monobase

Monobase image with all Cog & CUDA * CuDNN * Python * Torch dependencies

Motivation

Monobase aim to solve the following problems with Docker base images:

• 100+ images for all CUDA * CuDNN * Python * Torch combinations
• Many common files across images, e.g. Torch bundled CUDA & CuDNN
• Inefficient space utilization due to Docker layering
• Bumping a package requires rebuilding all base images
• Bumping base image requires rebuilding all model images

Design

Monobase is a Docker container that runs in two modes:

• As a daemon set on K8S nodes
  • Mounts host path /srv/r8/monobase
  • Installs Cog, CUDA, CuDNN, Python, Torch, and other PIP packages
• As a base image for weightless models
  • Mounts host path /srv/r8/monobase as read-only
  • Set *PATHs based on Cog, CUDA, CuDNN, Python, and Torch versions

Monobase is more efficient than base images because:

• Only system packages are baked into the image
• PIP packages are managed by uv which uses hard links for better space utilization
• Bumping a system package requires a new monobase image only
• Bumping a PIP package requires building a new immutable generation
• Building new generation is fast and efficient due to UV cache and hard links
• Immutable generations are easy to reason about
• Daemon set pins the image on K8S nodes and eliminates cache miss

Operations

The following commands are available as runnable modules:

python -m monobase.build --help

python -m monobase.diff --help

# NOTE: no --help available
python -m monobase.monogen

python -m monobase.update --help

Support Cog * Python verions are pre-installed in its own layer. Cog versions are managed via --cog-versions and --default-cog-version.

• There is only one Cog generation
• Generation ID hashed on Cog, Python versions and default Cog version
• A one-off venv will be created if R8_COG_VERSION is not available

A monobase generation is an immutable matrix of CUDA, CuDNN, Python, Torch, and other PIP packages. To add a generation:

• Add an element to PROD_MONOGENS in src/monobase/monogen.py
• Run script/update --min-gen-id X where X is the generation ID added
• Verify the new requirements files in src/monobase/requirements/g{X:05d}
• Check in the new requirements files into Git
• Build and update monobase daemon set to the latest image

A few notes about operations:

• Args --{min,max}-gen-id set the active generations [min, max]
• Readiness probe reports whether the active generations are ready
• Arg --prune-old-gen deletes generations older than --min-gen-id
• Arg --clean-uv-cache cleans UV cache in case of corruption

Models

When used as a base image for weightless models, the following environment variables determine the runtime environment:

• MONOBASE_GEN_ID - monobase generation to use, latest if unset
• R8_COG_VERSION - Cog major.minor.patch or https://*
• R8_CUDA_VERSION - CUDA major.minor
• R8_CUDNN_VERSION - CuDNN major
• R8_PYTHON_VERSION - Python major.minor
• R8_TORCH_VERSION - Torch major.minor.patch

A PYTHONPATH={cog}:{monobase} is constructed from these variables.