Creating an Algorithm container
In this tutorial, we will build an Algorithm container for a U-Net that segments retinal blood vessels from the DRIVE Challenge. We used evalutils to create the Algorithm container for the DRIVE challenge, and our scripts can be found here: https://github.com/DIAGNijmegen/drive-vessels-unet/tree/master/VesselSegmentation
The image below shows the output of a very simple U-Net that segments vessels.
To start the process, let's clone the repository that contains the weights from a pre-trained model and the python scripts to run inference on a new fundus image.
$ git clone https://github.com/DIAGNijmegen/drive-vessels-unet.git
Create base repository using evalutils
Evalutils provides methods to wrap your algorithm in Docker containers. Just execute the following command in a terminal of your choice:
$ evalutils init algorithm VesselSegmentationContainer
VesselSegmentationContainer is the custom name we have given for our algorithm container for this walkthrough. This will create a templated repository with a Dockerfile.
The scripts for your container files will be automatically generated by evalutils. It also creates commands for building, testing, and exporting the algorithm container.
VesselSegmentationContainer/ ├── Dockerfile ├── README.md ├── build.sh ├── export.sh ├── process.py ├── requirements.txt ├── test │ ├── 1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd │ ├── 1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw │ └── expected_output.json └── test.sh
Bring your own Algorithm in process.py
The next step is to edit process.py. This is the file where you will extend the Algorithm class of evalutils and implement your inference algorithm. In this file, a new class, VesselSegmentationContainer, has been created for you, and it is instantiated and run with:
if __name__ == "__main__": VesselSegmentationContainer().process()
You can build on top of this template and write your algorithm into the repository. The default algorithm generated by evalutils does simple binary thresholding of the input image. You'll have to replace that with your custom algorithm.
Configuring the Dockerfile
Ensure that you import the right base image in your Dockerfile. For our U-Net, we will build our Docker with the official PyTorch Docker as the base image. This should take care of installing PyTorch with the necessary CUDA environments inside your Docker. If you're using TensorFlow, please build your Docker with the official base image from TensorFlow. You can browse through Docker Hub to find your preferred base image. The base image can be specified in the first line of your Dockerfile:
Here are some best practices for configuring your Dockerfile.
🔩 Copying your model weights into the Docker
Ensure that you copy all the files needed to run your scripts, including the model weights, into
/opt/algorithm. This can be configured in the Dockerfile using the COPY command. If your model weights are stored in a "checkpoints" folder, first copy them into the repository which contains the Dockerfile. Then add this line into the Dockerfile to ensure the checkpoints are copied into the container when the Docker gets built.
COPY --chown=algorithm:algorithm checkpoints/ /opt/algorithm/checkpoints/
Ensure that all of the dependencies with their versions are specified in requirements.txt as shown in the example below:
evalutils==0.3.0 monai==0.4.0 scikit-learn==1.0 scipy==1.6.3 scikit-image==0.18.1
Note that we haven't included
torch as it comes with the PyTorch base image included in our Dockerfile in the previous step.