tigerflow-ml
ML tasks for TigerFlow — private cloud ML APIs on HPC infrastructure.
Installation
pip install tigerflow-ml
If using a task that relies on vllm (chat, OCR, or translation), install with:
pip install tigerflow-ml[vllm]
Tasks
| Task | Description | Entry Point |
|---|---|---|
| OCR | Extract text from images and PDFs | ocr / ocr-local |
| Translation | Translate text documents | translate / translate-local |
| Chat | Apply a chat prompt to images or text | chat / chat-local |
| Transcription | Transcribe audio to text | transcribe / transcribe-local |
| Object Detection | Detect objects in images and videos | detect / detect-local |
Each task provides both a Slurm variant (for HPC) and a Local variant (for development).
Usage
After installation, tasks are automatically discoverable via:
tigerflow tasks list
Run a task directly:
python -m tigerflow_ml.text.ocr.slurm --help
python -m tigerflow_ml.text.translate.slurm --help
python -m tigerflow_ml.text.chat.slurm --help
python -m tigerflow_ml.audio.transcribe.slurm --help
python -m tigerflow_ml.image.detect.slurm --help
Container
A GPU image with all tasks (including the vllm extra) is published to GHCR on
each release:
docker pull ghcr.io/princeton-ddss/tigerflow-ml:latest
The image bundles its own CUDA libraries, so only the host NVIDIA driver is
needed at runtime — pass --gpus all (Docker) or --nv (Apptainer). Models are
fetched on first use; mount a cache at /cache to persist them.
The entrypoint is tigerflow, so arguments are passed straight to the CLI:
# Run a pipeline (config file + input/output directories)
docker run --gpus all -v "$PWD/cache:/cache" -v "$PWD:/data" \
ghcr.io/princeton-ddss/tigerflow-ml:latest \
run /data/pipeline.yaml /data/input /data/output
# List available tasks
docker run ghcr.io/princeton-ddss/tigerflow-ml:latest tasks list
# Run a single task module directly (override the entrypoint)
docker run --gpus all -v "$PWD/cache:/cache" -v "$PWD:/data" \
--entrypoint python ghcr.io/princeton-ddss/tigerflow-ml:latest \
-m tigerflow_ml.audio.transcribe.local --help
On HPC, convert to a Singularity/Apptainer image:
apptainer build tigerflow-ml.sif docker://ghcr.io/princeton-ddss/tigerflow-ml:latest
apptainer run --nv -B ./cache:/cache tigerflow-ml.sif run pipeline.yaml input output
apptainer exec --nv tigerflow-ml.sif python -m tigerflow_ml.audio.transcribe.local --help
Development
uv sync --group dev
uv run pre-commit run --all-files
uv run pytest tests
1 maintainer
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