Working with Conda packs#
Conda packs provide runtime dependencies and a
python runtime for your code. The conda packs can be built inside an
OCI Data Science Notebook session or you can build it locally on your workstation.
ads opctl cli provides a way to setup a development environment to build and use the conda packs. You can push the conda packs that you build locally to Object Storage and use them in Jobs, Notebooks, Pipelines, or in Model Deployments.
Build a local
OCI Data Science Jobcompatible docker image
Connect to Object Storage through the Internet
Setup conda pack bucket, namespace, and authentication information using
ads opctl configure. Refer to configuration instructions.
In this version you cannot directly access the Service provided conda environments from ADS CLI, but you can publish a service provided conda pack from an OCI Data Science Notebook session to your object storage bucket and then use the CLI to access the published version.
ads opctl conda create -n <name> -f <path-to-environment-yaml>
Build conda packs from your workstation using
ads opctl conda create subcommand.
To publish a conda pack that is natively installed on a oracle linux host (compute or laptop), use
NO_CONTAINERenvironment variable to remove dependency on the ml-job container image:NO_CONTAINER=1 ads opctl conda publish -s <slug> --auth <api_key/instance_principal/resource_principal>
ads opctl conda publish -s <slug>
Publish conda pack to the object storage bucket from your laptop or workstation. You can use this conda pack inside
OCI Data Science Service or
Data Flow service.
Install conda pack using its URI. The conda pack can be used inside the docker image that you built. Use Visual Studio Code that is configured with the conda pack to help you test your code locally before submitting to OCI.
ads opctl conda install -u "oci://mybucket@namespace/conda_environment/path/to/my/conda"