| # Use the specific NVIDIA NeMo release | |
| FROM nvcr.io/nvidia/nemo:26.02 | |
| # 1. Set the working directory | |
| WORKDIR /workspace | |
| COPY . . | |
| # We point uv specifically to the NeMo virtual environment | |
| RUN uv pip install --upgrade --python /opt/venv/bin/python huggingface_hub fsspec && \ | |
| sed -i 's/require_version_core(deps\[pkg\])/pass/g' /opt/venv/lib/python3.12/site-packages/transformers/dependency_versions_check.py | |
| # This overwrites the broken file in the pre-installed library | |
| COPY rnnt_models.py /opt/NeMo/nemo/collections/asr/models/rnnt_models.py | |
| RUN LHOTSE_DIR=$(python -c 'import lhotse, os; print(os.path.dirname(lhotse.__file__))') && \ | |
| cp serialization.py $LHOTSE_DIR/serialization.py && \ | |
| cp lazy.py $LHOTSE_DIR/lazy.py && \ | |
| cp lazyShar.py $LHOTSE_DIR/shar/readers/lazy.py | |
| # 3. Handle WANDB (Passed at runtime for security) | |
| # We leave the variable name here so the script can find it | |
| ENV WANDB_API_KEY="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" | |
| # 4. Copy your local training scripts/configs | |
| # 5. Setup execution | |
| RUN chmod +x /workspace/runTrain.sh | |
| CMD ["/bin/bash", "-c", "sleep infinity"] | |