Apolo Base Docker image
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Our company provides a public Docker image with pre-configured Conda environments and pre-installed Python dependencies, designed to simplify development and deployment. This image ensures compatibility, consistency, and efficiency across diverse workflows, making it an ideal solution for streamlined integration into your projects.
Explore the guide below for setup instructions and configuration details.
The GitHub repository serves as the primary source of truth for all updates, configurations, and detailed documentation regarding this Docker image.
Releases can be found in
Each release includes four Docker images, each configured with a specific set of dependencies.
Dependencies version can be found in the specific release page.
You can utilize our docker image in various ways, pulling it from public repository, using it locally or in Apolo jobs.
Base path
Non-versioned tags
Apolo-flow
when you are creating Apolo flow using our template, by default we expose base Docker
Also, if you don't want to edit the Dockerfile, you can specify docker directly in your job .neuro/live.yml
By default, our Docker image provides three Conda environments:
Default: Automatically activated through the .bashrc configuration for all terminal sessions
can be activated using
TensorFlow-specific: Optimized for TensorFlow workflows. can be activated using
Torch-specific: Tailored for PyTorch operations.
can be activated using
Environment
We strive to keep dependencies up-to-date. If you require a more recent version or believe an update could enhance the platform, please reach out to us. Alternatively, you can extend our base Dockerfile to install any additional dependencies you need.
