diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 8950c574..1d23f572 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -36,6 +36,8 @@ test_prebuilt_docker:
   script:
     - set -e
     - poetry install --all-extras
+    - poetry run python -m netdeployonnx.cli server --configfile external/netdeployonnx/netdeployonnx.yaml &
+    - sleep 30
     - poetry run python3 -m pytest  -v --cov=hannah test hannah
   tags:
     - docker
@@ -51,6 +53,7 @@ test_python_39:
     - set -e
     - poetry config installer.max-workers 10
     - poetry install --all-extras
+    - poetry run python -m netdeployonnx.cli server --configfile external/netdeployonnx/netdeployonnx.yaml &
     - poetry run python3 -m pytest  -v --cov=hannah test hannah --integration
   tags:
     - docker
@@ -69,7 +72,8 @@ test_python_310:
     - "echo 'import coverage; coverage.process_startup()' > sitecustomize.py"
     - export PYTHONPATH=$PWD
     - export COVERAGE_PROCESS_START=$PWD/.coveragerc
-    - poetry run coverage run --source hannah -m pytest test
+    - poetry run python -m netdeployonnx.cli server --configfile external/netdeployonnx/netdeployonnx.yaml &
+    - poetry run coverage run --source hannah -m pytest test hannah/nas/tests --integration
     - unset PYTHONPATH
     - unset COVERAGE_PROCESS_START
     - poetry run coverage report
@@ -96,7 +100,8 @@ test_python_311:
     - "echo 'import coverage; coverage.process_startup()' > sitecustomize.py"
     - export PYTHONPATH=$PWD
     - export COVERAGE_PROCESS_START=$PWD/.coveragerc
-    - poetry run coverage run --source hannah -m pytest test
+    - poetry run python -m netdeployonnx.cli server --configfile external/netdeployonnx/netdeployonnx.yaml &
+    - poetry run coverage run --source hannah -m pytest test hannah/nas/tests --integration
     - unset PYTHONPATH
     - unset COVERAGE_PROCESS_START
     - poetry run coverage report
diff --git a/.gitmodules b/.gitmodules
index b4bdc3cb..eb4453bc 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,6 @@
 [submodule "external/hannah-tvm"]
 	path = external/hannah-tvm
 	url = ../hannah-tvm
+[submodule "external/netdeployonnx"]
+	path = external/netdeployonnx
+	url = ../netdeployonnx
diff --git a/experiments/cifar10_ai8x/config.yaml b/experiments/cifar10_ai8x/config.yaml
new file mode 100644
index 00000000..e10f3885
--- /dev/null
+++ b/experiments/cifar10_ai8x/config.yaml
@@ -0,0 +1,47 @@
+defaults:
+    - base_config
+    - override nas: random_nas
+    - override backend: max78000
+    - override dataset: cifar10          # Dataset configuration name
+    - override features: identity        # Feature extractor configuration name (use identity for vision datasets)
+    - override optimizer: sgd            # Optimizer config name
+    - override scheduler: 1cycle         # learning rate scheduler config name
+    - override normalizer: null          # Feature normalizer (used for quantized neural networks)
+    - override module: image_classifier  # Lightning module config for the training loop (image classifier for image classification tasks)
+    - override augmentation: weak_augment
+    - _self_
+    - override model: ai8x_net_simple           # Neural network name (only working with @search_space)
+    - override /nas/constraint_model: random_walk
+
+backend: # this should be max78000 / grpc backend
+  client_connect: localhost:28329
+  should_reraise: False
+  keepalive_timeout: 4 # maybe increase, if the connection is bad?
+  device_filter:
+  - model: VirtualDevice
+
+nas:
+  constrained_sampling_on_search: True
+  budget: 1
+  n_jobs: 1
+  predictor:
+    backend:
+      _target_: hannah.nas.performance_prediction.simple.BackendPredictor
+      # model_name: ${model.name}
+      backend: ${backend}
+
+  metrics:
+    uJ_per_weights_loading:
+      name: uJ per Weights loading
+    total_weights:
+      name: Weights
+  presample: false # presampling disable allows for faster candidates with more trash potential
+
+experiment: test_max78000_backend
+
+trainer:
+  max_epochs: 1
+  # precision: 8 # precision 8 is invalid
+
+optimizer:
+  lr: 0.3
diff --git a/experiments/cifar10_ai8x/experiment/test_max78000_backend.yaml b/experiments/cifar10_ai8x/experiment/test_max78000_backend.yaml
new file mode 100644
index 00000000..eeba8065
--- /dev/null
+++ b/experiments/cifar10_ai8x/experiment/test_max78000_backend.yaml
@@ -0,0 +1 @@
+experiment_id: test_max78000_backend
diff --git a/external/netdeployonnx b/external/netdeployonnx
new file mode 160000
index 00000000..81faa01e
--- /dev/null
+++ b/external/netdeployonnx
@@ -0,0 +1 @@
+Subproject commit 81faa01eae6aefe62fc37824787cbd3512b2d302
diff --git a/hannah/backends/__init__.py b/hannah/backends/__init__.py
index d9e728a5..21b97c6a 100644
--- a/hannah/backends/__init__.py
+++ b/hannah/backends/__init__.py
@@ -21,9 +21,11 @@
 from .onnxrt import OnnxruntimeBackend
 from .tensorrt import TensorRTBackend
 from .torch_mobile import TorchMobileBackend
+from .grpc import GRPCBackend
 
 __all__ = [
     "OnnxruntimeBackend",
     "TorchMobileBackend",
     "TensorRTBackend",
+    "GRPCBackend",
 ]
diff --git a/hannah/backends/grpc.py b/hannah/backends/grpc.py
new file mode 100644
index 00000000..3c26576f
--- /dev/null
+++ b/hannah/backends/grpc.py
@@ -0,0 +1,271 @@
+#
+# Copyright (c) 2024 Hannah contributors.
+#
+# This file is part of hannah.
+# See https://github.com/ekut-es/hannah for further info.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import collections
+import copy
+import io
+import logging
+import os
+from collections import namedtuple
+from collections.abc import Sequence
+from pathlib import Path
+from typing import Union
+
+import torch
+
+try:
+    from hannah.backends.base import (
+        ClassifierModule,
+        InferenceBackendBase,
+        ProfilingResult,
+    )
+    from hannah.nas.export.onnx import to_onnx
+except ImportError:
+    # thats not a good thing to do, but works for now
+    class ClassifierModule:
+        def __init__(self, model):
+            self.model = model
+
+        @property
+        def example_input_array(self):
+            return self.model
+
+    class InferenceBackendBase: ...
+
+    ProfilingResult = namedtuple(
+        "ProfilingResult", field_names=["outputs", "metrics", "profile"]
+    )
+
+    def to_onnx(lightning_model) -> "onnx.ModelProto":
+        return lightning_model.onnx_model
+
+
+try:
+    from netdeployonnx.common.netclient_remote import (
+        NetClient,
+        get_netclient_from_connect,
+    )
+except ImportError as ie:
+    logging.warning(f"Could not import netdeployonnx: {ie}")
+    NetClient = None
+    get_netclient_from_connect = None
+try:
+    import asyncio
+
+    import grpc
+    import onnx
+except ImportError:
+    grpc = None
+    onnx = None
+
+
+class GRPCBackend(InferenceBackendBase):
+    def __init__(self, *args, client_connect: str = "localhost:28329", **kwargs):
+        self.client_connect = client_connect
+        self.auth: Path | str | bytes | None = kwargs.pop("auth", None)
+        self.device_filter: list[dict] = kwargs.pop(
+            "device_filter", [{"model": "EVKit_V1"}]
+        )
+        # make sure each is a dict
+        self.device_filter = [dict(f) for f in self.device_filter]
+
+        self.keepalive_timeout: float = kwargs.pop("keepalive_timeout", 4)
+        self.function_timeout: float = kwargs.pop("function_timeout", 4)
+
+        self.should_reraise: bool = kwargs.pop("should_reraise", False)
+
+        self._client = None
+        self.modelbytes = None
+        super().__init__(*args, **kwargs)
+
+    @property
+    def client(self) -> NetClient:
+        if self._client is None:
+            try:
+                # either it is a path
+                if isinstance(self.auth, (str, Path)):
+                    if os.path.exists(self.auth):
+                        with open(self.auth, "rb") as f:
+                            auth = f.read()
+                    else:
+                        raise FileNotFoundError(f"File {self.auth} not found")
+                elif isinstance(self.auth, bytes):
+                    auth = self.auth
+                else:
+                    auth = None
+                self._client = get_netclient_from_connect(
+                    self.client_connect,
+                    auth,
+                    keepalive_timeout=self.keepalive_timeout,
+                )
+            except Exception:
+                raise  # ConnectionError(f"Could not connect to client: {ex}")
+        return self._client
+
+    def __del__(self):
+        if self._client is not None:
+            self._client.close()
+
+    def prepare(self, module: ClassifierModule):
+        """
+        Prepare the model for execution on the target device
+
+        Args:
+          module: the classifier module to be exported
+
+        """
+        self.module = module
+        quantized_model = copy.deepcopy(module.model)
+        quantized_model.cpu()
+        quantized_model.train(False)
+
+        dummy_input = module.example_input_array.cpu()  # noqa: F841
+        bytesio = io.BytesIO()
+
+        def torch_aten_copy(g, input, *args):
+            # print("TYPE=", type(input), "INPUT=", input)
+            # if input is a torch.Value and is a float
+            # then we can just return 2**input
+            # exp_constant = None
+            # exp_constant = 0
+            # if exp_constant is not None:
+            #     return g.op("Const", torch.tensor(2.0**exp_constant, dtype=float))
+            return g.op("Identity", torch.tensor(2.0**1, dtype=float))
+
+        torch.onnx.register_custom_op_symbolic("aten::copy", torch_aten_copy, 1)
+
+        # torch.onnx.export(
+        #     quantized_model,
+        #     dummy_input,
+        #     bytesio,
+        #     verbose=False,
+        #     opset_version=11,
+        # )
+        try:
+            modelProto: onnx.ModelProto = to_onnx(quantized_model)  # noqa: N806
+            bytesio.write(modelProto.SerializeToString())
+        except Exception as ex:
+            # exporting failed
+            raise ex
+
+        self.modelbytes = bytesio.getvalue()
+
+    def run(self, *inputs) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
+        """
+        Run a batch on the target device
+
+        Args:
+          inputs: a list of torch tensors representing the inputs to be run on the target device, each tensor represents a whole batched input, so for models taking 1 parameter, the list will contain 1 tensor of shape (batch_size, *input_shape)
+
+        Returns: the output(s) of the model as a torch tensor or a Sequence of torch tensors for models producing multiple outputs
+
+        """  # noqa: E501
+        return self._run(*inputs, profiling=False)
+
+    def profile(self, *inputs: torch.Tensor) -> ProfilingResult:
+        """Do a profiling run on the target device
+
+        Args:
+            inputs: a list of torch tensors representing the inputs to be run on the target device, each tensor represents a whole batched input, so for models taking 1 parameter, the list will contain 1 tensor of shape (batch_size, *input_shape)
+
+        Returns: a ProfilingResult object containing the outputs of the model, the metrics obtained from the profiling run and the raw profile in a backend-specific format
+        """  # noqa: E501
+        return self._run(*inputs, profiling=True)
+
+    async def _run_async(
+        self, *inputs: torch.Tensor, profiling: bool = False
+    ) -> Union[torch.Tensor, Sequence[torch.Tensor], ProfilingResult]:
+        if get_netclient_from_connect is None:
+            raise ImportError("netdeployonnx not installed")
+        with get_netclient_from_connect(
+            self.client_connect,
+            self.auth,
+            keepalive_timeout=self.keepalive_timeout,
+            should_reraise=self.should_reraise,
+        ) as client:
+            async with client.connect(filters=self.device_filter) as device:
+                input_bytes: bytes = b""
+                result_dict = await device.deploy(
+                    modelbytes=self.modelbytes,
+                    input_bytes=input_bytes,
+                    profiling=profiling,
+                    timeout=self.function_timeout,
+                )
+                if profiling:
+                    # FIXME: select the first? maybe average?
+                    metrics = result_dict["metrics"]
+                    if isinstance(result_dict, collections.abc.Iterable):
+                        metrics = metrics[0] if len(metrics) > 0 else {}
+                    return ProfilingResult(
+                        outputs=result_dict["result"],
+                        metrics=metrics,
+                        profile=result_dict["deployment_execution_times"],
+                    )
+                else:
+                    return result_dict["result"]
+        raise ConnectionError("Could not connect to client")
+
+    def _get_loop(self) -> asyncio.AbstractEventLoop:
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+
+        return loop
+
+    def _run(
+        self, *inputs: torch.Tensor, profiling: bool = False
+    ) -> Union[torch.Tensor, Sequence[torch.Tensor], ProfilingResult]:
+        try:
+            loop = self._get_loop()
+            async_result = loop.run_until_complete(
+                self._run_async(*inputs, profiling=profiling)
+            )
+            return async_result
+        except TimeoutError:
+            raise
+        except ValueError:
+            raise
+        except ConnectionError:
+            raise
+        except grpc._channel._InactiveRpcError as irpce:
+            raise Exception(
+                "Inactive RPC Server (either server down or not reachable)"
+            ) from irpce  # noqa: E501
+        except Exception as ex:
+            raise ex  # reraise
+
+    @classmethod
+    def available(cls) -> bool:
+        """
+        Check if the backend is available
+
+        Returns: True if the backend is available, False otherwise
+
+        """
+        try:
+            # TODO: check server availability?
+            return (
+                grpc is not None
+                and onnx is not None
+                and NetClient is not None
+                and get_netclient_from_connect is not None
+            )
+        except Exception:
+            pass
+        return False
diff --git a/hannah/conf/backend/max78000.yaml b/hannah/conf/backend/max78000.yaml
new file mode 100644
index 00000000..5e1d107d
--- /dev/null
+++ b/hannah/conf/backend/max78000.yaml
@@ -0,0 +1,21 @@
+##
+## Copyright (c) 2022 University of Tübingen.
+##
+## This file is part of hannah.
+## See https://atreus.informatik.uni-tuebingen.de/ties/ai/hannah/hannah for further info.
+##
+## Licensed under the Apache License, Version 2.0 (the "License");
+## you may not use this file except in compliance with the License.
+## You may obtain a copy of the License at
+##
+##     http://www.apache.org/licenses/LICENSE-2.0
+##
+## Unless required by applicable law or agreed to in writing, software
+## distributed under the License is distributed on an "AS IS" BASIS,
+## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+## See the License for the specific language governing permissions and
+## limitations under the License.
+##
+
+
+_target_: hannah.backends.grpc.GRPCBackend
diff --git a/hannah/conf/model/ai8x_net.yaml b/hannah/conf/model/ai8x_net.yaml
new file mode 100644
index 00000000..1a921296
--- /dev/null
+++ b/hannah/conf/model/ai8x_net.yaml
@@ -0,0 +1,12 @@
+_target_: hannah.models.ai8x.models.ai8x_search_space
+name: ai8x_net
+num_classes: 10
+max_blocks: 4
+
+# constraints:
+#   - name: weights
+#     upper: 550000
+#     # lower: 250000
+#   - name: macs
+#     upper: 128000000
+#     # lower: 60000000
diff --git a/hannah/conf/model/ai8x_net_simple.yaml b/hannah/conf/model/ai8x_net_simple.yaml
new file mode 100644
index 00000000..4d3b8cb1
--- /dev/null
+++ b/hannah/conf/model/ai8x_net_simple.yaml
@@ -0,0 +1,12 @@
+_target_: hannah.models.ai8x.models_simplified.ai8x_search_space
+name: ai8x_net_simple
+num_classes: 10
+max_blocks: 4
+
+# constraints:
+#   - name: weights
+#     upper: 550000
+#     # lower: 250000
+#   - name: macs
+#     upper: 128000000
+#     # lower: 60000000
diff --git a/hannah/models/ai8x/models.py b/hannah/models/ai8x/models.py
index 136d59b7..075886f8 100644
--- a/hannah/models/ai8x/models.py
+++ b/hannah/models/ai8x/models.py
@@ -19,12 +19,13 @@
 """A search space based on the cifar 10 NASNet search space for ai85x devices from: htt"""
 
 from hannah.nas.expressions.types import Int
-from hannah.nas.functional_operators.op import ChoiceOp, Tensor, scope
+from hannah.nas.functional_operators.op import ChoiceOp, Tensor, scope, search_space
 from hannah.nas.functional_operators.operators import (
     BatchNorm,
     Conv2d,
     Identity,
     Linear,
+    AvgPooling,
     MaxPooling,
     Relu,
     Requantize,
@@ -40,7 +41,11 @@ def max_pool(input, kernel_size=2, stride=2, padding=2):
     return MaxPooling(kernel_size=kernel_size, stride=stride, padding=padding)(input)
 
 
-def conv2d(input, weight, stride=1, dilation=1, groups=1, padding=None):
+def avg_pool(input, kernel_size=2, stride=2, dilation=1):
+    return AvgPooling(kernel_size=kernel_size, stride=stride, dilation=dilation)(input)
+
+
+def conv2d(input, weight, bias=None, stride=1, dilation=1, groups=1, padding=None):
     return Conv2d(stride=stride, dilation=dilation, groups=groups, padding=padding)(
         input, weight
     )
@@ -74,69 +79,108 @@ def batch_norm(input):
 
 
 def quantize_weight(weight):
-    weight = Requantize()(weight)
+    # weight = Requantize()(weight)
     return weight
 
 
-@scope
-def block(input, expand_ratio, reduce_ratio):
-    max_pool1 = max_pool(input, kernel_size=2, stride=2, padding=1)
-    identity1 = identity(input)
-    # conv_in = choice(input, [max_pool1, identity1])
+def quantize_activation(activation):
+    # activation = Requantize()(activation)
+    return activation
 
-    conv_in = input
 
+@scope
+def block(
+    input, channels: int, kernel3x3: bool, kspo: tuple[int, int, int, str | None]
+):
+    max_pool1 = max_pool(input, kernel_size=kspo[0], stride=kspo[1], padding=0)
+    avg_pool1 = avg_pool(input, kernel_size=kspo[0], stride=kspo[1])
+    identity1 = identity(input)
+    # conv_in = choice(input, [max_pool1, avg_pool1, identity1])
+    if kspo[3] in ["max"]:
+        conv_in = max_pool1
+    elif kspo[3] in ["avg"]:
+        conv_in = avg_pool1
+    elif kspo[3] in [None]:
+        conv_in = identity1
+    else:
+        raise Exception("wrong operation")
+
+    kernelsize = 3 if kernel3x3 else 1  # else 1x1
     weight1 = Tensor(
         "w1",
-        (Int(conv_in.shape()[1] * expand_ratio), Int(conv_in.shape()[1]), 1, 1),
+        (Int(channels), Int(conv_in.shape()[1]), kernelsize, kernelsize),
         axis=["O", "I", "kH", "kW"],
         grad=True,
     )
     weight1_quantized = quantize_weight(weight1)
-    conv1 = conv2d(conv_in, weight1_quantized)
-    bn1 = batch_norm(conv1)
-    relu1 = relu(bn1)
-
-    weight2 = Tensor(
-        "w2",
-        (Int(relu1.shape()[1] / reduce_ratio), relu1.shape()[1], 3, 3),
+    bias1 = Tensor(
+        "b1",
+        (Int(channels)),
         axis=["O", "I", "kH", "kW"],
         grad=True,
     )
-    weight2_quantized = quantize_weight(weight2)
-    conv2 = conv2d(relu1, weight2_quantized)
-    bn2 = batch_norm(conv2)
-    relu2 = relu(bn2)
-
-    return relu2
-
-
-def search_space(name, input, num_classes=10, max_blocks=9):
-    expand_ratio = IntScalarParameter(1, 3, name="expand_ratio")
-    reduce_ratio = IntScalarParameter(1, 3, name="reduce_ratio")
+    bias1_quantized = quantize_weight(bias1)
+    conv1 = conv2d(conv_in, weight1_quantized, bias1_quantized)
 
-    depth = IntScalarParameter(0, max_blocks, name="depth")
-    input_channels = IntScalarParameter(8, 64, name="input_channels", step_size=8)
-
-    weight = Tensor(
-        "w1",
-        (input_channels, input.shape()[1], 3, 3),
-        axis=["O", "I", "kH", "kW"],
-        grad=True,
+    bn1 = batch_norm(conv1)
+    bn1 = conv1
+    act1 = quantize_activation(relu(bn1))
+
+    return act1
+
+
+@search_space
+def ai8x_search_space(name, input, num_classes=10, max_blocks=9, rng=None):
+    kernel3x3 = CategoricalParameter([True, False], name="kernel3x3", rng=rng)
+    pool_kernel = CategoricalParameter([2], name="pool_kernel", rng=rng)
+    pool_stride = CategoricalParameter([1, 2], name="pool_stride", rng=rng)
+    pool_pad = CategoricalParameter([0], name="pool_pad", rng=rng)  # has to stay 0
+    pool_op = CategoricalParameter(["max"], name="pool_op", rng=rng)  # , "avg", None
+    channels = CategoricalParameter(
+        # single passes = 1 to 64
+        # [i for i in range(1, 64 + 1)],
+        # multipasses = 1*64, 2*64, 3*64, 4*64
+        # + [j * i for j in range(1, 4 + 1) for i in range(64, 64 + 1)],
+        [2**i for i in range(6 + 1)],  # 1,2,4,8,16,32,64
+        name="channels",
+        rng=rng,
     )
-    weight = quantize_weight(weight)
+    max_blocks = min(max_blocks, 4)
+    depth = IntScalarParameter(0, max_blocks, name="depth", rng=rng)
+    input_channels = IntScalarParameter(
+        8, 64, name="input_channels", step_size=8, rng=rng
+    )
+
+    assert (
+        len(input.shape()) == 4
+    ), f"a shape of [1, C, W, H] is required, and {input.shape()} was provided"
+    assert input.shape()[0] == 1, "it is required to have shape[0] == 1"
 
-    out = conv2d(input, weight)
+    out = block(input, channels.new(), kernel3x3.new(), (2, 2, 1, "max"))
 
     blocks = []
+    channelslist = []
     for i in range(max_blocks + 1):
-        out = block(out, expand_ratio.new(), reduce_ratio.new())
+        channelslist.append(channels.new())
+        kspo = (pool_kernel.new(), pool_stride.new(), 1, pool_op.new())
+        out = block(out, channelslist[-1], kernel3x3.new(), kspo)
+        max_shape = out.shape()[1] * out.shape()[2] * out.shape()[3]
+        out.cond(
+            max_shape > 0 and max_shape <= 1024,
+            allowed_params=[
+                p for k, p in out.parametrization().items() if p.name in ["channel"]
+            ]
+            + [kspo[0], kspo[1], kspo[3], kernel3x3],
+        )
         blocks.append(out)
 
     out = dynamic_depth(*blocks, switch=depth)
 
+    # maybe fix input = output channels so that izer wont complain
+    out = block(out, num_classes, True, (2, 2, 0, None))
+
     linear_weight = Tensor(
-        "w2",
+        "lin_weight_w2",
         (out.shape()[1] * out.shape()[2] * out.shape()[3], num_classes),
         axis=["O", "I"],
         grad=True,
@@ -144,4 +188,13 @@ def search_space(name, input, num_classes=10, max_blocks=9):
     linear_weight = quantize_weight(linear_weight)
     out = linear(out, linear_weight)
 
+    out.cond(
+        out.shape()[0] < 1025 and out.shape()[0] > 0,
+        allowed_params=[
+            p
+            for k, p in out.parametrization().items()
+            if p.name in ["channels", "depth"]
+        ],
+    )
+
     return out
diff --git a/hannah/models/ai8x/models_simplified.py b/hannah/models/ai8x/models_simplified.py
new file mode 100644
index 00000000..0ba99895
--- /dev/null
+++ b/hannah/models/ai8x/models_simplified.py
@@ -0,0 +1,148 @@
+#
+# Copyright (c) 2024 Hannah contributors.
+#
+# This file is part of hannah.
+# See https://github.com/ekut-es/hannah for further info.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""A search space based on the cifar 10 NASNet search space for ai85x devices from: htt"""
+
+from hannah.nas.expressions.types import Int
+from hannah.nas.functional_operators.op import ChoiceOp, Tensor, scope, search_space
+from hannah.nas.functional_operators.operators import (
+    BatchNorm,
+    Conv2d,
+    Identity,
+    Linear,
+    AvgPooling,
+    MaxPooling,
+    Relu,
+    Requantize,
+)
+from hannah.nas.parameters.parameters import (
+    CategoricalParameter,
+    FloatScalarParameter,
+    IntScalarParameter,
+)
+
+
+def max_pool(input, kernel_size=2, stride=2, padding=2):
+    return MaxPooling(kernel_size=kernel_size, stride=stride, padding=padding)(input)
+
+
+def avg_pool(input, kernel_size=2, stride=2, dilation=1):
+    return AvgPooling(kernel_size=kernel_size, stride=stride, dilation=dilation)(input)
+
+
+def conv2d(input, weight, bias=None, stride=1, dilation=1, groups=1, padding=None):
+    return Conv2d(stride=stride, dilation=dilation, groups=groups, padding=padding)(
+        input, weight
+    )
+
+
+def relu(input):
+    return Relu()(input)
+
+
+def linear(input, weight):
+    return Linear()(input, weight)
+
+
+def identity(input):
+    return Identity()(input)
+
+
+def choice(input, choices, switch=None):
+    return ChoiceOp(*choices, switch=switch)(input)
+
+
+def dynamic_depth(*exits, switch):
+    return ChoiceOp(*exits, switch=switch)()
+
+
+def batch_norm(input):
+    n_chans = input.shape()[1]
+    running_mu = Tensor(name="running_mean", shape=(n_chans,), axis=("c",))
+    running_std = Tensor(name="running_std", shape=(n_chans,), axis=("c",))
+    return BatchNorm()(input, running_mu, running_std)
+
+
+def quantize_weight(weight):
+    # weight = Requantize()(weight)
+    return weight
+
+
+def quantize_activation(activation):
+    # activation = Requantize()(activation)
+    return activation
+
+
+@scope
+def block(input, channels: int, kernel_size: int):
+    max_pool1 = max_pool(input, kernel_size=2, stride=2, padding=0)
+
+    weight = Tensor(
+        "w1",
+        (Int(channels), Int(max_pool1.shape()[1]), kernel_size, kernel_size),
+        axis=["O", "I", "kH", "kW"],
+        grad=True,
+    )
+    weight_quantized = quantize_weight(weight)
+    bias = Tensor(
+        "b1",
+        (Int(channels)),
+        axis=["O", "I", "kH", "kW"],
+        grad=True,
+    )
+    bias_quantized = quantize_weight(bias)
+    conv = conv2d(max_pool1, weight_quantized, bias_quantized)
+    act = quantize_activation(relu(conv))
+
+    return act
+
+
+@search_space
+def ai8x_search_space(name, input, num_classes=10, max_blocks=9, rng=None):
+    # do exactly 5 blocks so we get an input shape [1,1] for linear
+    max_blocks = 5  # min(max_blocks, 4)
+    depth = IntScalarParameter(0, max_blocks - 1, name="depth", rng=rng)
+    channel_multiplier = IntScalarParameter(
+        1, 16, name="channels", step_size=1, rng=rng
+    )
+
+    assert (
+        len(input.shape()) == 4
+    ), f"a shape of [1, C, W, H] is required, and {input.shape()} was provided"
+    assert input.shape()[0] == 1, "it is required to have shape[0] == 1"
+
+    out = input
+
+    blocks = []
+    for i in range(max_blocks):
+        out = block(out, i + 1 * channel_multiplier, 3)
+
+        blocks.append(out)
+
+    # out = dynamic_depth(*blocks, switch=depth)
+
+    linear_weight = Tensor(
+        "lin_weight_w2",
+        (out.shape()[1] * out.shape()[2] * out.shape()[3], num_classes),
+        axis=["O", "I"],
+        grad=True,
+    )
+    linear_weight = quantize_weight(linear_weight)
+    out = linear(out, linear_weight)
+
+    return out
diff --git a/hannah/nas/export/onnx.py b/hannah/nas/export/onnx.py
index 48cbcdf6..886ba441 100644
--- a/hannah/nas/export/onnx.py
+++ b/hannah/nas/export/onnx.py
@@ -130,13 +130,14 @@ def do_eval(exp):
 
 def to_onnx(model: Union[BasicExecutor, Op], filename: str = "") -> onnx.ModelProto:
     if isinstance(model, BasicExecutor):
-        worklist = [(model.output, False)]
-    else:
-        worklist = [(model, False)]
+        model = model.output
+
+    worklist = [(model, False)]
 
     visited = set()
 
     input_tensors = []
+    intializer_tensors = []
 
     node_cache = {}
     while worklist:
@@ -147,9 +148,13 @@ def to_onnx(model: Union[BasicExecutor, Op], filename: str = "") -> onnx.ModelPr
             visited.add(node)
             if isinstance(node, Tensor):
                 res = spox.argument(
-                    spox.Tensor(eval(node.dtype).as_numpy(), eval(node.shape()))
+                    spox.Tensor(eval(node.dtype).as_numpy(), eval(node.shape())),
                 )
                 input_tensors.append(node)
+                if node.grad:
+                    # create an initializer instead of input
+                    # call hannah.nas
+                    intializer_tensors.append(node)
             elif isinstance(node, Relu):
                 res = op.relu(node_cache[node.operands[0]])
             elif isinstance(node, Conv1d):
@@ -160,6 +165,9 @@ def to_onnx(model: Union[BasicExecutor, Op], filename: str = "") -> onnx.ModelPr
                     group=eval(node.groups),
                     pads=triple(eval(node.padding)),
                     strides=single(eval(node.stride)),
+                    kernel_shape=pair(
+                        eval(node.kernel_size)
+                    ),  # its weird that it is a pair?
                 )
             elif isinstance(node, Conv2d):
                 res = op.conv(
@@ -169,6 +177,7 @@ def to_onnx(model: Union[BasicExecutor, Op], filename: str = "") -> onnx.ModelPr
                     group=eval(node.groups),
                     pads=quadruple(eval(node.padding)),
                     strides=pair(eval(node.stride)),
+                    kernel_shape=pair(eval(node.kernel_size)),
                 )
             elif isinstance(node, Linear):
                 input_shape = eval(node.operands[0].shape_fun())
@@ -223,7 +232,7 @@ def to_onnx(model: Union[BasicExecutor, Op], filename: str = "") -> onnx.ModelPr
                 mean = node_cache[node.operands[1]]
                 var = node_cache[node.operands[2]]
 
-                print(mean, var)
+                # print(mean, var)
 
                 epsilon = 1e-5
 
@@ -323,4 +332,9 @@ def to_onnx(model: Union[BasicExecutor, Op], filename: str = "") -> onnx.ModelPr
     outputs = {}
     outputs[model.id] = node_cache[model]
 
+    initializers = {}
+    for idx, node in enumerate(intializer_tensors):
+        node_id = f"{node.id}_{idx}"
+        initializers[node_id] = node_cache[node]
+
     return spox.build(inputs=inputs, outputs=outputs)
diff --git a/hannah/nas/functional_operators/executor.py b/hannah/nas/functional_operators/executor.py
index cc9aadd1..454467be 100644
--- a/hannah/nas/functional_operators/executor.py
+++ b/hannah/nas/functional_operators/executor.py
@@ -1,14 +1,33 @@
+#
+# Copyright (c) 2024 Hannah contributors.
+#
+# This file is part of hannah.
+# See https://github.com/ekut-es/hannah for further info.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 from copy import deepcopy
 from typing import Iterator, Tuple
 import math
 import torch
+from torch.nn.modules.module import Module
 from hannah.nas.functional_operators.op import ChoiceOp, Op, Tensor, get_nodes
 from collections import defaultdict
 from torch.nn.parameter import Parameter
 
 
 class BasicExecutor(torch.nn.Module):
-    def __init__(self, net, input_node_name='input', init=None) -> None:
+    def __init__(self, net, input_node_name="input", init=None) -> None:
         super().__init__()
         self.output = deepcopy(net)
         self.input_node_name = input_node_name
@@ -23,6 +42,13 @@ def __init__(self, net, input_node_name='input', init=None) -> None:
 
     def initialize(self):
         self.find_execution_order()
+        self.register_modules()
+
+    def register_modules(self) -> None:
+        for node in self.nodes:
+            op = self.node_dict[node]
+            if isinstance(op, torch.nn.Module):
+                self.add_module(node.replace(".", "_"), op)
 
     def initialize_tensor(self, node):
         if isinstance(node, Tensor):
@@ -45,16 +71,16 @@ def initialize_tensor(self, node):
                     self.register_parameter(node_name, data)
             if node.name == self.input_node_name:
                 self.input = node
-            if node.name == 'running_mean':
+            if node.name == "running_mean":
                 data = torch.zeros(node.current_shape())
                 self.register_buffer(node_name, data)
-            if node.name == 'running_std':
+            if node.name == "running_std":
                 data = torch.ones(node.current_shape())
                 self.register_buffer(node_name, data)
             node.executor = self
 
     def get_data(self, id):
-        if id == 'input':
+        if id == "input":
             return self.input_data
         else:
             name = id.replace(".", "_")
@@ -139,15 +165,17 @@ def find_execution_order(self):
                 queue = [node] + queue
             else:
                 nodes.append(node)
-                dependency_dict = self.remove_from_dependency_dict(dependency_dict, node)
+                dependency_dict = self.remove_from_dependency_dict(
+                    dependency_dict, node
+                )
         self.nodes = nodes
-        self.nodes.remove('input')
+        self.nodes.remove("input")
 
     def find_active_modules(self):
         pass
 
     def forward(self, x):
-        out = {'input': x}
+        out = {"input": x}
         for node in self.nodes:
             operands = [out[n] for n in self.forward_dict[node]]
             out[node] = self.node_dict[node].forward(*operands)
diff --git a/hannah/nas/functional_operators/op.py b/hannah/nas/functional_operators/op.py
index 59cb4397..86de194c 100644
--- a/hannah/nas/functional_operators/op.py
+++ b/hannah/nas/functional_operators/op.py
@@ -85,12 +85,15 @@ def get_highest_scope_counter(scope, scope_dict):
 
 def scope(function):
     """Decorator defining a scope in a search space. The id of every subcomponent (operators or lower-hierarchy scopes)
-       enclosed in a function decorated with this will be prefixed with the name of the function, creating a
-       hierarchical scope.
+    enclosed in a function decorated with this will be prefixed with the name of the function, creating a
+    hierarchical scope.
     """
+
     @wraps(function)
     def set_scope(*args, **kwargs):
-        assert "global_scope_stack" in globals(), "No scope tracking found, did you wrap the search space with @search_space?"
+        assert (
+            "global_scope_stack" in globals()
+        ), "No scope tracking found, did you wrap the search space with @search_space?"
 
         inputs = [a for a in args if isinstance(a, (Op, Tensor))] + [
             a for k, a in kwargs.items() if isinstance(a, (Op, Tensor))
@@ -110,9 +113,10 @@ def set_scope(*args, **kwargs):
 
 def search_space(function):
     """Decorator to define a search space. For correct scoping,
-       a search space containing functional ops must be enclosed by
-       a function decorated with @search_space.
+    a search space containing functional ops must be enclosed by
+    a function decorated with @search_space.
     """
+
     @wraps(function)
     def search_space_limits(*args, **kwargs):
         global global_scope_stack
@@ -125,7 +129,7 @@ def search_space_limits(*args, **kwargs):
 
 
 @parametrize
-class Op:
+class Op(torch.nn.Module):
     def __init__(self, name, *args, **kwargs) -> None:
         super().__init__()
         self.operands = []
@@ -142,7 +146,9 @@ def __call__(self, *operands) -> Any:
 
         # Some Ops (ChoiceOp) can be called multiple times and already have a counter
         if not len(self.id.split(".")[-1].split("_")) > 1:
-            assert "global_scope_stack" in globals(), "No scope tracking found, did you wrap the search space with @search_space?"
+            assert (
+                "global_scope_stack" in globals()
+            ), "No scope tracking found, did you wrap the search space with @search_space?"
             global global_scope_stack
             ct = get_highest_scope_counter(self.name, global_scope_stack[-1])
             self.id = f"{self.id}_{ct}"
@@ -186,8 +192,7 @@ def shape(self):
         return self._shape
 
     @abstractmethod
-    def _forward_implementation(self, *operands):
-        ...
+    def _forward_implementation(self, *operands): ...
 
     def shape_fun(self):
         raise NotImplementedError
@@ -328,7 +333,9 @@ def _connect_options(self, *operands):
             self.options[i] = node_opt(*operands)
             if is_parametrized(self.options[i]):
                 self._PARAMETERS[self.options[i].id] = self.options[i]  # FIXME:
-        assert "global_scope_stack" in globals(), "No scope tracking found, did you wrap the search space with @search_space?"
+        assert (
+            "global_scope_stack" in globals()
+        ), "No scope tracking found, did you wrap the search space with @search_space?"
         global global_scope_stack
         ct = get_highest_scope_counter(self.name, global_scope_stack[-1])
         self.id = f"{self.id}_{ct}"
diff --git a/hannah/nas/functional_operators/operators.py b/hannah/nas/functional_operators/operators.py
index 79d42720..a908512c 100644
--- a/hannah/nas/functional_operators/operators.py
+++ b/hannah/nas/functional_operators/operators.py
@@ -194,6 +194,11 @@ def relu_linear_attention(q, k, v, num_heads, d_model, *, id):
     return out
 
 
+@torch.fx.wrap
+def quantize(input, quantizer):
+    return quantizer(input)
+
+
 @torch.fx.wrap
 def reshape(input, new_shape):
     return input.view(new_shape)
diff --git a/hannah/nas/graph_conversion.py b/hannah/nas/graph_conversion.py
index 568d8827..3915bc92 100644
--- a/hannah/nas/graph_conversion.py
+++ b/hannah/nas/graph_conversion.py
@@ -62,6 +62,7 @@ class GraphConversionTracer(SearchSpaceTracer):
         f_ops.SelfAttention2d,
         f_ops.ReluLinearAttention,
         Tensor,
+        torch.ao.quantization.fake_quantize.FakeQuantize,
     ]
 
     def is_leaf_module(self, module, module_qualified_name):
@@ -127,6 +128,7 @@ def __init__(self, module, garbage_collect_values=True):
             "dropout": self.add_nodes_dropout,
             "self_attention2d": self.add_nodes_attn2d,
             "relu_linear_attention": self.add_nodes_attn2d,
+            torch.ao.quantization.fake_quantize.FakeQuantize: self.add_nodes_quantize,
         }
         self.layer_encodings = [
             "conv",
@@ -143,6 +145,9 @@ def __init__(self, module, garbage_collect_values=True):
         self.func_num = 0
 
     def extract_quant_attrs(self, quantizer):
+        if isinstance(quantizer, torch.ao.quantization.fake_quantize.FakeQuantize):
+            return {"dtype": "int", "bits": 8, "method": "symmetric"}
+
         if quantizer:
             quant_attrs = {
                 "dtype": quantizer.dtype,
@@ -164,6 +169,7 @@ def extract_input_attrs(self, args):
 
     def add_nodes_quantize(self, target, mod, args, kwargs, output):
         type_onehot = to_one_hot("quantize", self.layer_encodings)
+
         quant_attrs = self.extract_quant_attrs(mod)
 
         input_attrs = self.extract_input_attrs(args)
diff --git a/hannah/nas/search/search.py b/hannah/nas/search/search.py
index 098f2f4f..ecf0c585 100644
--- a/hannah/nas/search/search.py
+++ b/hannah/nas/search/search.py
@@ -111,6 +111,7 @@ def __init__(
         bounds=None,
         total_candidates=100,
         num_selected_candidates=10,
+        constrained_sampling_on_search=False,
         *args,
         **kwargs,
     ) -> None:
@@ -120,6 +121,7 @@ def __init__(
         self.bounds = bounds
         self.total_candidates = total_candidates
         self.num_selected_candidates = num_selected_candidates
+        self.constrained_sampling_on_search = constrained_sampling_on_search
 
     def before_search(self):
         self.initialize_dataset()
@@ -165,7 +167,7 @@ def init_candidates(self):
                 remaining_candidates,
                 remaining_candidates,
                 presample=self.presample,
-                constrain=False,
+                constrain=False,  # FIXME: shouldn't this be true?
             )
 
     def search(self):
@@ -195,6 +197,7 @@ def search(self):
                         self.total_candidates,
                         self.num_selected_candidates,
                         presample=self.presample,
+                        constrain=self.constrained_sampling_on_search,
                     )
 
                 while len(self.worklist) < self.n_jobs and len(self.candidates) > 0:
@@ -251,12 +254,9 @@ def sample_candidates(
         while len(candidates) < num_total:
             parameters = self.sample(constrain)
             model = self.build_model(parameters)
-            try:
-                estimated_metrics, satisfied_bounds = self.estimate_metrics(
-                    copy.deepcopy(model)
-                )
-            except:
-                msglogger.critical("Could not estimate performance for model")
+            estimated_metrics, satisfied_bounds = self.estimate_metrics(
+                copy.deepcopy(model)
+            )
             if presample:
                 if not self.presampler.check(model, estimated_metrics):
                     skip_ct += 1
diff --git a/hannah/nas/test/test_max78000_backend.py b/hannah/nas/test/test_max78000_backend.py
new file mode 100644
index 00000000..f60d3c8e
--- /dev/null
+++ b/hannah/nas/test/test_max78000_backend.py
@@ -0,0 +1,179 @@
+#
+# Copyright (c) 2024 Hannah contributors.
+#
+# This file is part of hannah.
+# See https://github.com/ekut-es/hannah for further info.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import warnings
+from collections import namedtuple
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import onnx
+import pytest
+import torch
+
+from hannah.backends import GRPCBackend
+from hannah.backends.base import (
+    ClassifierModule,
+    InferenceBackendBase,
+    ProfilingResult,
+)
+from hannah.models.ai8x.models_simplified import ai8x_search_space
+from hannah.models.embedded_vision_net.models import embedded_vision_net, search_space
+from hannah.nas.constraints.random_walk import RandomWalkConstraintSolver
+from hannah.nas.export import to_onnx
+from hannah.nas.functional_operators.op import ChoiceOp, Tensor, scope
+from hannah.nas.functional_operators.operators import Conv2d, MaxPooling, Relu
+from hannah.nas.parameters import IntScalarParameter
+from hannah.nas.parameters.parametrize import set_parametrization
+from hannah.nas.search.sampler.random_sampler import RandomSampler
+
+
+class MockModule(torch.nn.Module): ...
+
+
+def get_random_tensor_from_onnx(onnxmodel: onnx.ModelProto):
+    # Check if model has any inputs
+    if not onnxmodel.graph.input:
+        raise ValueError("ONNX model has no inputs")
+
+    # Get first input's shape, handling potential None/dynamic dimensions
+    shape = []
+    for dim in onnxmodel.graph.input[0].type.tensor_type.shape.dim:
+        # Use dim_value if available, else default to 1 (or whatever default you want)
+        shape.append(dim.dim_value if dim.dim_value else 1)
+
+    return torch.randn(shape)
+
+
+class SimpleTestModule(ClassifierModule):
+    """Simple test module for the backends"""
+
+    def __init__(self, model=None, onnxmodel=None):
+        if model is None:
+            magicmodel = MagicMock(spec=MockModule)
+            magicmodel.onnx = onnxmodel
+            super().__init__(None, model=magicmodel)
+        else:
+            super().__init__(None, model=model)
+
+    def forward(self, x):
+        return self.model(x)
+
+    def get_example_input_array(self):
+        return self.example_input_array
+
+    def setup(self, stage):
+        self.example_input_array = get_random_tensor_from_onnx(self.model.onnx)
+
+    def prepare_data(self):
+        pass
+
+    def get_class_names(self):
+        return ["test"]
+
+
+def get_graph(seed):
+    input = Tensor("input", (1, 3, 32, 32), axis=["N", "C", "H", "W"], grad=False)
+    graph = ai8x_search_space(
+        "test", input, num_classes=10, max_blocks=1, rng=np.random.default_rng(seed)
+    )
+    print(graph)
+
+    torch.random.manual_seed(seed)
+
+    warnings.warn("remove this when seedable randomsampling works")
+
+    print("Init sampler")
+    sampler = RandomSampler(None, graph.parametrization(flatten=True))
+
+    print("Init solver")
+    solver = RandomWalkConstraintSolver()
+
+    print("Sampling:", end="")
+    # for _ in range(10):
+    for i in range(1):
+        print(".", end="")
+
+        sampled_params, keys = sampler.next_parameters()
+
+        solver.solve(graph, sampled_params)
+
+        parameters = solver.get_constrained_params(sampled_params)
+
+        set_parametrization(parameters, graph.parametrization(flatten=True))
+    return graph
+
+
+def get_onnxmodel(seed):
+    graph = get_graph(seed)
+    onnx_model = to_onnx(graph)
+    return onnx_model
+
+
+def our_to_onnx(model: "type", filename: str = "") -> onnx.ModelProto:
+    if hasattr(model, "onnx"):
+        return model.onnx
+    else:
+        return to_onnx(model, filename)
+
+
+@pytest.mark.xfail(reason="need to fix onnx model generation")
+@pytest.mark.parametrize(
+    "modelname, test_module",
+    [
+        # ("VirtualDevice", SimpleTestModule(model=get_graph())), # does not work for some reason
+        ("VirtualDevice", SimpleTestModule(onnxmodel=get_onnxmodel(56))),
+        # ("EVKit_V1", SimpleTestModule(onnxmodel=get_onnxmodel(56))), # does not work without a device
+    ],
+)
+def test_virtual_device_profiling(modelname, test_module):
+    test_module.prepare_data()
+    test_module.setup("fit")
+
+    x = test_module.get_example_input_array()
+
+    backend = GRPCBackend(device_filter=[{"model": modelname}])
+    with patch("hannah.backends.grpc.to_onnx", our_to_onnx):
+        backend.prepare(test_module)
+    result = backend.profile(x)
+
+    ref = test_module(x)
+
+    assert len(result.outputs) > 0, "did not return results"
+
+    assert result.metrics["us_per_all"] is not None
+
+
+@pytest.mark.parametrize(
+    "seed",
+    [],  # range(57) # dont need to find a seed everytime
+)
+def test_search_seed(seed):
+    modelname, test_module = (
+        "VirtualDevice",
+        SimpleTestModule(onnxmodel=get_onnxmodel(seed)),
+    )
+    test_module.prepare_data()
+    test_module.setup("fit")
+
+    x = test_module.get_example_input_array()
+
+    backend = GRPCBackend(device_filter=[{"model": modelname}])
+    with patch("hannah.backends.grpc.to_onnx", our_to_onnx):
+        backend.prepare(test_module)
+    result = backend.profile(x)
+    assert result.profile != dict(), "seed does not work"
diff --git a/hannah/nas/test/test_onnx_export.py b/hannah/nas/test/test_onnx_export.py
index c3e8d9f3..b7f45be9 100644
--- a/hannah/nas/test/test_onnx_export.py
+++ b/hannah/nas/test/test_onnx_export.py
@@ -18,10 +18,12 @@
 #
 import copy
 import time
+from pathlib import Path
 
 import onnx
 
 from hannah.models.embedded_vision_net.models import embedded_vision_net, search_space
+from hannah.models.ai8x.models import ai8x_search_space
 from hannah.nas.constraints.random_walk import RandomWalkConstraintSolver
 from hannah.nas.export import to_onnx
 from hannah.nas.functional_operators.op import ChoiceOp, Tensor, scope
@@ -129,8 +131,53 @@ def test_export_embedded_vision_net():
     print("Done")
 
 
+def test_export_ai8x_net():
+    print("Start time: ", time.time())
+
+    print("Init search space")
+    input = Tensor("input", (1, 3, 32, 32), axis=["N", "C", "H", "W"], grad=False)
+
+    graph = ai8x_search_space("test", input, num_classes=10, max_blocks=1)
+    print(graph)
+
+    print("Init sampler")
+    sampler = RandomSampler(None, graph.parametrization(flatten=True))
+
+    print("Init solver")
+    solver = RandomWalkConstraintSolver()
+
+    print("Sampling:", end="")
+    # for _ in range(10):
+    for i in range(1):
+        print(".", end="")
+
+        sampled_params, keys = sampler.next_parameters()
+
+        solver.solve(graph, sampled_params)
+
+        parameters = solver.get_constrained_params(sampled_params)
+
+        set_parametrization(parameters, graph.parametrization(flatten=True))
+
+        onnx_model = to_onnx(graph)
+
+        this_dir = Path(
+            __file__
+        ).parent.parent.parent.parent  # hannah/nas/test/../../../../ => /
+        path = this_dir / f"ai8x_net_{i}_20240907.onnx"
+
+        print(onnx.printer.to_text(onnx_model))
+
+        print("saving to location:", path)
+        onnx.save(onnx_model, path)
+
+    print("")
+    print("Done")
+
+
 if __name__ == "__main__":
-    test_deepcopy()
-    test_export_choice()
-    test_export_conv2d()
-    test_export_embedded_vision_net()
+    # test_deepcopy()
+    # test_export_choice()
+    # test_export_conv2d()
+    # test_export_embedded_vision_net()
+    test_export_ai8x_net()
diff --git a/hannah/nas/test/test_searchspace_to_graph.py b/hannah/nas/test/test_searchspace_to_graph.py
new file mode 100644
index 00000000..1935d701
--- /dev/null
+++ b/hannah/nas/test/test_searchspace_to_graph.py
@@ -0,0 +1,75 @@
+#
+# Copyright (c) 2024 Hannah contributors.
+#
+# This file is part of hannah.
+# See https://github.com/ekut-es/hannah for further info.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import pytest
+import torch
+
+
+from hannah.models.ai8x.models import ai8x_search_space
+from hannah.models.embedded_vision_net.models import embedded_vision_net
+from hannah.models.conv_vit.models import conv_vit
+from hannah.nas.functional_operators.op import Tensor
+from hannah.nas.graph_conversion import model_to_graph
+from hannah.nas.functional_operators.executor import BasicExecutor
+
+from hannah.nas.performance_prediction.features.dataset import (
+    get_features,
+    to_dgl_graph,
+)
+
+from hannah.nas.parameters.parametrize import set_parametrization
+from hannah.nas.search.sampler.random_sampler import RandomSampler
+from hannah.nas.constraints.random_walk import RandomWalkConstraintSolver
+from flaky import flaky
+
+
+@flaky(max_runs=5)
+@pytest.mark.parametrize("model", [ai8x_search_space, embedded_vision_net])
+def test_model_conversion(model):
+    input = Tensor(name="input", shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
+    model = model("test", input, 10)
+    model.sample()
+
+    print("Init sampler")
+    sampler = RandomSampler(None, model.parametrization(flatten=True))
+
+    print("Init solver")
+    solver = RandomWalkConstraintSolver()
+
+    sampled_params, keys = sampler.next_parameters()
+
+    solver.solve(model, sampled_params)
+
+    parameters = solver.get_constrained_params(sampled_params)
+
+    set_parametrization(parameters, model.parametrization(flatten=True))
+
+    executor = BasicExecutor(model)
+    executor.initialize()
+
+    x = torch.ones(input.shape())
+
+    nx_graph = model_to_graph(executor, x)
+    fea = get_features(nx_graph)
+    for i, n in enumerate(nx_graph.nodes):
+        nx_graph.nodes[n]["features"] = fea.iloc[i].to_numpy()
+    dgl_graph = to_dgl_graph(nx_graph)
+
+
+if __name__ == "__main__":
+    test_model_conversion(ai8x_search_space)
diff --git a/poetry.lock b/poetry.lock
index 5442301e..539243f0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "about-time"
@@ -24,113 +24,98 @@ files = [
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.0"
+version = "2.3.5"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
-    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+    {file = "aiohappyeyeballs-2.3.5-py3-none-any.whl", hash = "sha256:4d6dea59215537dbc746e93e779caea8178c866856a721c9c660d7a5a7b8be03"},
+    {file = "aiohappyeyeballs-2.3.5.tar.gz", hash = "sha256:6fa48b9f1317254f122a07a131a86b71ca6946ca989ce6326fff54a99a920105"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.10.5"
+version = "3.10.2"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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+    {file = "aiohttp-3.10.2.tar.gz", hash = "sha256:4d1f694b5d6e459352e5e925a42e05bac66655bfde44d81c59992463d2897014"},
 ]
 
 [package.dependencies]
@@ -145,6 +130,19 @@ yarl = ">=1.0,<2.0"
 [package.extras]
 speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
+[[package]]
+name = "aioserial"
+version = "1.3.1"
+description = "An asynchronous serial port library of Python"
+optional = false
+python-versions = ">=3.6,<4.0"
+files = [
+    {file = "aioserial-1.3.1.tar.gz", hash = "sha256:702bf03b0eb84b8ef2d8dac5cb925e1e685dce98f77b125569bc6fd2b3b58228"},
+]
+
+[package.dependencies]
+pyserial = "*"
+
 [[package]]
 name = "aiosignal"
 version = "1.3.1"
@@ -161,29 +159,30 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "albucore"
-version = "0.0.14"
+version = "0.0.13"
 description = "A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "albucore-0.0.14-py3-none-any.whl", hash = "sha256:ae07db5e32bbb2196b67f9e341a92925cc7da42df3c6d57f962f43f77b6d0a8f"},
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+    {file = "albucore-0.0.13-py3-none-any.whl", hash = "sha256:5a00c931ff80149726b80ad0c4c0a8327048463f14cbdf0ca5ff34a051700a84"},
+    {file = "albucore-0.0.13.tar.gz", hash = "sha256:c36d0af878e06f6e97e7882731c5d29243e38c21caec2005ee420fe85db4a7bc"},
 ]
 
 [package.dependencies]
-numpy = ">=1.24"
+numpy = ">=1.24.4,<2"
 opencv-python-headless = ">=4.9.0.80"
-typing-extensions = {version = ">=4.9.0", markers = "python_version < \"3.10\""}
+tomli = ">=2.0.1"
+typing-extensions = ">=4.9.0"
 
 [[package]]
 name = "albumentations"
-version = "1.4.14"
+version = "1.4.13"
 description = "An efficient library for image augmentation, providing extensive transformations to support machine learning and computer vision tasks."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "albumentations-1.4.14-py3-none-any.whl", hash = "sha256:a550bc2e182e4092ab9d914febd9c82a75fed88f0152d077fb323216b0b7ed51"},
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+    {file = "albumentations-1.4.13-py3-none-any.whl", hash = "sha256:fc4f154c7225dcf903f4a0116e7388885af6eb098fa759def76c7ba3e106e721"},
+    {file = "albumentations-1.4.13.tar.gz", hash = "sha256:57b131dccf4d5c45c6ec8f140511f827905f85188464884488643459d702641b"},
 ]
 
 [package.dependencies]
@@ -239,7 +238,7 @@ grapheme = "0.6.0"
 name = "annotated-types"
 version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
@@ -256,6 +255,39 @@ files = [
     {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
 ]
 
+[[package]]
+name = "anyio"
+version = "4.4.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "appdirs"
+version = "1.4.4"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = "*"
+files = [
+    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
+    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
+]
+
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -343,6 +375,17 @@ files = [
     {file = "autopage-0.5.2.tar.gz", hash = "sha256:826996d74c5aa9f4b6916195547312ac6384bac3810b8517063f293248257b72"},
 ]
 
+[[package]]
+name = "backports-strenum"
+version = "1.3.1"
+description = "Base class for creating enumerated constants that are also subclasses of str"
+optional = false
+python-versions = ">=3.8.6,<3.11"
+files = [
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+    {file = "backports_strenum-1.3.1.tar.gz", hash = "sha256:77c52407342898497714f0596e86188bb7084f89063226f4ba66863482f42414"},
+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -435,13 +478,13 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -708,13 +751,13 @@ plotting = ["matplotlib"]
 
 [[package]]
 name = "cmaes"
-version = "0.11.1"
+version = "0.11.0"
 description = "Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cmaes-0.11.1-py3-none-any.whl", hash = "sha256:1de77d2175045389680619c1e9b6d59d90e7888524d9e440e1704ba001de9fcc"},
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+    {file = "cmaes-0.11.0-py3-none-any.whl", hash = "sha256:65f9390fa28536762f33b60ab74a7bf01176b8e0371dca86965fde30fe4d4ae1"},
+    {file = "cmaes-0.11.0.tar.gz", hash = "sha256:580f5e40f25f57153deaf821b2b51b50ed6b598963af3966a50aa42b40609dba"},
 ]
 
 [package.dependencies]
@@ -760,7 +803,7 @@ files = [
 name = "coloredlogs"
 version = "15.0.1"
 description = "Colored terminal output for Python's logging module"
-optional = true
+optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
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@@ -809,87 +852,66 @@ test = ["pytest"]
 
 [[package]]
 name = "contourpy"
-version = "1.3.0"
+version = "1.2.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
-numpy = ">=1.23"
+numpy = ">=1.20"
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
 docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.11.1)", "types-Pillow"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"]
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
-test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
+test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
@@ -978,6 +1000,66 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
+[[package]]
+name = "crc"
+version = "7.0.0"
+description = "Library and CLI to calculate and verify all kinds of CRC checksums"
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
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+]
+
+[[package]]
+name = "cryptography"
+version = "43.0.0"
+description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
+optional = false
+python-versions = ">=3.7"
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+
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+pep8test = ["check-sdist", "click", "mypy", "ruff"]
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+test-randomorder = ["pytest-randomly"]
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -1058,7 +1140,7 @@ files = [
 name = "deprecated"
 version = "1.2.14"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
-optional = true
+optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
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 [[package]]
 name = "executing"
-version = "2.1.0"
+version = "2.0.1"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
-python-versions = ">=3.8"
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 torch = ">=1.8.0"
 
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 [[package]]
 name = "filelock"
 version = "3.15.4"
@@ -1235,7 +1427,7 @@ files = [
 name = "flatbuffers"
 version = "24.3.25"
 description = "The FlatBuffers serialization format for Python"
-optional = true
+optional = false
 python-versions = "*"
 files = [
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@@ -1393,6 +1585,25 @@ files = [
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 ]
 
+[[package]]
+name = "fs"
+version = "2.4.16"
+description = "Python's filesystem abstraction layer"
+optional = false
+python-versions = "*"
+files = [
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+
+[package.dependencies]
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+setuptools = "*"
+six = ">=1.10,<2.0"
+
+[package.extras]
+scandir = ["scandir (>=1.5,<2.0)"]
+
 [[package]]
 name = "fsspec"
 version = "2024.6.1"
@@ -1602,75 +1813,76 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "1.2.0"
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 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
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 colorama = ">=0.4"
 
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 name = "grpcio"
-version = "1.66.1"
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 python-versions = ">=3.8"
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 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.66.1)"]
+protobuf = ["grpcio-tools (>=1.65.4)"]
 
 [[package]]
 name = "h5py"
@@ -1707,13 +1919,13 @@ numpy = ">=1.17.3"
 
 [[package]]
 name = "huggingface-hub"
-version = "0.24.6"
+version = "0.24.5"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = true
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1743,7 +1955,7 @@ typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "t
 name = "humanfriendly"
 version = "10.0"
 description = "Human friendly output for text interfaces using Python"
-optional = true
+optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
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@@ -1845,13 +2057,13 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.8"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.5"
 files = [
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 ]
 
 [[package]]
@@ -1874,13 +2086,13 @@ scipy = ">=1.2.1"
 
 [[package]]
 name = "imageio"
-version = "2.35.1"
+version = "2.34.2"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1888,32 +2100,31 @@ numpy = "*"
 pillow = ">=8.3.2"
 
 [package.extras]
-all-plugins = ["astropy", "av", "imageio-ffmpeg", "psutil", "tifffile"]
-all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
 build = ["wheel"]
 dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
 docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
 ffmpeg = ["imageio-ffmpeg", "psutil"]
 fits = ["astropy"]
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+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
 gdal = ["gdal"]
 itk = ["itk"]
 linting = ["black", "flake8"]
 pillow-heif = ["pillow-heif"]
 pyav = ["av"]
-rawpy = ["numpy (>2)", "rawpy"]
 test = ["fsspec[github]", "pytest", "pytest-cov"]
 tifffile = ["tifffile"]
 
 [[package]]
 name = "importlib-metadata"
-version = "8.4.0"
+version = "8.2.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1926,25 +2137,21 @@ test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "p
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.4"
+version = "6.4.0"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
-type = ["pytest-mypy"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "iniconfig"
@@ -2491,13 +2698,13 @@ testing = ["pytest"]
 
 [[package]]
 name = "markdown"
-version = "3.7"
+version = "3.6"
 description = "Python implementation of John Gruber's Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Markdown-3.7-py3-none-any.whl", hash = "sha256:7eb6df5690b81a1d7942992c97fad2938e956e79df20cbc6186e9c3a77b1c803"},
-    {file = "markdown-3.7.tar.gz", hash = "sha256:2ae2471477cfd02dbbf038d5d9bc226d40def84b4fe2986e49b59b6b472bbed2"},
+    {file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
+    {file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
 ]
 
 [package.dependencies]
@@ -2507,6 +2714,30 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -2578,13 +2809,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.22.0"
+version = "3.21.3"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2592,7 +2823,7 @@ packaging = ">=17.0"
 
 [package.extras]
 dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.13)", "sphinx (==8.0.2)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
+docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
@@ -2611,51 +2842,40 @@ marshmallow = ">=2.0.0"
 
 [[package]]
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-version = "3.9.2"
+version = "3.9.1.post1"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -2687,6 +2907,17 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -2700,13 +2931,13 @@ files = [
 
 [[package]]
 name = "mkdocs"
-version = "1.6.1"
+version = "1.6.0"
 description = "Project documentation with Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2731,13 +2962,13 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "1.2.0"
+version = "1.0.1"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_autorefs-1.2.0-py3-none-any.whl", hash = "sha256:d588754ae89bd0ced0c70c06f58566a4ee43471eeeee5202427da7de9ef85a2f"},
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 ]
 
 [package.dependencies]
@@ -2792,25 +3023,25 @@ mkdocs = ">=1.0.3"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.26.0"
+version = "0.25.2"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 click = ">=7.0"
 importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
 Jinja2 = ">=2.11.1"
-Markdown = ">=3.6"
+Markdown = ">=3.3"
 MarkupSafe = ">=1.1"
 mkdocs = ">=1.4"
-mkdocs-autorefs = ">=1.2"
+mkdocs-autorefs = ">=0.3.1"
 mkdocstrings-python = {version = ">=0.5.2", optional = true, markers = "extra == \"python\""}
-platformdirs = ">=2.2"
+platformdirs = ">=2.2.0"
 pymdown-extensions = ">=6.3"
 typing-extensions = {version = ">=4.1", markers = "python_version < \"3.10\""}
 
@@ -2821,18 +3052,17 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.10.9"
+version = "1.10.7"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-griffe = ">=0.49"
-mkdocs-autorefs = ">=1.0"
+griffe = ">=0.48"
 mkdocstrings = ">=0.25"
 
 [[package]]
@@ -2969,38 +3199,38 @@ dev = ["check-manifest", "wheel"]
 
 [[package]]
 name = "mypy"
-version = "1.11.2"
+version = "1.11.1"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -3036,6 +3266,48 @@ files = [
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 ]
 
+[[package]]
+name = "netdeployonnx"
+version = "0.1.0"
+description = "netDeployONNX"
+optional = false
+python-versions = "^3.9"
+files = []
+develop = true
+
+[package.dependencies]
+aioserial = "^1.3.1"
+anyio = "^4.4.0"
+black = "^24.4.2"
+click = "^8.1.7"
+crc = "^7.0.0"
+fastcrc = "^0.3.0"
+fs = "^2"
+gitpython = "^3.1.43"
+grpcio = "^1.64.1"
+matplotlib = "^3.9.1"
+mypy = "^1.10.1"
+networkx = "^3"
+onnx = "^1.16.1"
+onnxruntime = "^1.18.1"
+progressbar = "^2.5"
+prompt-toolkit = "^3.0.47"
+protobuf = "^5.27.2"
+pydantic = "^2.8.0"
+pygithub = "^2.3.0"
+pyserial-asyncio = "^0.6"
+pytest-grpc = "^0.8.0"
+pyyaml = "^6.0.1"
+rich = "^13.7.1"
+torch = "^2"
+tqdm = "^4.66.5"
+uvloop = "^0.20.0"
+xxhash = "^3"
+
+[package.source]
+type = "directory"
+url = "external/netdeployonnx"
+
 [[package]]
 name = "networkx"
 version = "3.2.1"
@@ -3254,14 +3526,14 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.6.68"
+version = "12.6.20"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -3345,42 +3617,42 @@ reference = ["google-re2", "pillow"]
 
 [[package]]
 name = "onnxruntime"
-version = "1.19.0"
+version = "1.18.1"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
-optional = true
+optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
 coloredlogs = "*"
 flatbuffers = "*"
-numpy = ">=1.21.6"
+numpy = ">=1.21.6,<2.0"
 packaging = "*"
 protobuf = "*"
 sympy = "*"
@@ -3404,10 +3676,10 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
+    {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
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     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
 ]
 
 [[package]]
@@ -3429,10 +3701,10 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
+    {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
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     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
 ]
 
 [[package]]
@@ -3617,13 +3889,13 @@ files = [
 
 [[package]]
 name = "pbr"
-version = "6.1.0"
+version = "6.0.0"
 description = "Python Build Reasonableness"
 optional = false
 python-versions = ">=2.6"
 files = [
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 ]
 
 [[package]]
@@ -3788,13 +4060,13 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "prettytable"
-version = "3.11.0"
+version = "3.10.2"
 description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3803,6 +4075,16 @@ wcwidth = "*"
 [package.extras]
 tests = ["pytest", "pytest-cov", "pytest-lazy-fixtures"]
 
+[[package]]
+name = "progressbar"
+version = "2.5"
+description = "Text progress bar library for Python."
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "prompt-toolkit"
 version = "3.0.47"
@@ -3819,22 +4101,22 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "5.28.0"
+version = "5.27.3"
 description = ""
 optional = false
 python-versions = ">=3.8"
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+    {file = "protobuf-5.27.3-cp39-cp39-win_amd64.whl", hash = "sha256:af7c0b7cfbbb649ad26132e53faa348580f844d9ca46fd3ec7ca48a1ea5db8a1"},
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+    {file = "protobuf-5.27.3.tar.gz", hash = "sha256:82460903e640f2b7e34ee81a947fdaad89de796d324bcbc38ff5430bcdead82c"},
 ]
 
 [[package]]
@@ -3963,7 +4245,7 @@ files = [
 name = "pydantic"
 version = "2.8.2"
 description = "Data validation using Python type hints"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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@@ -3982,7 +4264,7 @@ email = ["email-validator (>=2.0.0)"]
 name = "pydantic-core"
 version = "2.20.1"
 description = "Core functionality for Pydantic validation and serialization"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "pydantic_core-2.20.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3acae97ffd19bf091c72df4d726d552c473f3576409b2a7ca36b2f535ffff4a3"},
@@ -4093,6 +4375,25 @@ files = [
 numpy = "*"
 scipy = "*"
 
+[[package]]
+name = "pygithub"
+version = "2.3.0"
+description = "Use the full Github API v3"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "PyGithub-2.3.0-py3-none-any.whl", hash = "sha256:65b499728be3ce7b0cd2cd760da3b32f0f4d7bc55e5e0677617f90f6564e793e"},
+    {file = "PyGithub-2.3.0.tar.gz", hash = "sha256:0148d7347a1cdeed99af905077010aef81a4dad988b0ba51d4108bf66b443f7e"},
+]
+
+[package.dependencies]
+Deprecated = "*"
+pyjwt = {version = ">=2.4.0", extras = ["crypto"]}
+pynacl = ">=1.4.0"
+requests = ">=2.14.0"
+typing-extensions = ">=4.0.0"
+urllib3 = ">=1.26.0"
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -4107,6 +4408,26 @@ files = [
 [package.extras]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "pyjwt"
+version = "2.9.0"
+description = "JSON Web Token implementation in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "pyjwt-2.9.0.tar.gz", hash = "sha256:7e1e5b56cc735432a7369cbfa0efe50fa113ebecdc04ae6922deba8b84582d0c"},
+]
+
+[package.dependencies]
+cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""}
+
+[package.extras]
+crypto = ["cryptography (>=3.4.0)"]
+dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx", "sphinx-rtd-theme", "zope.interface"]
+docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"]
+tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
+
 [[package]]
 name = "pymdown-extensions"
 version = "10.9"
@@ -4169,15 +4490,41 @@ matplotlib = ">=3"
 numpy = ">=1.15"
 scipy = ">=1.1"
 
+[[package]]
+name = "pynacl"
+version = "1.5.0"
+description = "Python binding to the Networking and Cryptography (NaCl) library"
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "PyNaCl-1.5.0.tar.gz", hash = "sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba"},
+]
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+[package.dependencies]
+cffi = ">=1.4.1"
+
+[package.extras]
+docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
+tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
+
 [[package]]
 name = "pyparsing"
-version = "3.1.4"
+version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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 [package.extras]
@@ -4243,6 +4590,34 @@ files = [
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 ]
 
+[[package]]
+name = "pyserial"
+version = "3.5"
+description = "Python Serial Port Extension"
+optional = false
+python-versions = "*"
+files = [
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+    {file = "pyserial-3.5.tar.gz", hash = "sha256:3c77e014170dfffbd816e6ffc205e9842efb10be9f58ec16d3e8675b4925cddb"},
+]
+
+[package.extras]
+cp2110 = ["hidapi"]
+
+[[package]]
+name = "pyserial-asyncio"
+version = "0.6"
+description = "Python Serial Port Extension - Asynchronous I/O support"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+pyserial = "*"
+
 [[package]]
 name = "pysocks"
 version = "1.7.1"
@@ -4277,6 +4652,24 @@ tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "pytest-asyncio"
+version = "0.24.0"
+description = "Pytest support for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "pytest_asyncio-0.24.0.tar.gz", hash = "sha256:d081d828e576d85f875399194281e92bf8a68d60d72d1a2faf2feddb6c46b276"},
+]
+
+[package.dependencies]
+pytest = ">=8.2,<9"
+
+[package.extras]
+docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
+testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
+
 [[package]]
 name = "pytest-cov"
 version = "5.0.0"
@@ -4295,6 +4688,20 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
+[[package]]
+name = "pytest-grpc"
+version = "0.8.0"
+description = "pytest plugin for grpc"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+pytest = ">=3.6.0"
+
 [[package]]
 name = "pytest-xdist"
 version = "3.6.1"
@@ -4493,120 +4900,120 @@ pyyaml = "*"
 
 [[package]]
 name = "pyzmq"
-version = "26.2.0"
+version = "26.1.0"
 description = "Python bindings for 0MQ"
 optional = false
 python-versions = ">=3.7"
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-type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
+core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "ordered-set (>=3.1.1)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
@@ -5021,6 +5442,17 @@ files = [
 [package.dependencies]
 smmap = ">=3.0.1"
 
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "sortedcontainers"
 version = "2.4.0"
@@ -5054,13 +5486,13 @@ numpy = ["numpy"]
 
 [[package]]
 name = "soupsieve"
-version = "2.6"
+version = "2.5"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -5187,17 +5619,17 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "stevedore"
-version = "5.3.0"
+version = "5.2.0"
 description = "Manage dynamic plugins for Python applications"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-pbr = ">=2.0.0"
+pbr = ">=2.0.0,<2.1.0 || >2.1.0"
 
 [[package]]
 name = "submitit"
@@ -5219,13 +5651,13 @@ dev = ["black (==23.3.0)", "coverage[toml] (>=5.1)", "flit (>=3.5.1)", "isort (=
 
 [[package]]
 name = "sympy"
-version = "1.13.2"
+version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5313,13 +5745,13 @@ files = [
 
 [[package]]
 name = "tifffile"
-version = "2024.8.30"
+version = "2024.7.24"
 description = "Read and write TIFF files"
 optional = true
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -5329,7 +5761,6 @@ numpy = "*"
 all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
 codecs = ["imagecodecs (>=2023.8.12)"]
 plot = ["matplotlib"]
-test = ["cmapfile", "czifile", "dask", "defusedxml", "fsspec", "imagecodecs", "lfdfiles", "lxml", "ndtiff", "oiffile", "psdtags", "pytest", "roifile", "xarray", "zarr"]
 xml = ["defusedxml", "lxml"]
 zarr = ["fsspec", "zarr"]
 
@@ -5683,6 +6114,50 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "uvloop"
+version = "0.20.0"
+description = "Fast implementation of asyncio event loop on top of libuv"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+
 [[package]]
 name = "virtualenv"
 version = "20.26.3"
@@ -5705,41 +6180,43 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "watchdog"
-version = "5.0.0"
+version = "4.0.1"
 description = "Filesystem events monitoring"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
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+    {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
 ]
 
 [package.dependencies]
@@ -6023,13 +6615,13 @@ files = [
 
 [[package]]
 name = "zigzag-dse"
-version = "3.6.1"
+version = "3.6.2"
 description = "ZigZag - Deep Learning Hardware Design Space Exploration"
 optional = true
 python-versions = ">=3.11"
 files = [
-    {file = "zigzag_dse-3.6.1-py3-none-any.whl", hash = "sha256:61ac8d41d7dab0188dde36d0d820510e22184cfba86d5b3b30cc5cd97369b62f"},
-    {file = "zigzag_dse-3.6.1.tar.gz", hash = "sha256:ff0b61bd3bdfbe8b4a2b0acf1708e3cae3dfb49360653a256eb18b77f01205a3"},
+    {file = "zigzag_dse-3.6.2-py3-none-any.whl", hash = "sha256:89c9c2534bd5feaa2b60cbe7a5b8159b578a24b37ff6622048214921388b1b92"},
+    {file = "zigzag_dse-3.6.2.tar.gz", hash = "sha256:0dfc6c92ad0f7c501b81a74b88f9b5bbf2986fb9aeac3091b8e54312b69e3103"},
 ]
 
 [package.dependencies]
@@ -6049,22 +6641,18 @@ dev = ["build", "bumpver", "pip-tools", "pytest", "ruff", "twine", "typeguard"]
 
 [[package]]
 name = "zipp"
-version = "3.20.1"
+version = "3.19.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.20.1-py3-none-any.whl", hash = "sha256:9960cd8967c8f85a56f920d5d507274e74f9ff813a0ab8889a5b5be2daf44064"},
-    {file = "zipp-3.20.1.tar.gz", hash = "sha256:c22b14cc4763c5a5b04134207736c107db42e9d3ef2d9779d465f5f1bcba572b"},
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 onnxrt = ["onnx", "onnxruntime"]
@@ -6075,4 +6663,4 @@ vision = ["albumentations", "gdown", "imagecorruptions", "kornia", "pycocotools"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9 <3.12"
-content-hash = "2cc9bd18bb42ed3fe37a445e9888ca4a8c12d4450d6e1b6bc46ec8e0b25ad70f"
+content-hash = "f65df09b424a6f2e96203b3a03baa31e2ac17d5178869efb54bdb5b2a95e759e"
diff --git a/pyproject.toml b/pyproject.toml
index 963deef9..360be7e8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,6 +64,7 @@ pytorch-lightning = "^2.1.3"
 onnx = "^1.16.0"
 spox = "^0.12.0"
 optree = "^0.11.0"
+netdeployonnx = {path = "external/netdeployonnx", develop = true}
 tensorboard = "^2.17.1"
 pymoo = {version = "^0.6.1.3", optional = true}
 zigzag-dse = {version = "^3.6.1", optional = true, python = ">=3.11"}
@@ -107,6 +108,7 @@ pyre-check = "^0.9.17"
 pytest-xdist = "^3.1.0"
 ipykernel = "^6.29.4"
 ruff = "^0.5.5"
+pytest-asyncio = "^0.24.0"
 
 
 [build-system]
diff --git a/test/test_backend.py b/test/test_backend.py
index b934de88..82120a9b 100644
--- a/test/test_backend.py
+++ b/test/test_backend.py
@@ -33,7 +33,7 @@ def backends():
     """Iterates over the backends"""
     for item in inspect.getmembers(hannah.backends):
         if inspect.isclass(item[1]) and item[0] != "InferenceBackendBase":
-            if item[1].available():
+            if item[1].available() and item[0] != "GRPCBackend":
                 yield item[1]
             else:
                 print(f"Skipping {item[0]} because it is not available")
diff --git a/test/test_graph_conversion_global.py b/test/test_graph_conversion_global.py
new file mode 100644
index 00000000..8dc16d6e
--- /dev/null
+++ b/test/test_graph_conversion_global.py
@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024 Hannah contributors.
+#
+# This file is part of hannah.
+# See https://github.com/ekut-es/hannah for further info.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import networkx as nx
+import torch
+from omegaconf import OmegaConf
+from torch.nn import Module
+
+from hannah.models.factory.pooling import ApproximateGlobalAveragePooling1D
+from hannah.models.factory.qat import ConvBn1d, ConvBnReLU1d, Linear
+from hannah.models.factory.qconfig import get_trax_qat_qconfig
+from hannah.nas.functional_operators.op import Tensor
+from hannah.nas.graph_conversion import model_to_graph
+
+
+class Model(Module):
+    def __init__(self):
+        super().__init__()
+        qconfig = get_trax_qat_qconfig(
+            OmegaConf.create({"bw_w": 6, "bw_b": 8, "bw_f": 8})
+        )
+        self.activation_post_process = qconfig.activation()
+        self.conv = ConvBn1d(16, 24, 9, padding=4, qconfig=qconfig)
+        self.conv_relu = ConvBnReLU1d(24, 32, 3, padding=1, stride=2, qconfig=qconfig)
+        self.skip_conv = ConvBnReLU1d(16, 32, 1, stride=2, qconfig=qconfig)
+        # self.pooling = ApproximateGlobalAveragePooling1D(2, qconfig=qconfig)
+        self.linear = Linear(25, 12, qconfig=qconfig)
+
+    def forward(self, x):
+        x = self.activation_post_process(x)
+        skip = self.skip_conv(x)
+        x = self.conv(x)
+        x = self.conv_relu(x)
+        x = x + skip
+        if hasattr(self, "pooling"):
+            x = self.pooling(x)
+        x = self.linear(x)
+        return x
+
+
+def test_graph_conversion():
+    model = Model()
+
+    test_output = model(torch.randn((1, 16, 50)))
+
+    graph = model_to_graph(model, torch.rand((1, 16, 50), dtype=torch.float32))
+    data = nx.json_graph.node_link_data(graph)
+
+    # graph['metrics'] = {'val_error': 0.03}
+
+    from pprint import pprint
+
+    pprint(data, indent=2)
+
+
+def test_graph_conversion_functional_operators():
+    from hannah.models.embedded_vision_net.models import embedded_vision_net
+    from hannah.nas.functional_operators.executor import BasicExecutor
+    import hannah.nas.functional_operators.operators
+
+    input = Tensor(name="input", shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
+
+    # space = test_net(input)
+
+    space = embedded_vision_net("net", input, num_classes=10)
+    # space.sample()
+    model = BasicExecutor(space)
+    model.initialize()
+    x = torch.rand((1, 3, 32, 32), dtype=torch.float32)
+    # model.forward(x)
+    model.find_execution_order()
+    # model.create_forward()
+    # out = model.forward(x)
+    graph = model_to_graph(model, x)
+    print()
+
+
+if __name__ == "__main__":
+    test_graph_conversion()
+    test_graph_conversion_functional_operators()
diff --git a/test/test_quant_callback.py b/test/test_quant_callback.py
index 5a5a618f..a1e4804e 100644
--- a/test/test_quant_callback.py
+++ b/test/test_quant_callback.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2023 Hannah contributors.
+# Copyright (c) 2024 Hannah contributors.
 #
 # This file is part of hannah.
 # See https://github.com/ekut-es/hannah for further info.
@@ -22,7 +22,7 @@
 from hannah.quantization.callback import QuantizationCallback
 
 
-class TestModel(torch.nn.Module):
+class MyModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
         self.conv = torch.nn.Conv2d(3, 3, 3)
@@ -39,7 +39,7 @@ def forward(self, x):
 def test_quant_callback():
     callback = QuantizationCallback()
 
-    model = TestModel()
+    model = MyModel()
 
     module = ImageClassifierModule(
         None,