Chore/update docs (#67)

* chore: update anvil install instructions * chore: remove settings.json where it is deprecated * chore: update aggregagtion docs * chore: lint * remove more settings, get-srs --------- Co-authored-by: jason <[email protected]>
zkonduit · Oct 6, 2023 · 953a443 · 953a443
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diff --git a/About_EZKL/Commands.md b/About_EZKL/Commands.md
@@ -81,7 +81,7 @@ Along with our SRS and circuit parameters, we need two other elements to generat
 Run this command to set up your proving and verifying keys:
 
 ```bash
-ezkl setup -M network.ezkl --srs-path=17.srs  --settings-path=settings.json
+ezkl setup -M network.ezkl --srs-path=17.srs  
 ```
 You should now have files called `vk.key` and `pk.key` in the root of your project. You can also specify different paths for these outputs with `--vk-path=altvk.key --pk-path=altpk.key`
 
@@ -130,7 +130,7 @@ Note that these are not the only operations that can be performed by `ezkl`. You
 When you're testing a model, you may not want to run `setup` and `prove` with each iteration. `ezkl` provides a simple alternative with `mock`, where you can convert your model to constraints and run the inputs tosee if a proof can be generated. This saves time when testing new iterations of models with potential issues. Here is the command for `mock`:
 
 ```bash
-ezkl mock -M network.ezkl --witness witness.json --settings-path=settings.json
+ezkl mock -M network.ezkl --witness witness.json
 ```
 
 Mock is basically checking that constraints that your model has been translated into are satisfied, without doing any of the subsequent cryptographic heavy lifting to produce a proof. 

diff --git a/FAQ.md b/FAQ.md
@@ -2,78 +2,110 @@
 icon: question
 order: -99999999
 ---
-![](../assets/copter.png) 
+![](../assets/copter.png)
+
 # FAQ
+
 ### What programming languages and frameworks does ezkl support?
-`ezkl` is a command line tool, and a library that can be used from Rust or Python. You may want to use Python to create a neural network and export it. Though ezkl is built with Rust, you do not need to use Rust except possibly for installation. 
+
+`ezkl` is a command line tool, and a library that can be used from Rust or Python. You may want to use Python to create a neural network and export it. Though ezkl is built with Rust, you do not need to use Rust except possibly for installation.
 
 ### Do I need to know Rust before getting started with ezkl?
-No, Rust is not a requirement to use the library. As long as you have the ONNX file and proper input & output format of the model, you can use `ezkl`. 
 
+No, Rust is not a requirement to use the library. As long as you have the ONNX file and proper input & output format of the model, you can use `ezkl`.
 
 # Technical
+
 ### Why is the gen-srs step slow?
+
 Generating a structured reference string takes a considerable amount of time and memory. Make sure your machine has enough memory available and wait for the process to finish. Alternatively, download a pre-generated srs.
 
 ### Can I use ezkl with other machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn?
+
 All `ezkl` requires is an onnx file and a JSON configuration of mock inputs and outputs of the neural network. At this time, it works best with PyTorch.
 
 ### How fast is ezkl?
+
 We believe that `ezkl` is the fastest zkml package available, and we are working hard every day to make it faster. Feel free to run `cargo bench` on your machine to see what the benchmarks are for your hardware.
 
 ### Do I need to deploy a verifier smart contract to use ezkl?
+
 No, we recently integrated a [WASM verifier](https://github.com/zkonduit/ezkl/pull/219) that you can use to verify proofs from your web application. You can also use the EVM verifier to verify proofs locally, or the command line `ezkl verify` command.
 
-# Errors:
+# Errors
+
 ### Error: VerifyError
-A VerifyError is thrown when the Mock prover fails, often due to a mismatched shape problem in the model. Please verify that your input.json inputs and outputs match those of your .onnx file. 
+
+A VerifyError is thrown when the Mock prover fails, often due to a mismatched shape problem in the model. Please verify that your input.json inputs and outputs match those of your .onnx file.
 
 ### Error: DimMismatch
-A DimMismatch error is thrown when there is a mismatch in the lengths of the tensor operands during circuit construction. 
+
+A DimMismatch error is thrown when there is a mismatch in the lengths of the tensor operands during circuit construction.
+
 ### Error: LookupInstantiation
+
 This error is thrown when there is an error during the creation of a lookup table
+
 ### Error: TableAlreadyAssigned
+
 A TableAlreadyAssigned Error is thrown when `ezkl` attempts to initialize a lookup table that has already been initialized
+
 ### Error: UnsupportedOp
+
 An UnsupportedOp Error is thrown when there is an operation in the ONNX file that `ezkl` cannot yet handle. Please look at the supported operations under src/circuit/ops to get an idea of what operations `ezkl` can handle.
+
 ### Error: PyValueError
+
 This is a pyo3 error that occurs when a data type fails to be extracted from Python to Rust. Please make sure you are passing the correct data types when utilizing the python bindings.
+
 ### Error: InvalidLookupInputs
-InvalidLookupInputs is thrown when the wrong inputs were passed to a lookup node. 
+
+InvalidLookupInputs is thrown when the wrong inputs were passed to a lookup node.
+
 ### Error: InvalidDims
+
 InvalidDims is thrown when there is a shape mismatch in circuit construction. Invalid dimensions were used for a node with the given index and description.
+
 ### Error: WrongMethod
+
 This error means that the wrong method was called to configure a node with the given index and description
+
 ### Error: MissingNode
+
 MissingNode is thrown when a requested node is missing in the graph with the given index
+
 ### Error: OpMismatch
+
 OpMismatch is thrown when an unsupported method was called on a node with the given index and description
+
 ### Error: UnsupportedOp
+
 UnsupportedOp is thrown when there is an operation in the onnx graph that isn't supported by `ezkl`
+
 ### Error: MissingParams
-MissingParams is thrown when a node has missing parameters; please check the parameters in your model's operations
-### Error: MisformedParams
-MisformedParams is thrown when a node has misformed parameters; the error can stem from erroneous padding height and width dimensions, wrong kernel / data format, dilations that are not uint type, and more.
-### Error: Visibility
-This error is typically thrown when no public variables are passed to the circuit configuration function
-### Error: NonConstantDiv
-`ezkl` only supports divisions by constants
-### Error: NonConstantPower
-`ezkl` only supports constant exponents
-### Error: RescalingError
-This error is thrown when attempting to rescale inputs for an operation
-### Error: ModelLoad
-This error is thrown when a model fails to load; please check your onnx file for missing connections / unsupported layers. We suggest using [Netron](https://netron.app/) to view onnx files. 
 
+MissingParams is thrown when a node has missing parameters; please check the parameters in your model's operations
 
+### Error: MisformedParams
 
+MisformedParams is thrown when a node has misformed parameters; the error can stem from erroneous padding height and width dimensions, wrong kernel / data format, dilations that are not uint type, and more.
 
+### Error: Visibility
 
+This error is typically thrown when no public variables are passed to the circuit configuration function
 
+### Error: NonConstantDiv
 
+`ezkl` only supports divisions by constants
 
+### Error: NonConstantPower
 
+`ezkl` only supports constant exponents
 
+### Error: RescalingError
 
+This error is thrown when attempting to rescale inputs for an operation
 
+### Error: ModelLoad
 
+This error is thrown when a model fails to load; please check your onnx file for missing connections / unsupported layers. We suggest using [Netron](https://netron.app/) to view onnx files.
diff --git a/Getting_Started.md b/Getting_Started.md
@@ -4,30 +4,37 @@ order: 95
 ---
 
 ### Installing EZKL
+
 To use `ezkl` in Python, just `pip install ezkl`. You will generally also need `onnx` installed if you are exporting models, and Pytorch, Tensorflow, or similar if you are creating models.
 
 `ezkl` uses your system `solc` Solidity compiler, so you may need to tweak it using svm-rs or solc-select, particularly if you are targeting a specific hardfork.
 
-To use the cli, download a [release binary](https://github.com/zkonduit/ezkl/releases) from GitHub. If you want the latest build, you can also install from source. 
-
+To use the cli, download a [release binary](https://github.com/zkonduit/ezkl/releases) from GitHub. If you want the latest build, you can also install from source.
 
 ### Building from source 🔨
+
 Ezkl is built in rust. First [install rust](https://www.rust-lang.org/tools/install), then download the repo and enter the directory
+
 ```bash
 git clone [email protected]:zkonduit/ezkl.git
 cd ezkl
 ```
+
 After which you may build and install the library
+
 ```bash
 cargo install --force --path .
 ```
 
 If you want to build manually with cargo build, be sure to use the release flag as the debug build will result in slow proofs
+
 ```bash
 cargo build --release --bin ezkl
 ```
+
 > Note: To render your model circuits, you'll need to compile `ezkl` with the `render` feature (`cargo build --features render --bin ezkl`). This enables the `render-circuit` command which can create `.png` representations of the compiled circuits. You'll also need to install the `libexpat1-dev` and `libfreetype6-dev` libraries on Debian systems (there are equivalents for MacOS as well).
 ---------
+
 ##### Rust docs 📖
 
 Use `cargo doc --open` to compile and open the Rust documentation for `ezkl` in your default browser.