromStr;
-#[cfg(not(target_arch = "wasm32"))]
-use std::sync::OnceLock;
-
-#[cfg(not(target_arch = "wasm32"))]
-use crate::EZKL_BUF_CAPACITY;
-#[cfg(not(target_arch = "wasm32"))]
-use std::io::BufWriter;
 use std::time::Duration;
 use tabled::Tabled;
 use thiserror::Error;
 
-#[cfg(not(target_arch = "wasm32"))]
-static _SOLC_REQUIREMENT: OnceLock<bool> = OnceLock::new();
-#[cfg(not(target_arch = "wasm32"))]
-fn check_solc_requirement() {
-    info!("checking solc installation..");
-    _SOLC_REQUIREMENT.get_or_init(|| match Command::new("solc").arg("--version").output() {
-        Ok(output) => {
-            debug!("solc output: {:#?}", output);
-            debug!("solc output success: {:#?}", output.status.success());
-            if !output.status.success() {
-                log::error!(
-                    "`solc` check failed: {}",
-                    String::from_utf8_lossy(&output.stderr)
-                );
-                return false;
-            }
-            debug!("solc check passed, proceeding");
-            true
-        }
-        Err(_) => {
-            log::error!("`solc` check failed: solc not found");
-            false
-        }
-    });
-}
-
 use lazy_static::lazy_static;
 
 lazy_static! {
@@ -195,6 +164,7 @@ pub async fn run(command: Commands) -> Result<String, Box<dyn Error>> {
             only_range_check_rebase.unwrap_or(DEFAULT_ONLY_RANGE_CHECK_REBASE.parse()?),
             max_logrows,
         )
+        .await
         .map(|e| serde_json::to_string(&e).unwrap()),
         Commands::GenWitness {
             data,
@@ -209,6 +179,7 @@ pub async fn run(command: Commands) -> Result<String, Box<dyn Error>> {
             vk_path,
             srs_path,
         )
+        .await
         .map(|e| serde_json::to_string(&e).unwrap()),
         Commands::Mock { model, witness } => mock(
             model.unwrap_or(DEFAULT_MODEL.into()),
@@ -222,39 +193,48 @@ pub async fn run(command: Commands) -> Result<String, Box<dyn Error>> {
             sol_code_path,
             abi_path,
             render_vk_seperately,
-        } => create_evm_verifier(
-            vk_path.unwrap_or(DEFAULT_VK.into()),
-            srs_path,
-            settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
-            sol_code_path.unwrap_or(DEFAULT_SOL_CODE.into()),
-            abi_path.unwrap_or(DEFAULT_VERIFIER_ABI.into()),
-            render_vk_seperately.unwrap_or(DEFAULT_RENDER_VK_SEPERATELY.parse()?),
-        ),
+        } => {
+            create_evm_verifier(
+                vk_path.unwrap_or(DEFAULT_VK.into()),
+                srs_path,
+                settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
+                sol_code_path.unwrap_or(DEFAULT_SOL_CODE.into()),
+                abi_path.unwrap_or(DEFAULT_VERIFIER_ABI.into()),
+                render_vk_seperately.unwrap_or(DEFAULT_RENDER_VK_SEPERATELY.parse()?),
+            )
+            .await
+        }
         Commands::CreateEvmVK {
             vk_path,
             srs_path,
             settings_path,
             sol_code_path,
             abi_path,
-        } => create_evm_vk(
-            vk_path.unwrap_or(DEFAULT_VK.into()),
-            srs_path,
-            settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
-            sol_code_path.unwrap_or(DEFAULT_VK_SOL.into()),
-            abi_path.unwrap_or(DEFAULT_VK_ABI.into()),
-        ),
+        } => {
+            create_evm_vk(
+                vk_path.unwrap_or(DEFAULT_VK.into()),
+                srs_path,
+                settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
+                sol_code_path.unwrap_or(DEFAULT_VK_SOL.into()),
+                abi_path.unwrap_or(DEFAULT_VK_ABI.into()),
+            )
+            .await
+        }
         #[cfg(not(target_arch = "wasm32"))]
         Commands::CreateEvmDataAttestation {
             settings_path,
             sol_code_path,
             abi_path,
             data,
-        } => create_evm_data_attestation(
-            settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
-            sol_code_path.unwrap_or(DEFAULT_SOL_CODE_DA.into()),
-            abi_path.unwrap_or(DEFAULT_VERIFIER_DA_ABI.into()),
-            data.unwrap_or(DEFAULT_DATA.into()),
-        ),
+        } => {
+            create_evm_data_attestation(
+                settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
+                sol_code_path.unwrap_or(DEFAULT_SOL_CODE_DA.into()),
+                abi_path.unwrap_or(DEFAULT_VERIFIER_DA_ABI.into()),
+                data.unwrap_or(DEFAULT_DATA.into()),
+            )
+            .await
+        }
         #[cfg(not(target_arch = "wasm32"))]
         Commands::CreateEvmVerifierAggr {
             vk_path,
@@ -264,15 +244,18 @@ pub async fn run(command: Commands) -> Result<String, Box<dyn Error>> {
             aggregation_settings,
             logrows,
             render_vk_seperately,
-        } => create_evm_aggregate_verifier(
-            vk_path.unwrap_or(DEFAULT_VK.into()),
-            srs_path,
-            sol_code_path.unwrap_or(DEFAULT_SOL_CODE_AGGREGATED.into()),
-            abi_path.unwrap_or(DEFAULT_VERIFIER_AGGREGATED_ABI.into()),
-            aggregation_settings,
-            logrows.unwrap_or(DEFAULT_AGGREGATED_LOGROWS.parse()?),
-            render_vk_seperately.unwrap_or(DEFAULT_RENDER_VK_SEPERATELY.parse()?),
-        ),
+        } => {
+            create_evm_aggregate_verifier(
+                vk_path.unwrap_or(DEFAULT_VK.into()),
+                srs_path,
+                sol_code_path.unwrap_or(DEFAULT_SOL_CODE_AGGREGATED.into()),
+                abi_path.unwrap_or(DEFAULT_VERIFIER_AGGREGATED_ABI.into()),
+                aggregation_settings,
+                logrows.unwrap_or(DEFAULT_AGGREGATED_LOGROWS.parse()?),
+                render_vk_seperately.unwrap_or(DEFAULT_RENDER_VK_SEPERATELY.parse()?),
+            )
+            .await
+        }
         Commands::CompileCircuit {
             model,
             compiled_circuit,
@@ -690,7 +673,7 @@ pub(crate) fn table(model: PathBuf, run_args: RunArgs) -> Result<String, Box<dyn
     Ok(String::new())
 }
 
-pub(crate) fn gen_witness(
+pub(crate) async fn gen_witness(
     compiled_circuit_path: PathBuf,
     data: PathBuf,
     output: Option<PathBuf>,
@@ -713,7 +696,7 @@ pub(crate) fn gen_witness(
     };
 
     #[cfg(not(target_arch = "wasm32"))]
-    let mut input = circuit.load_graph_input(&data)?;
+    let mut input = circuit.load_graph_input(&data).await?;
     #[cfg(target_arch = "wasm32")]
     let mut input = circuit.load_graph_input(&data)?;
 
@@ -944,7 +927,7 @@ impl AccuracyResults {
 #[cfg(not(target_arch = "wasm32"))]
 #[allow(trivial_casts)]
 #[allow(clippy::too_many_arguments)]
-pub(crate) fn calibrate(
+pub(crate) async fn calibrate(
     model_path: PathBuf,
     data: PathBuf,
     settings_path: PathBuf,
@@ -967,7 +950,9 @@ pub(crate) fn calibrate(
 
     let model = Model::from_run_args(&settings.run_args, &model_path)?;
 
-    let chunks = data.split_into_batches(model.graph.input_shapes()?)?;
+    let input_shapes = model.graph.input_shapes()?;
+
+    let chunks = data.split_into_batches(input_shapes).await?;
     info!("num calibration batches: {}", chunks.len());
 
     debug!("running onnx predictions...");
@@ -1350,7 +1335,7 @@ pub(crate) fn mock(
 }
 
 #[cfg(not(target_arch = "wasm32"))]
-pub(crate) fn create_evm_verifier(
+pub(crate) async fn create_evm_verifier(
     vk_path: PathBuf,
     srs_path: Option<PathBuf>,
     settings_path: PathBuf,
@@ -1358,8 +1343,6 @@ pub(crate) fn create_evm_verifier(
     abi_path: PathBuf,
     render_vk_seperately: bool,
 ) -> Result<String, Box<dyn Error>> {
-    check_solc_requirement();
-
     let settings = GraphSettings::load(&settings_path)?;
     let commitment: Commitments = settings.run_args.commitment.into();
     let params = load_params_verifier::<KZGCommitmentScheme<Bn256>>(
@@ -1389,7 +1372,7 @@ pub(crate) fn create_evm_verifier(
     File::create(sol_code_path.clone())?.write_all(verifier_solidity.as_bytes())?;
 
     // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2Verifier", 0)?;
+    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2Verifier", 0).await?;
     // save abi to file
     serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
 
@@ -1397,14 +1380,13 @@ pub(crate) fn create_evm_verifier(
 }
 
 #[cfg(not(target_arch = "wasm32"))]
-pub(crate) fn create_evm_vk(
+pub(crate) async fn create_evm_vk(
     vk_path: PathBuf,
     srs_path: Option<PathBuf>,
     settings_path: PathBuf,
     sol_code_path: PathBuf,
     abi_path: PathBuf,
 ) -> Result<String, Box<dyn Error>> {
-    check_solc_requirement();
     let settings = GraphSettings::load(&settings_path)?;
     let commitment: Commitments = settings.run_args.commitment.into();
     let params = load_params_verifier::<KZGCommitmentScheme<Bn256>>(
@@ -1431,7 +1413,7 @@ pub(crate) fn create_evm_vk(
     File::create(sol_code_path.clone())?.write_all(vk_solidity.as_bytes())?;
 
     // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2VerifyingKey", 0)?;
+    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2VerifyingKey", 0).await?;
     // save abi to file
     serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
 
@@ -1439,7 +1421,7 @@ pub(crate) fn create_evm_vk(
 }
 
 #[cfg(not(target_arch = "wasm32"))]
-pub(crate) fn create_evm_data_attestation(
+pub(crate) async fn create_evm_data_attestation(
     settings_path: PathBuf,
     _sol_code_path: PathBuf,
     _abi_path: PathBuf,
@@ -1447,7 +1429,6 @@ pub(crate) fn create_evm_data_attestation(
 ) -> Result<String, Box<dyn Error>> {
     #[allow(unused_imports)]
     use crate::graph::{DataSource, VarVisibility};
-    check_solc_requirement();
 
     let settings = GraphSettings::load(&settings_path)?;
 
@@ -1487,7 +1468,7 @@ pub(crate) fn create_evm_data_attestation(
         let mut f = File::create(_sol_code_path.clone())?;
         let _ = f.write(output.as_bytes());
         // fetch abi of the contract
-        let (abi, _, _) = get_contract_artifacts(_sol_code_path, "DataAttestation", 0)?;
+        let (abi, _, _) = get_contract_artifacts(_sol_code_path, "DataAttestation", 0).await?;
         // save abi to file
         serde_json::to_writer(std::fs::File::create(_abi_path)?, &abi)?;
     } else {
@@ -1508,7 +1489,6 @@ pub(crate) async fn deploy_da_evm(
     runs: usize,
     private_key: Option<String>,
 ) -> Result<String, Box<dyn Error>> {
-    check_solc_requirement();
     let contract_address = deploy_da_verifier_via_solidity(
         settings_path,
         data,
@@ -1535,7 +1515,6 @@ pub(crate) async fn deploy_evm(
     private_key: Option<String>,
     contract_name: &str,
 ) -> Result<String, Box<dyn Error>> {
-    check_solc_requirement();
     let contract_address = deploy_contract_via_solidity(
         sol_code_path,
         rpc_url.as_deref(),
@@ -1561,7 +1540,6 @@ pub(crate) async fn verify_evm(
     addr_vk: Option<H160Flag>,
 ) -> Result<String, Box<dyn Error>> {
     use crate::eth::verify_proof_with_data_attestation;
-    check_solc_requirement();
 
     let proof = Snark::load::<KZGCommitmentScheme<Bn256>>(&proof_path)?;
 
@@ -1594,7 +1572,7 @@ pub(crate) async fn verify_evm(
 }
 
 #[cfg(not(target_arch = "wasm32"))]
-pub(crate) fn create_evm_aggregate_verifier(
+pub(crate) async fn create_evm_aggregate_verifier(
     vk_path: PathBuf,
     srs_path: Option<PathBuf>,
     sol_code_path: PathBuf,
@@ -1603,7 +1581,6 @@ pub(crate) fn create_evm_aggregate_verifier(
     logrows: u32,
     render_vk_seperately: bool,
 ) -> Result<String, Box<dyn Error>> {
-    check_solc_requirement();
     let srs_path = get_srs_path(logrows, srs_path, Commitments::KZG);
     let params: ParamsKZG<Bn256> = load_srs_verifier::<KZGCommitmentScheme<Bn256>>(srs_path)?;
 
@@ -1649,7 +1626,7 @@ pub(crate) fn create_evm_aggregate_verifier(
     File::create(sol_code_path.clone())?.write_all(verifier_solidity.as_bytes())?;
 
     // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2Verifier", 0)?;
+    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2Verifier", 0).await?;
     // save abi to file
     serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
 
@@ -1729,7 +1706,6 @@ pub(crate) async fn setup_test_evm_witness(
 ) -> Result<String, Box<dyn Error>> {
     use crate::graph::TestOnChainData;
 
-    info!("run this command in background to keep the instance running for testing");
     let mut data = GraphData::from_path(data_path)?;
     let mut circuit = GraphCircuit::load(compiled_circuit_path)?;
 
@@ -1765,7 +1741,6 @@ pub(crate) async fn test_update_account_calls(
 ) -> Result<String, Box<dyn Error>> {
     use crate::eth::update_account_calls;
 
-    check_solc_requirement();
     update_account_calls(addr.into(), data, rpc_url.as_deref()).await?;
 
     Ok(String::new())
diff --git a/src/graph/input.rs b/src/graph/input.rs
index ff4ade430..c17f42f79 100644
--- a/src/graph/input.rs
+++ b/src/graph/input.rs
@@ -3,11 +3,11 @@ use super::GraphError;
 use crate::circuit::InputType;
 use crate::fieldutils::i64_to_felt;
 #[cfg(not(target_arch = "wasm32"))]
+use crate::graph::postgres::Client;
+#[cfg(not(target_arch = "wasm32"))]
 use crate::tensor::Tensor;
 use crate::EZKL_BUF_CAPACITY;
 use halo2curves::bn256::Fr as Fp;
-#[cfg(not(target_arch = "wasm32"))]
-use postgres::{Client, NoTls};
 #[cfg(feature = "python-bindings")]
 use pyo3::prelude::*;
 #[cfg(feature = "python-bindings")]
@@ -211,7 +211,9 @@ impl PostgresSource {
     }
 
     /// Fetch data from postgres
-    pub fn fetch(&self) -> Result<Vec<Vec<pg_bigdecimal::PgNumeric>>, Box<dyn std::error::Error>> {
+    pub async fn fetch(
+        &self,
+    ) -> Result<Vec<Vec<pg_bigdecimal::PgNumeric>>, Box<dyn std::error::Error>> {
         // clone to move into thread
         let user = self.user.clone();
         let host = self.host.clone();
@@ -232,10 +234,10 @@ impl PostgresSource {
             )
         };
 
-        let mut client = Client::connect(&config, NoTls)?;
+        let mut client = Client::connect(&config).await?;
         let mut res: Vec<pg_bigdecimal::PgNumeric> = Vec::new();
         // extract rows from query
-        for row in client.query(&query, &[])? {
+        for row in client.query(&query, &[]).await? {
             // extract features from row
             for i in 0..row.len() {
                 res.push(row.get(i));
@@ -245,11 +247,12 @@ impl PostgresSource {
     }
 
     /// Fetch data from postgres and format it as a FileSource
-    pub fn fetch_and_format_as_file(
+    pub async fn fetch_and_format_as_file(
         &self,
     ) -> Result<Vec<Vec<FileSourceInner>>, Box<dyn std::error::Error>> {
         Ok(self
-            .fetch()?
+            .fetch()
+            .await?
             .iter()
             .map(|d| {
                 d.iter()
@@ -277,13 +280,13 @@ impl OnChainSource {
         mut shapes: Vec<Vec<usize>>,
         rpc: Option<&str>,
     ) -> Result<(Vec<Tensor<Fp>>, Self), Box<dyn std::error::Error>> {
-        use crate::eth::{evm_quantize, read_on_chain_inputs, test_on_chain_data};
+        use crate::eth::{
+            evm_quantize, read_on_chain_inputs, test_on_chain_data, DEFAULT_ANVIL_ENDPOINT,
+        };
         use log::debug;
 
         // Set up local anvil instance for reading on-chain data
-        let (anvil, client) = crate::eth::setup_eth_backend(rpc, None).await?;
-
-        let address = client.address();
+        let (client, client_address) = crate::eth::setup_eth_backend(rpc, None).await?;
 
         let mut scales = scales;
         // set scales to 1 where data is a field element
@@ -296,7 +299,8 @@ impl OnChainSource {
 
         let calls_to_accounts = test_on_chain_data(client.clone(), data).await?;
         debug!("Calls to accounts: {:?}", calls_to_accounts);
-        let inputs = read_on_chain_inputs(client.clone(), address, &calls_to_accounts).await?;
+        let inputs =
+            read_on_chain_inputs(client.clone(), client_address, &calls_to_accounts).await?;
         debug!("Inputs: {:?}", inputs);
 
         let mut quantized_evm_inputs = vec![];
@@ -325,7 +329,7 @@ impl OnChainSource {
             inputs.push(t);
         }
 
-        let used_rpc = rpc.unwrap_or(&anvil.endpoint()).to_string();
+        let used_rpc = rpc.unwrap_or(DEFAULT_ANVIL_ENDPOINT).to_string();
 
         // Fill the input_data field of the GraphData struct
         Ok((
@@ -502,7 +506,7 @@ impl GraphData {
     }
 
     ///
-    pub fn split_into_batches(
+    pub async fn split_into_batches(
         &self,
         input_shapes: Vec<Vec<usize>>,
     ) -> Result<Vec<Self>, Box<dyn std::error::Error>> {
@@ -527,7 +531,7 @@ impl GraphData {
             GraphData {
                 input_data: DataSource::DB(data),
                 output_data: _,
-            } => data.fetch_and_format_as_file()?,
+            } => data.fetch_and_format_as_file().await?,
         };
 
         for (i, shape) in input_shapes.iter().enumerate() {
diff --git a/src/graph/mod.rs b/src/graph/mod.rs
index 495dcc5ea..9c875bc8c 100644
--- a/src/graph/mod.rs
+++ b/src/graph/mod.rs
@@ -6,10 +6,14 @@ pub mod model;
 pub mod modules;
 /// Inner elements of a computational graph that represent a single operation / constraints.
 pub mod node;
+/// postgres helper functions
+#[cfg(not(target_arch = "wasm32"))]
+pub mod postgres;
 /// Helper functions
 pub mod utilities;
 /// Representations of a computational graph's variables.
 pub mod vars;
+
 #[cfg(not(target_arch = "wasm32"))]
 use colored_json::ToColoredJson;
 #[cfg(unix)]
@@ -958,7 +962,7 @@ impl GraphCircuit {
 
     ///
     #[cfg(not(target_arch = "wasm32"))]
-    pub fn load_graph_input(
+    pub async fn load_graph_input(
         &mut self,
         data: &GraphData,
     ) -> Result<Vec<Tensor<Fp>>, Box<dyn std::error::Error>> {
@@ -968,6 +972,7 @@ impl GraphCircuit {
         debug!("input scales: {:?}", scales);
 
         self.process_data_source(&data.input_data, shapes, scales, input_types)
+            .await
     }
 
     #[cfg(target_arch = "wasm32")]
@@ -991,7 +996,7 @@ impl GraphCircuit {
 
     #[cfg(not(target_arch = "wasm32"))]
     /// Process the data source for the model
-    fn process_data_source(
+    async fn process_data_source(
         &mut self,
         data: &DataSource,
         shapes: Vec<Vec<usize>>,
@@ -1005,21 +1010,14 @@ impl GraphCircuit {
                     per_item_scale.extend(vec![scales[i]; shape.iter().product::<usize>()]);
                 }
 
-                // start runtime and fetch data
-                let runtime = tokio::runtime::Builder::new_current_thread()
-                    .enable_all()
-                    .build()?;
-
-                runtime.block_on(async {
-                    self.load_on_chain_data(source.clone(), &shapes, per_item_scale)
-                        .await
-                })
+                self.load_on_chain_data(source.clone(), &shapes, per_item_scale)
+                    .await
             }
             DataSource::File(file_data) => {
                 self.load_file_data(file_data, &shapes, scales, input_types)
             }
             DataSource::DB(pg) => {
-                let data = pg.fetch_and_format_as_file()?;
+                let data = pg.fetch_and_format_as_file().await?;
                 self.load_file_data(&data, &shapes, scales, input_types)
             }
         }
@@ -1034,8 +1032,8 @@ impl GraphCircuit {
         scales: Vec<crate::Scale>,
     ) -> Result<Vec<Tensor<Fp>>, Box<dyn std::error::Error>> {
         use crate::eth::{evm_quantize, read_on_chain_inputs, setup_eth_backend};
-        let (_, client) = setup_eth_backend(Some(&source.rpc), None).await?;
-        let inputs = read_on_chain_inputs(client.clone(), client.address(), &source.calls).await?;
+        let (client, client_address) = setup_eth_backend(Some(&source.rpc), None).await?;
+        let inputs = read_on_chain_inputs(client.clone(), client_address, &source.calls).await?;
         // quantize the supplied data using the provided scale + QuantizeData.sol
         let quantized_evm_inputs = evm_quantize(client, scales, &inputs).await?;
         // on-chain data has already been quantized at this point. Just need to reshape it and push into tensor vector
diff --git a/src/graph/postgres.rs b/src/graph/postgres.rs
new file mode 100644
index 000000000..a9be54528
--- /dev/null
+++ b/src/graph/postgres.rs
@@ -0,0 +1,492 @@
+use log::{debug, error, info};
+use std::fmt::Debug;
+use std::net::IpAddr;
+use std::path::Path;
+use std::str::FromStr;
+use std::sync::Arc;
+use std::time::Duration;
+use std::{fmt, pin::Pin};
+use tokio::task::JoinHandle;
+#[doc(inline)]
+pub use tokio_postgres::config::{
+    ChannelBinding, Host, LoadBalanceHosts, SslMode, TargetSessionAttrs,
+};
+use tokio_postgres::tls::NoTlsStream;
+use tokio_postgres::NoTls;
+use tokio_postgres::{error::DbError, types::ToSql, Error, Row, Socket, ToStatement};
+
+/// Connection configuration.
+///
+/// Configuration can be parsed from libpq-style connection strings. These strings come in two formats:
+///
+///
+#[derive(Clone)]
+pub struct Config {
+    config: tokio_postgres::Config,
+    notice_callback: Arc<dyn Fn(DbError) + Send + Sync>,
+}
+
+impl fmt::Debug for Config {
+    fn fmt(&self, fmt: &mut fmt::Formatter<'_>) -> fmt::Result {
+        fmt.debug_struct("Config")
+            .field("config", &self.config)
+            .finish()
+    }
+}
+
+impl Default for Config {
+    fn default() -> Config {
+        Config::new()
+    }
+}
+
+impl Config {
+    /// Creates a new configuration.
+    pub fn new() -> Config {
+        tokio_postgres::Config::new().into()
+    }
+
+    /// Sets the user to authenticate with.
+    ///
+    /// If the user is not set, then this defaults to the user executing this process.
+    pub fn user(&mut self, user: &str) -> &mut Config {
+        self.config.user(user);
+        self
+    }
+
+    /// Gets the user to authenticate with, if one has been configured with
+    /// the `user` method.
+    pub fn get_user(&self) -> Option<&str> {
+        self.config.get_user()
+    }
+
+    /// Sets the password to authenticate with.
+    pub fn password<T>(&mut self, password: T) -> &mut Config
+    where
+        T: AsRef<[u8]>,
+    {
+        self.config.password(password);
+        self
+    }
+
+    /// Gets the password to authenticate with, if one has been configured with
+    /// the `password` method.
+    pub fn get_password(&self) -> Option<&[u8]> {
+        self.config.get_password()
+    }
+
+    /// Sets the name of the database to connect to.
+    ///
+    /// Defaults to the user.
+    pub fn dbname(&mut self, dbname: &str) -> &mut Config {
+        self.config.dbname(dbname);
+        self
+    }
+
+    /// Gets the name of the database to connect to, if one has been configured
+    /// with the `dbname` method.
+    pub fn get_dbname(&self) -> Option<&str> {
+        self.config.get_dbname()
+    }
+
+    /// Sets command line options used to configure the server.
+    pub fn options(&mut self, options: &str) -> &mut Config {
+        self.config.options(options);
+        self
+    }
+
+    /// Gets the command line options used to configure the server, if the
+    /// options have been set with the `options` method.
+    pub fn get_options(&self) -> Option<&str> {
+        self.config.get_options()
+    }
+
+    /// Sets the value of the `application_name` runtime parameter.
+    pub fn application_name(&mut self, application_name: &str) -> &mut Config {
+        self.config.application_name(application_name);
+        self
+    }
+
+    /// Gets the value of the `application_name` runtime parameter, if it has
+    /// been set with the `application_name` method.
+    pub fn get_application_name(&self) -> Option<&str> {
+        self.config.get_application_name()
+    }
+
+    /// Sets the SSL configuration.
+    ///
+    /// Defaults to `prefer`.
+    pub fn ssl_mode(&mut self, ssl_mode: SslMode) -> &mut Config {
+        self.config.ssl_mode(ssl_mode);
+        self
+    }
+
+    /// Gets the SSL configuration.
+    pub fn get_ssl_mode(&self) -> SslMode {
+        self.config.get_ssl_mode()
+    }
+
+    /// Adds a host to the configuration.
+    ///
+    /// Multiple hosts can be specified by calling this method multiple times, and each will be tried in order. On Unix
+    /// systems, a host starting with a `/` is interpreted as a path to a directory containing Unix domain sockets.
+    /// There must be either no hosts, or the same number of hosts as hostaddrs.
+    pub fn host(&mut self, host: &str) -> &mut Config {
+        self.config.host(host);
+        self
+    }
+
+    /// Gets the hosts that have been added to the configuration with `host`.
+    pub fn get_hosts(&self) -> &[Host] {
+        self.config.get_hosts()
+    }
+
+    /// Gets the hostaddrs that have been added to the configuration with `hostaddr`.
+    pub fn get_hostaddrs(&self) -> &[IpAddr] {
+        self.config.get_hostaddrs()
+    }
+
+    /// Adds a Unix socket host to the configuration.
+    ///
+    /// Unlike `host`, this method allows non-UTF8 paths.
+    #[cfg(unix)]
+    pub fn host_path<T>(&mut self, host: T) -> &mut Config
+    where
+        T: AsRef<Path>,
+    {
+        self.config.host_path(host);
+        self
+    }
+
+    /// Adds a hostaddr to the configuration.
+    ///
+    /// Multiple hostaddrs can be specified by calling this method multiple times, and each will be tried in order.
+    /// There must be either no hostaddrs, or the same number of hostaddrs as hosts.
+    pub fn hostaddr(&mut self, hostaddr: IpAddr) -> &mut Config {
+        self.config.hostaddr(hostaddr);
+        self
+    }
+
+    /// Adds a port to the configuration.
+    ///
+    /// Multiple ports can be specified by calling this method multiple times. There must either be no ports, in which
+    /// case the default of 5432 is used, a single port, in which it is used for all hosts, or the same number of ports
+    /// as hosts.
+    pub fn port(&mut self, port: u16) -> &mut Config {
+        self.config.port(port);
+        self
+    }
+
+    /// Gets the ports that have been added to the configuration with `port`.
+    pub fn get_ports(&self) -> &[u16] {
+        self.config.get_ports()
+    }
+
+    /// Sets the timeout applied to socket-level connection attempts.
+    ///
+    /// Note that hostnames can resolve to multiple IP addresses, and this timeout will apply to each address of each
+    /// host separately. Defaults to no limit.
+    pub fn connect_timeout(&mut self, connect_timeout: Duration) -> &mut Config {
+        self.config.connect_timeout(connect_timeout);
+        self
+    }
+
+    /// Gets the connection timeout, if one has been set with the
+    /// `connect_timeout` method.
+    pub fn get_connect_timeout(&self) -> Option<&Duration> {
+        self.config.get_connect_timeout()
+    }
+
+    /// Sets the TCP user timeout.
+    ///
+    /// This is ignored for Unix domain socket connections. It is only supported on systems where
+    /// TCP_USER_TIMEOUT is available and will default to the system default if omitted or set to 0;
+    /// on other systems, it has no effect.
+    pub fn tcp_user_timeout(&mut self, tcp_user_timeout: Duration) -> &mut Config {
+        self.config.tcp_user_timeout(tcp_user_timeout);
+        self
+    }
+
+    /// Gets the TCP user timeout, if one has been set with the
+    /// `user_timeout` method.
+    pub fn get_tcp_user_timeout(&self) -> Option<&Duration> {
+        self.config.get_tcp_user_timeout()
+    }
+
+    /// Controls the use of TCP keepalive.
+    ///
+    /// This is ignored for Unix domain socket connections. Defaults to `true`.
+    pub fn keepalives(&mut self, keepalives: bool) -> &mut Config {
+        self.config.keepalives(keepalives);
+        self
+    }
+
+    /// Reports whether TCP keepalives will be used.
+    pub fn get_keepalives(&self) -> bool {
+        self.config.get_keepalives()
+    }
+
+    /// Sets the amount of idle time before a keepalive packet is sent on the connection.
+    ///
+    /// This is ignored for Unix domain sockets, or if the `keepalives` option is disabled. Defaults to 2 hours.
+    pub fn keepalives_idle(&mut self, keepalives_idle: Duration) -> &mut Config {
+        self.config.keepalives_idle(keepalives_idle);
+        self
+    }
+
+    /// Gets the configured amount of idle time before a keepalive packet will
+    /// be sent on the connection.
+    pub fn get_keepalives_idle(&self) -> Duration {
+        self.config.get_keepalives_idle()
+    }
+
+    /// Sets the time interval between TCP keepalive probes.
+    /// On Windows, this sets the value of the tcp_keepalive struct’s keepaliveinterval field.
+    ///
+    /// This is ignored for Unix domain sockets, or if the `keepalives` option is disabled.
+    pub fn keepalives_interval(&mut self, keepalives_interval: Duration) -> &mut Config {
+        self.config.keepalives_interval(keepalives_interval);
+        self
+    }
+
+    /// Gets the time interval between TCP keepalive probes.
+    pub fn get_keepalives_interval(&self) -> Option<Duration> {
+        self.config.get_keepalives_interval()
+    }
+
+    /// Sets the maximum number of TCP keepalive probes that will be sent before dropping a connection.
+    ///
+    /// This is ignored for Unix domain sockets, or if the `keepalives` option is disabled.
+    pub fn keepalives_retries(&mut self, keepalives_retries: u32) -> &mut Config {
+        self.config.keepalives_retries(keepalives_retries);
+        self
+    }
+
+    /// Gets the maximum number of TCP keepalive probes that will be sent before dropping a connection.
+    pub fn get_keepalives_retries(&self) -> Option<u32> {
+        self.config.get_keepalives_retries()
+    }
+
+    /// Sets the requirements of the session.
+    ///
+    /// This can be used to connect to the primary server in a clustered database rather than one of the read-only
+    /// secondary servers. Defaults to `Any`.
+    pub fn target_session_attrs(
+        &mut self,
+        target_session_attrs: TargetSessionAttrs,
+    ) -> &mut Config {
+        self.config.target_session_attrs(target_session_attrs);
+        self
+    }
+
+    /// Gets the requirements of the session.
+    pub fn get_target_session_attrs(&self) -> TargetSessionAttrs {
+        self.config.get_target_session_attrs()
+    }
+
+    /// Sets the channel binding behavior.
+    ///
+    /// Defaults to `prefer`.
+    pub fn channel_binding(&mut self, channel_binding: ChannelBinding) -> &mut Config {
+        self.config.channel_binding(channel_binding);
+        self
+    }
+
+    /// Gets the channel binding behavior.
+    pub fn get_channel_binding(&self) -> ChannelBinding {
+        self.config.get_channel_binding()
+    }
+
+    /// Sets the host load balancing behavior.
+    ///
+    /// Defaults to `disable`.
+    pub fn load_balance_hosts(&mut self, load_balance_hosts: LoadBalanceHosts) -> &mut Config {
+        self.config.load_balance_hosts(load_balance_hosts);
+        self
+    }
+
+    /// Gets the host load balancing behavior.
+    pub fn get_load_balance_hosts(&self) -> LoadBalanceHosts {
+        self.config.get_load_balance_hosts()
+    }
+
+    /// Sets the notice callback.
+    ///
+    /// This callback will be invoked with the contents of every
+    /// [`AsyncMessage::Notice`] that is received by the connection. Notices use
+    /// the same structure as errors, but they are not "errors" per-se.
+    ///
+    /// Notices are distinct from notifications, which are instead accessible
+    /// via the [`Notifications`] API.
+    ///
+    /// [`AsyncMessage::Notice`]: tokio_postgres::AsyncMessage::Notice
+    /// [`Notifications`]: crate::Notifications
+    pub fn notice_callback<F>(&mut self, f: F) -> &mut Config
+    where
+        F: Fn(DbError) + Send + Sync + 'static,
+    {
+        self.notice_callback = Arc::new(f);
+        self
+    }
+
+    /// Opens a connection to a PostgreSQL database.
+    pub async fn connect(&self) -> Result<Client, Error> {
+        let (client, connection) = self.config.connect(NoTls).await?;
+
+        let connection = Connection::new(connection);
+
+        Ok(Client::new(client, connection))
+    }
+}
+
+impl FromStr for Config {
+    type Err = Error;
+
+    fn from_str(s: &str) -> Result<Config, Error> {
+        s.parse::<tokio_postgres::Config>().map(Config::from)
+    }
+}
+
+impl From<tokio_postgres::Config> for Config {
+    fn from(config: tokio_postgres::Config) -> Config {
+        Config {
+            config,
+            notice_callback: Arc::new(|notice| {
+                info!("{}: {}", notice.severity(), notice.message())
+            }),
+        }
+    }
+}
+
+#[allow(missing_debug_implementations, dead_code)]
+/// An asynchronous PostgreSQL connection. We use this to keep the connection alive / keep it pinned so that it doesn't
+/// get dropped.
+pub struct Connection {
+    /// The underlying connection stream.
+    connection: Pin<Box<tokio_postgres::Connection<Socket, NoTlsStream>>>,
+}
+
+impl Connection {
+    /// Creates a new connection.
+    pub fn new(connection: tokio_postgres::Connection<Socket, NoTlsStream>) -> Self {
+        Connection {
+            connection: Box::pin(connection),
+        }
+    }
+
+    /// start the connection
+    pub async fn start(self) {
+        if let Err(e) = self.connection.await {
+            error!("connection error: {}", e);
+        }
+    }
+}
+
+#[allow(missing_debug_implementations, dead_code)]
+/// An asynchronous PostgreSQL client.
+pub struct Client {
+    connection: JoinHandle<()>,
+    client: tokio_postgres::Client,
+}
+
+impl Drop for Client {
+    fn drop(&mut self) {
+        let _ = self.close_inner();
+    }
+}
+
+impl Client {
+    pub(crate) fn new(client: tokio_postgres::Client, connection: Connection) -> Client {
+        // The connection object performs the actual communication with the database,
+        // so spawn it off to run on its own.
+        let thread = tokio::spawn(async move {
+            connection.start().await;
+        });
+
+        Client {
+            client,
+            connection: thread,
+        }
+    }
+
+    /// A convenience function which parses a configuration string into a `Config` and then connects to the database.
+    ///
+    /// See the documentation for [`Config`] for information about the connection syntax.
+    ///
+    /// [`Config`]: config/struct.Config.html
+    pub async fn connect(params: &str) -> Result<Client, Error> {
+        debug!("Connecting to database with params: {}", params);
+        params.parse::<Config>()?.connect().await
+    }
+
+    /// Returns a new `Config` object which can be used to configure and connect to a database.
+    pub fn configure() -> Config {
+        Config::new()
+    }
+
+    /// Executes a statement, returning the number of rows modified.
+    ///
+    /// A statement may contain parameters, specified by `$n`, where `n` is the index of the parameter of the list
+    /// provided, 1-indexed.
+    ///
+    /// If the statement does not modify any rows (e.g. `SELECT`), 0 is returned.
+    ///
+    /// The `query` argument can either be a `Statement`, or a raw query string. If the same statement will be
+    /// repeatedly executed (perhaps with different query parameters), consider preparing the statement up front
+    /// with the `prepare` method.
+    ///
+    pub async fn execute<T>(
+        &mut self,
+        query: &T,
+        params: &[&(dyn ToSql + Sync)],
+    ) -> Result<u64, Error>
+    where
+        T: ?Sized + ToStatement + Debug,
+    {
+        debug!("Executing query: {:?}", query);
+        self.client.execute(query, params).await
+    }
+
+    /// Executes a statement, returning the resulting rows.
+    ///
+    /// A statement may contain parameters, specified by `$n`, where `n` is the index of the parameter of the list
+    /// provided, 1-indexed.
+    ///
+    /// The `query` argument can either be a `Statement`, or a raw query string. If the same statement will be
+    /// repeatedly executed (perhaps with different query parameters), consider preparing the statement up front
+    /// with the `prepare` method.
+    ///
+    /// # Examples
+    ///
+    pub async fn query<T>(
+        &mut self,
+        query: &T,
+        params: &[&(dyn ToSql + Sync)],
+    ) -> Result<Vec<Row>, Error>
+    where
+        T: ?Sized + ToStatement + Debug,
+    {
+        debug!("Executing query: {:?}", query);
+        self.client.query(query, params).await
+    }
+
+    /// Determines if the client's connection has already closed.
+    ///
+    /// If this returns `true`, the client is no longer usable.
+    pub fn is_closed(&self) -> bool {
+        self.client.is_closed()
+    }
+
+    /// Closes the client's connection to the server.
+    ///
+    /// This is equivalent to `Client`'s `Drop` implementation, except that it returns any error encountered to the
+    /// caller.
+    pub fn close(mut self) -> Result<(), Error> {
+        self.close_inner()
+    }
+
+    fn close_inner(&mut self) -> Result<(), Error> {
+        self.client.__private_api_close();
+        Ok(())
+    }
+}
diff --git a/src/python.rs b/src/python.rs
index 14b6ee63c..dbf1f7875 100644
--- a/src/python.rs
+++ b/src/python.rs
@@ -29,7 +29,6 @@ use pyo3_log;
 use snark_verifier::util::arithmetic::PrimeField;
 use std::str::FromStr;
 use std::{fs::File, path::PathBuf};
-use tokio::runtime::Runtime;
 
 type PyFelt = String;
 
@@ -779,29 +778,27 @@ fn gen_srs(srs_path: PathBuf, logrows: usize) -> PyResult<()> {
     commitment=None,
 ))]
 fn get_srs(
+    py: Python,
     settings_path: Option<PathBuf>,
     logrows: Option<u32>,
     srs_path: Option<PathBuf>,
     commitment: Option<PyCommitments>,
-) -> PyResult<bool> {
+) -> PyResult<Bound<'_, PyAny>> {
     let commitment: Option<Commitments> = match commitment {
         Some(c) => Some(c.into()),
         None => None,
     };
 
-    Runtime::new()
-        .unwrap()
-        .block_on(crate::execute::get_srs_cmd(
-            srs_path,
-            settings_path,
-            logrows,
-            commitment,
-        ))
-        .map_err(|e| {
-            let err_str = format!("Failed to get srs: {}", e);
-            PyRuntimeError::new_err(err_str)
-        })?;
-    Ok(true)
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::get_srs_cmd(srs_path, settings_path, logrows, commitment)
+            .await
+            .map_err(|e| {
+                let err_str = format!("Failed to get srs: {}", e);
+                PyRuntimeError::new_err(err_str)
+            })?;
+
+        Ok(true)
+    })
 }
 
 /// Generates the circuit settings
@@ -885,6 +882,7 @@ fn gen_settings(
     only_range_check_rebase = DEFAULT_ONLY_RANGE_CHECK_REBASE.parse().unwrap(),
 ))]
 fn calibrate_settings(
+    py: Python,
     data: PathBuf,
     model: PathBuf,
     settings: PathBuf,
@@ -894,24 +892,27 @@ fn calibrate_settings(
     scale_rebase_multiplier: Vec<u32>,
     max_logrows: Option<u32>,
     only_range_check_rebase: bool,
-) -> Result<bool, PyErr> {
-    crate::execute::calibrate(
-        model,
-        data,
-        settings,
-        target,
-        lookup_safety_margin,
-        scales,
-        scale_rebase_multiplier,
-        only_range_check_rebase,
-        max_logrows,
-    )
-    .map_err(|e| {
-        let err_str = format!("Failed to calibrate settings: {}", e);
-        PyRuntimeError::new_err(err_str)
-    })?;
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::calibrate(
+            model,
+            data,
+            settings,
+            target,
+            lookup_safety_margin,
+            scales,
+            scale_rebase_multiplier,
+            only_range_check_rebase,
+            max_logrows,
+        )
+        .await
+        .map_err(|e| {
+            let err_str = format!("Failed to calibrate settings: {}", e);
+            PyRuntimeError::new_err(err_str)
+        })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// Runs the forward pass operation to generate a witness
@@ -946,18 +947,22 @@ fn calibrate_settings(
     srs_path=None,
 ))]
 fn gen_witness(
+    py: Python,
     data: PathBuf,
     model: PathBuf,
     output: Option<PathBuf>,
     vk_path: Option<PathBuf>,
     srs_path: Option<PathBuf>,
-) -> PyResult<PyObject> {
-    let output =
-        crate::execute::gen_witness(model, data, output, vk_path, srs_path).map_err(|e| {
-            let err_str = format!("Failed to run generate witness: {}", e);
-            PyRuntimeError::new_err(err_str)
-        })?;
-    Python::with_gil(|py| Ok(output.to_object(py)))
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        let output = crate::execute::gen_witness(model, data, output, vk_path, srs_path)
+            .await
+            .map_err(|e| {
+                let err_str = format!("Failed to generate witness: {}", e);
+                PyRuntimeError::new_err(err_str)
+            })?;
+        Python::with_gil(|py| Ok(output.to_object(py)))
+    })
 }
 
 /// Mocks the prover
@@ -1460,27 +1465,31 @@ fn verify_aggr(
     render_vk_seperately = DEFAULT_RENDER_VK_SEPERATELY.parse().unwrap(),
 ))]
 fn create_evm_verifier(
+    py: Python,
     vk_path: PathBuf,
     settings_path: PathBuf,
     sol_code_path: PathBuf,
     abi_path: PathBuf,
     srs_path: Option<PathBuf>,
     render_vk_seperately: bool,
-) -> Result<bool, PyErr> {
-    crate::execute::create_evm_verifier(
-        vk_path,
-        srs_path,
-        settings_path,
-        sol_code_path,
-        abi_path,
-        render_vk_seperately,
-    )
-    .map_err(|e| {
-        let err_str = format!("Failed to run create_evm_verifier: {}", e);
-        PyRuntimeError::new_err(err_str)
-    })?;
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::create_evm_verifier(
+            vk_path,
+            srs_path,
+            settings_path,
+            sol_code_path,
+            abi_path,
+            render_vk_seperately,
+        )
+        .await
+        .map_err(|e| {
+            let err_str = format!("Failed to run create_evm_verifier: {}", e);
+            PyRuntimeError::new_err(err_str)
+        })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// Creates an EVM compatible data attestation verifier, you will need solc installed in your environment to run this
@@ -1510,18 +1519,27 @@ fn create_evm_verifier(
     abi_path=PathBuf::from(DEFAULT_VERIFIER_DA_ABI),
 ))]
 fn create_evm_data_attestation(
+    py: Python,
     input_data: PathBuf,
     settings_path: PathBuf,
     sol_code_path: PathBuf,
     abi_path: PathBuf,
-) -> Result<bool, PyErr> {
-    crate::execute::create_evm_data_attestation(settings_path, sol_code_path, abi_path, input_data)
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::create_evm_data_attestation(
+            settings_path,
+            sol_code_path,
+            abi_path,
+            input_data,
+        )
+        .await
         .map_err(|e| {
             let err_str = format!("Failed to run create_evm_data_attestation: {}", e);
             PyRuntimeError::new_err(err_str)
         })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// Setup test evm witness
@@ -1559,29 +1577,31 @@ fn create_evm_data_attestation(
     rpc_url=None,
 ))]
 fn setup_test_evm_witness(
+    py: Python,
     data_path: PathBuf,
     compiled_circuit_path: PathBuf,
     test_data: PathBuf,
     input_source: PyTestDataSource,
     output_source: PyTestDataSource,
     rpc_url: Option<String>,
-) -> Result<bool, PyErr> {
-    Runtime::new()
-        .unwrap()
-        .block_on(crate::execute::setup_test_evm_witness(
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::setup_test_evm_witness(
             data_path,
             compiled_circuit_path,
             test_data,
             rpc_url,
             input_source.into(),
             output_source.into(),
-        ))
+        )
+        .await
         .map_err(|e| {
             let err_str = format!("Failed to run setup_test_evm_witness: {}", e);
             PyRuntimeError::new_err(err_str)
         })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// deploys the solidity verifier
@@ -1593,28 +1613,30 @@ fn setup_test_evm_witness(
     private_key=None,
 ))]
 fn deploy_evm(
+    py: Python,
     addr_path: PathBuf,
     sol_code_path: PathBuf,
     rpc_url: Option<String>,
     optimizer_runs: usize,
     private_key: Option<String>,
-) -> Result<bool, PyErr> {
-    Runtime::new()
-        .unwrap()
-        .block_on(crate::execute::deploy_evm(
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::deploy_evm(
             sol_code_path,
             rpc_url,
             addr_path,
             optimizer_runs,
             private_key,
             "Halo2Verifier",
-        ))
+        )
+        .await
         .map_err(|e| {
             let err_str = format!("Failed to run deploy_evm: {}", e);
             PyRuntimeError::new_err(err_str)
         })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// deploys the solidity vk verifier
@@ -1626,28 +1648,30 @@ fn deploy_evm(
     private_key=None,
 ))]
 fn deploy_vk_evm(
+    py: Python,
     addr_path: PathBuf,
     sol_code_path: PathBuf,
     rpc_url: Option<String>,
     optimizer_runs: usize,
     private_key: Option<String>,
-) -> Result<bool, PyErr> {
-    Runtime::new()
-        .unwrap()
-        .block_on(crate::execute::deploy_evm(
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::deploy_evm(
             sol_code_path,
             rpc_url,
             addr_path,
             optimizer_runs,
             private_key,
             "Halo2VerifyingKey",
-        ))
+        )
+        .await
         .map_err(|e| {
             let err_str = format!("Failed to run deploy_evm: {}", e);
             PyRuntimeError::new_err(err_str)
         })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// deploys the solidity da verifier
@@ -1661,6 +1685,7 @@ fn deploy_vk_evm(
     private_key=None
 ))]
 fn deploy_da_evm(
+    py: Python,
     addr_path: PathBuf,
     input_data: PathBuf,
     settings_path: PathBuf,
@@ -1668,10 +1693,9 @@ fn deploy_da_evm(
     rpc_url: Option<String>,
     optimizer_runs: usize,
     private_key: Option<String>,
-) -> Result<bool, PyErr> {
-    Runtime::new()
-        .unwrap()
-        .block_on(crate::execute::deploy_da_evm(
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::deploy_da_evm(
             input_data,
             settings_path,
             sol_code_path,
@@ -1679,13 +1703,15 @@ fn deploy_da_evm(
             addr_path,
             optimizer_runs,
             private_key,
-        ))
+        )
+        .await
         .map_err(|e| {
             let err_str = format!("Failed to run deploy_da_evm: {}", e);
             PyRuntimeError::new_err(err_str)
         })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 /// verifies an evm compatible proof, you will need solc installed in your environment to run this
 ///
@@ -1716,13 +1742,14 @@ fn deploy_da_evm(
     addr_da = None,
     addr_vk = None,
 ))]
-fn verify_evm(
-    addr_verifier: &str,
+fn verify_evm<'a>(
+    py: Python<'a>,
+    addr_verifier: &'a str,
     proof_path: PathBuf,
     rpc_url: Option<String>,
-    addr_da: Option<&str>,
-    addr_vk: Option<&str>,
-) -> Result<bool, PyErr> {
+    addr_da: Option<&'a str>,
+    addr_vk: Option<&'a str>,
+) -> PyResult<Bound<'a, PyAny>> {
     let addr_verifier = H160Flag::from(addr_verifier);
     let addr_da = if let Some(addr_da) = addr_da {
         let addr_da = H160Flag::from(addr_da);
@@ -1737,21 +1764,16 @@ fn verify_evm(
         None
     };
 
-    Runtime::new()
-        .unwrap()
-        .block_on(crate::execute::verify_evm(
-            proof_path,
-            addr_verifier,
-            rpc_url,
-            addr_da,
-            addr_vk,
-        ))
-        .map_err(|e| {
-            let err_str = format!("Failed to run verify_evm: {}", e);
-            PyRuntimeError::new_err(err_str)
-        })?;
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::verify_evm(proof_path, addr_verifier, rpc_url, addr_da, addr_vk)
+            .await
+            .map_err(|e| {
+                let err_str = format!("Failed to run verify_evm: {}", e);
+                PyRuntimeError::new_err(err_str)
+            })?;
 
-    Ok(true)
+        Ok(true)
+    })
 }
 
 /// Creates an evm compatible aggregate verifier, you will need solc installed in your environment to run this
@@ -1793,6 +1815,7 @@ fn verify_evm(
     render_vk_seperately = DEFAULT_RENDER_VK_SEPERATELY.parse().unwrap(),
 ))]
 fn create_evm_verifier_aggr(
+    py: Python,
     aggregation_settings: Vec<PathBuf>,
     vk_path: PathBuf,
     sol_code_path: PathBuf,
@@ -1800,21 +1823,25 @@ fn create_evm_verifier_aggr(
     logrows: u32,
     srs_path: Option<PathBuf>,
     render_vk_seperately: bool,
-) -> Result<bool, PyErr> {
-    crate::execute::create_evm_aggregate_verifier(
-        vk_path,
-        srs_path,
-        sol_code_path,
-        abi_path,
-        aggregation_settings,
-        logrows,
-        render_vk_seperately,
-    )
-    .map_err(|e| {
-        let err_str = format!("Failed to run create_evm_verifier_aggr: {}", e);
-        PyRuntimeError::new_err(err_str)
-    })?;
-    Ok(true)
+) -> PyResult<Bound<'_, PyAny>> {
+    pyo3_asyncio::tokio::future_into_py(py, async move {
+        crate::execute::create_evm_aggregate_verifier(
+            vk_path,
+            srs_path,
+            sol_code_path,
+            abi_path,
+            aggregation_settings,
+            logrows,
+            render_vk_seperately,
+        )
+        .await
+        .map_err(|e| {
+            let err_str = format!("Failed to run create_evm_verifier_aggr: {}", e);
+            PyRuntimeError::new_err(err_str)
+        })?;
+
+        Ok(true)
+    })
 }
 
 // Python Module
diff --git a/src/tensor/mod.rs b/src/tensor/mod.rs
index 3b1a77b09..7cadc7ea0 100644
--- a/src/tensor/mod.rs
+++ b/src/tensor/mod.rs
@@ -401,7 +401,7 @@ impl IntoI64 for () {
         0
     }
     fn from_i64(_: i64) -> Self {
-        ()
+        
     }
 }
 
@@ -419,7 +419,7 @@ impl<F: PrimeField + IntoI64> IntoI64 for Value<F> {
         let mut res = vec![];
         self.map(|x| res.push(x.into_i64()));
 
-        if res.len() == 0 {
+        if res.is_empty() {
             0
         } else {
             res[0]
diff --git a/tests/python/binding_tests.py b/tests/python/binding_tests.py
index 902d5f210..d7bea3c29 100644
--- a/tests/python/binding_tests.py
+++ b/tests/python/binding_tests.py
@@ -109,7 +109,7 @@ def test_gen_srs():
 
 
 
-def test_calibrate_over_user_range():
+async def test_calibrate_over_user_range():
     data_path = os.path.join(
         examples_path,
         'onnx',
@@ -136,14 +136,14 @@ def test_calibrate_over_user_range():
         model_path, output_path, py_run_args=run_args)
     assert res == True
 
-    res = ezkl.calibrate_settings(
+    res = await ezkl.calibrate_settings(
         data_path, model_path, output_path, "resources", 1, [0, 1, 2])
     assert res == True
     assert os.path.isfile(output_path)
 
 
 
-def test_calibrate():
+async def test_calibrate():
     data_path = os.path.join(
         examples_path,
         'onnx',
@@ -170,7 +170,7 @@ def test_calibrate():
         model_path, output_path, py_run_args=run_args)
     assert res == True
 
-    res = ezkl.calibrate_settings(
+    res = await ezkl.calibrate_settings(
         data_path, model_path, output_path, "resources")
     assert res == True
     assert os.path.isfile(output_path)
@@ -198,7 +198,7 @@ def test_model_compile():
     assert res == True
 
 
-def test_forward():
+async def test_forward():
     """
     Test for vanilla forward pass
     """
@@ -217,7 +217,7 @@ def test_forward():
         'witness.json'
     )
 
-    res = ezkl.gen_witness(data_path, model_path, output_path)
+    res = await ezkl.gen_witness(data_path, model_path, output_path)
 
     with open(output_path, "r") as f:
         data = json.load(f)
@@ -229,12 +229,12 @@ def test_forward():
     assert data["processed_outputs"]["poseidon_hash"] == res["processed_outputs"]["poseidon_hash"]
 
 
-def test_get_srs():
+async def test_get_srs():
     """
     Test for get_srs
     """
     settings_path = os.path.join(folder_path, 'settings.json')
-    res = ezkl.get_srs(settings_path, srs_path=srs_path)
+    res = await ezkl.get_srs(settings_path, srs_path=srs_path)
 
     assert res == True
 
@@ -242,7 +242,7 @@ def test_get_srs():
 
     another_srs_path = os.path.join(folder_path, "kzg_test_k8.params")
 
-    res = ezkl.get_srs(logrows=8, srs_path=another_srs_path, commitment=ezkl.PyCommitments.KZG)
+    res = await ezkl.get_srs(logrows=8, srs_path=another_srs_path, commitment=ezkl.PyCommitments.KZG)
 
     assert os.path.isfile(another_srs_path)
 
@@ -393,9 +393,7 @@ def test_prove_evm():
     assert os.path.isfile(proof_path)
 
 
-
-
-def test_create_evm_verifier():
+async def test_create_evm_verifier():
     """
     Create EVM verifier with solidity code
     In order to run this test you will need to install solc in your environment
@@ -405,7 +403,7 @@ def test_create_evm_verifier():
     sol_code_path = os.path.join(folder_path, 'test.sol')
     abi_path = os.path.join(folder_path, 'test.abi')
 
-    res = ezkl.create_evm_verifier(
+    res = await ezkl.create_evm_verifier(
         vk_path,
         settings_path,
         sol_code_path,
@@ -417,7 +415,7 @@ def test_create_evm_verifier():
     assert os.path.isfile(sol_code_path)
 
 
-def test_deploy_evm():
+async def test_deploy_evm():
     """
     Test deployment of the verifier smart contract
     In order to run this you will need to install solc in your environment
@@ -428,7 +426,7 @@ def test_deploy_evm():
     # TODO: without optimization there will be out of gas errors
     # sol_code_path = os.path.join(folder_path, 'test.sol')
 
-    res = ezkl.deploy_evm(
+    res = await ezkl.deploy_evm(
         addr_path,
         sol_code_path,
         rpc_url=anvil_url,
@@ -437,7 +435,7 @@ def test_deploy_evm():
     assert res == True
 
 
-def test_deploy_evm_with_private_key():
+async def test_deploy_evm_with_private_key():
     """
     Test deployment of the verifier smart contract using a custom private key
     In order to run this you will need to install solc in your environment
@@ -450,7 +448,7 @@ def test_deploy_evm_with_private_key():
 
     anvil_default_private_key = "ac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80"
 
-    res = ezkl.deploy_evm(
+    res = await ezkl.deploy_evm(
         addr_path,
         sol_code_path,
         rpc_url=anvil_url,
@@ -462,7 +460,7 @@ def test_deploy_evm_with_private_key():
     custom_zero_balance_private_key = "ff9dfe0b6d31e93ba13460a4d6f63b5e31dd9532b1304f1cbccea7092a042aa4"
 
     with pytest.raises(RuntimeError, match="Failed to run deploy_evm"):
-        res = ezkl.deploy_evm(
+        res = await ezkl.deploy_evm(
             addr_path,
             sol_code_path,
             rpc_url=anvil_url,
@@ -470,7 +468,7 @@ def test_deploy_evm_with_private_key():
         )
 
 
-def test_verify_evm():
+async def test_verify_evm():
     """
     Verifies an evm proof
     In order to run this you will need to install solc in your environment
@@ -486,7 +484,7 @@ def test_verify_evm():
     # TODO: without optimization there will be out of gas errors
     # sol_code_path = os.path.join(folder_path, 'test.sol')
 
-    res = ezkl.verify_evm(
+    res = await ezkl.verify_evm(
         addr,
         proof_path,
         rpc_url=anvil_url,
@@ -497,7 +495,7 @@ def test_verify_evm():
     assert res == True
 
 
-def test_aggregate_and_verify_aggr():
+async def test_aggregate_and_verify_aggr():
     data_path = os.path.join(
         examples_path,
         'onnx',
@@ -526,7 +524,7 @@ def test_aggregate_and_verify_aggr():
     res = ezkl.gen_settings(model_path, settings_path)
     assert res == True
 
-    res = ezkl.calibrate_settings(
+    res = await ezkl.calibrate_settings(
         data_path, model_path, settings_path, "resources")
     assert res == True
     assert os.path.isfile(settings_path)
@@ -548,7 +546,7 @@ def test_aggregate_and_verify_aggr():
         '1l_relu_aggr_witness.json'
     )
 
-    res = ezkl.gen_witness(data_path, compiled_model_path,
+    res = await ezkl.gen_witness(data_path, compiled_model_path,
                            output_path)
 
     ezkl.prove(
@@ -603,7 +601,7 @@ def test_aggregate_and_verify_aggr():
     assert res == True
 
 
-def test_evm_aggregate_and_verify_aggr():
+async def test_evm_aggregate_and_verify_aggr():
     data_path = os.path.join(
         examples_path,
         'onnx',
@@ -628,7 +626,7 @@ def test_evm_aggregate_and_verify_aggr():
         settings_path,
     )
 
-    ezkl.calibrate_settings(
+    await ezkl.calibrate_settings(
         data_path,
         model_path,
         settings_path,
@@ -657,7 +655,7 @@ def test_evm_aggregate_and_verify_aggr():
         '1l_relu_aggr_evm_witness.json'
     )
 
-    res = ezkl.gen_witness(data_path, compiled_model_path,
+    res = await ezkl.gen_witness(data_path, compiled_model_path,
                            output_path)
 
     ezkl.prove(
@@ -698,7 +696,7 @@ def test_evm_aggregate_and_verify_aggr():
     sol_code_path = os.path.join(folder_path, 'aggr_evm_1l_relu.sol')
     abi_path = os.path.join(folder_path, 'aggr_evm_1l_relu.abi')
 
-    res = ezkl.create_evm_verifier_aggr(
+    res = await ezkl.create_evm_verifier_aggr(
         [settings_path],
         aggregate_vk_path,
         sol_code_path,
@@ -712,7 +710,7 @@ def test_evm_aggregate_and_verify_aggr():
 
     addr_path = os.path.join(folder_path, 'address_aggr.json')
 
-    res = ezkl.deploy_evm(
+    res = await ezkl.deploy_evm(
         addr_path,
         sol_code_path,
         rpc_url=anvil_url,
@@ -730,7 +728,7 @@ def test_evm_aggregate_and_verify_aggr():
     # with open(addr_path, 'r') as file:
     #     addr_aggr = file.read().rstrip()
 
-    # res = ezkl.verify_evm(
+    # res = await ezkl.verify_evm(
     #     aggregate_proof_path,
     #     addr_aggr,
     #     rpc_url=anvil_url,
@@ -769,7 +767,7 @@ def get_examples():
 
 
 @pytest.mark.parametrize("model_file, input_file", get_examples())
-def test_all_examples(model_file, input_file):
+async def test_all_examples(model_file, input_file):
     """Tests all examples in the examples folder"""
     # gen settings
     settings_path = os.path.join(folder_path, "settings.json")
@@ -783,7 +781,7 @@ def test_all_examples(model_file, input_file):
     res = ezkl.gen_settings(model_file, settings_path)
     assert res
 
-    res = ezkl.calibrate_settings(
+    res = await ezkl.calibrate_settings(
         input_file, model_file, settings_path, "resources")
     assert res
 
@@ -814,7 +812,7 @@ def test_all_examples(model_file, input_file):
     assert os.path.isfile(pk_path)
 
     print("Generating witness for example: ", model_file)
-    res = ezkl.gen_witness(input_file, compiled_model_path, witness_path)
+    res = await ezkl.gen_witness(input_file, compiled_model_path, witness_path)
     assert os.path.isfile(witness_path)
 
     print("Proving example: ", model_file)