aleleba
572ee0b60e
Scaffolding inicial del repo: carpetas scripts/, data/schemas/, data/raw/, out/, .gitignore para binarios/checkpoints, y el docker-compose.yml del contenedor de training (imagen NGC pytorch 25.12-py3, GPU reservada, bind mounts a ai-projects vía NFS y a ~/ft-models en disco local rápido de spark) sin tocar jupyter-pyt ni el vllm de producción.
19 lines
536 B
YAML
19 lines
536 B
YAML
services:
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qwen-lora-train:
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image: nvcr.io/nvidia/pytorch:25.12-py3
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container_name: qwen-lora-train
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restart: unless-stopped
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command: ["sleep", "infinity"]
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working_dir: /workspace/ai-projects/qwen3-6-lora
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shm_size: "16gb"
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volumes:
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- /mnt/docker-nas/projects/ai-projects:/workspace/ai-projects
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- /home/aleleba/ft-models:/workspace/ft-models
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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