- Companion repository for Medium Post: How to predict many protein structures with AlphaFold2 at-scale in Azure Machine Learning.
- Jupyter Notebook: AzureML_AlphaFold2_HyperDrive.ipynb
- Sample Sequence FASTA: sequences.fasta
- Dockerfile: Dockerfile
The custom Docker image for running ColabFold in Azure ML HyperDrive is available on Docker Hub at https://hub.docker.com/r/cford38/alphafold2_aml. If you'd like to build the image for yourself, follow the instructions below.
First, build the custom Azure ML Docker image (with the required dependencies for the AlphaFold2 system).
docker build -t alphafold2_aml --build-arg IMAGE_VERSION=$(git rev-parse --short HEAD) .
# docker run --name alphafold2_aml --rm -p 8787:8787 alphafold2_aml
# docker exec -it alphafold2_aml /bin/bashTo push the image to DockerHub, run the following: (If you'd like to use Azure Container Registry, see this documentation.)
docker image tag alphafold2_aml <USERNAME>/alphafold2_aml:latest
docker push <USERNAME>/alphafold2_aml:latest
# docker image tag alphafold2_aml cford38/alphafold2_aml:latest
# docker push cford38/alphafold2_aml:latestTo push the image to an Azure Container Registry, run the following:
az login
az account set --subscription <SUBSCRIPTION_ID>
az acr login --name <CONTAINER_REGISTRY_NAME>
docker tag mmae_aml <CONTAINER_REGISTRY_NAME>.azurecr.io/alphafold2_aml:$(git rev-parse --short HEAD)
docker push <CONTAINER_REGISTRY_NAME>.azurecr.io/alphafold2_aml:$(git rev-parse --short HEAD)