60 lines
1.5 KiB
YAML
60 lines
1.5 KiB
YAML
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# version: "3.9"
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services:
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dl:
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build:
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context: .
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dockerfile: Dockerfile
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args:
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BASE_IMAGE: "pytorch/pytorch:2.9.0-cuda13.0-cudnn9-devel"
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USER: "dev"
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UID: "1000"
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GID: "1000"
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container_name: dl
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# GPUs + large DataLoader shared memory
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gpus: all
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shm_size: "12g"
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ipc: host
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environment:
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# Always use GPUs (you can limit to some: e.g., "0,1")
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- NVIDIA_VISIBLE_DEVICES=0
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
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# Prefer NCCL for multi-GPU
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- TORCH_DISTRIBUTED_DEBUG=INFO
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- NCCL_P2P_DISABLE=0
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- NCCL_ASYNC_ERROR_HANDLING=1
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# Persisted virtualenv on PATH (lives in a named volume)
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- VIRTUAL_ENV=./venv
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- PATH=./venv/bin:/usr/local/bin:/usr/bin:/bin
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- PYTHONUNBUFFERED=1
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- TZ=America/Los_Angeles
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volumes:
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# your code/data
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- .:/workspace
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- /home/image1325/ssd1/dataset/coco:/data
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# persisted venv: your pip installs live here and survive image/container removal
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- venv:./venv
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# (optional) speed up installs
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- pip-cache:/home/dev/.cache/pip
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working_dir: /workspace
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ulimits:
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memlock: -1
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stack: 67108864
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# On first run, create the venv if it doesn't exist; then drop to a shell.
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command: >
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bash -lc "
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if [ ! -d /opt/venv/bin ]; then
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python -m venv /opt/venv;
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/opt/venv/bin/python -m pip install --upgrade pip;
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fi;
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exec bash
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"
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volumes:
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venv:
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pip-cache:
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