Merge branch 'f/mlonmcu_docker' into 'main'

Build mlonmcu docker images

See merge request es/ai/hannah/hannah!384

.github/workflows/publish_images.yml

@@ -0,0 +1,71 @@
+##
+## 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.
+##
+
+#
+name: Create and publish a Docker image
+
+# Configures this workflow to run every time a change is pushed to the branch called `release`.
+on:
+  push:
+    branches:
+      - '*'
+      - '*/*'
+
+# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job.
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      #
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
+      - name: Log in to the Container registry
+        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
+      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository.
+      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.gitlab-ci.yml

@@ -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.
@@ -44,7 +44,7 @@ before_script:
 
   # install and manage dependencies with poetry
   - poetry config installer.max-workers 10
-  - poetry install -E vision
+  - poetry install --all-extras
 
   # Show limits
   - ulimit -a
@@ -133,6 +133,33 @@ test_python_310:
   rules:
     - if: $CI_PIPELINE_SOURCE == "push" && $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
 
+test_python_311:
+  stage: test
+  image: python:3.11
+  script:
+    - set -e
+    - "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
+    - unset PYTHONPATH
+    - unset COVERAGE_PROCESS_START
+    - poetry run coverage report
+    - poetry run coverage xml
+  tags:
+    - docker
+  interruptible: true
+  artifacts:
+    reports:
+      coverage_report:
+        coverage_format: cobertura
+        path: coverage.xml
+  dependencies: []
+  cache:
+    paths:
+      - .cache/
+  rules:
+    - if: $CI_PIPELINE_SOURCE == "push" && $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
 
 
 build_docs:
@@ -215,7 +242,7 @@ deploy to github:
   only:
     - main
     - pub/.*
-  script: 
+  script:
     - env
     - ./scripts/git-push [email protected]:ekut-es/hannah.git $CI_COMMIT_REF_NAME
   tags:

Dockerfile

@@ -17,9 +17,16 @@
 ## limitations under the License.
 ##
 
-FROM ubuntu:22.04
+ARG target="hannah"
 
-RUN  apt update -y && apt -y install git mesa-utils python3-pip python3-dev libblas-dev liblapack-dev libsndfile1-dev libsox-dev cmake ninja-build curl build-essential python-is-python3
+FROM ubuntu:22.04 as hannah
+
+FROM tumeda/mlonmcu-bench:latest as mlonmcu
+
+FROM ${target}
+
+
+RUN  apt-get update -y && apt-get -y install git mesa-utils python3-pip python3-dev libblas-dev liblapack-dev libsndfile1-dev libsox-dev cmake ninja-build curl build-essential python-is-python3
 
 
 # Install poetry using recommended method
@@ -33,4 +40,4 @@ COPY poetry.lock pyproject.toml /deps/
 
 # Install dependencies
 RUN poetry config virtualenvs.create false \
-  && poetry install --no-interaction --no-ansi -E vision --no-root
+  && poetry install --no-interaction --no-ansi --all-extras --no-root

doc/deployment/tensorrt.md

@@ -22,11 +22,8 @@ For deployment on NVIDIA targets we support TensorRT backends.
 Currently the TensorRT backend always compiles for the first GPU of the local system.
 
 ## Installation
-The tensorrt module in the poetry shell needs to be installed seperately via pip:
-```
-poetry shell
-pip install tensorrt
-```
+
+Tensorrt is unfortunately not compatible to poetry installation and must be installed separately `pip install tensorrt`
 
 ## Configuration
 

hannah/models/ai8x/models.py

@@ -27,6 +27,7 @@
     Linear,
     MaxPooling,
     Relu,
+    Requantize,
 )
 from hannah.nas.parameters.parameters import (
     CategoricalParameter,
@@ -65,64 +66,71 @@ def dynamic_depth(*exits, switch):
     return ChoiceOp(*exits, switch=switch)()
 
 
-@scope
 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)
 
 
-@scope
-def block(input):
-    expand_ratio = IntScalarParameter(1, 3, name="expand_ratio")
-    reduce_ratio = IntScalarParameter(1, 3, name="reduce_ratio")
+def quantize_weight(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])
+    # conv_in = choice(input, [max_pool1, identity1])
 
     conv_in = input
 
     weight1 = Tensor(
         "w1",
-        (conv_in.shape()[1], Int(conv_in.shape()[1] * expand_ratio), 1, 1),
+        (Int(conv_in.shape()[1] * expand_ratio), Int(conv_in.shape()[1]), 1, 1),
         axis=["O", "I", "kH", "kW"],
         grad=True,
     )
-    conv1 = conv2d(conv_in, weight1)
+    weight1_quantized = quantize_weight(weight1)
+    conv1 = conv2d(conv_in, weight1_quantized)
     bn1 = batch_norm(conv1)
     relu1 = relu(bn1)
 
     weight2 = Tensor(
         "w2",
-        (relu1.shape()[1], Int(relu1.shape()[1] / reduce_ratio), 3, 3),
+        (Int(relu1.shape()[1] / reduce_ratio), relu1.shape()[1], 3, 3),
         axis=["O", "I", "kH", "kW"],
         grad=True,
     )
-    conv2 = conv2d(relu1, weight2)
+    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")
+
     depth = IntScalarParameter(0, max_blocks, name="depth")
     input_channels = IntScalarParameter(8, 64, name="input_channels", step_size=8)
 
     weight = Tensor(
         "w1",
-        (input.shape()[1], input_channels, 3, 3),
+        (input_channels, input.shape()[1], 3, 3),
         axis=["O", "I", "kH", "kW"],
         grad=True,
     )
+    weight = quantize_weight(weight)
 
     out = conv2d(input, weight)
 
     blocks = []
     for i in range(max_blocks + 1):
-        out = block(out)
+        out = block(out, expand_ratio.new(), reduce_ratio.new())
         blocks.append(out)
 
     out = dynamic_depth(*blocks, switch=depth)
@@ -133,6 +141,7 @@ def search_space(name, input, num_classes=10, max_blocks=9):
         axis=["O", "I"],
         grad=True,
     )
+    linear_weight = quantize_weight(linear_weight)
     out = linear(out, linear_weight)
 
     return out