docker-compose.yml

services:
  app:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: instructlab
    runtime: nvidia
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    volumes:
      - ./volumes/taxonomy:/root/.local/share/instructlab/taxonomy
      - ./volumes/models:/root/.cache/instructlab/models
      - ./volumes/checkpoints:/root/.local/share/instructlab/checkpoints
      - ./volumes/datasets:/root/.local/share/instructlab/datasets

    working_dir: /workspace
    stdin_open: true  # Mantém o terminal aberto para interatividade
    tty: true         # Necessário para o bash interativo
    env_file:
      - .env  # <-- Adicionando o arquivo .env aqui
    environment:
    # Testada Variavel para allocar memoria CUDA com Split, nao funcionou, o erro :
    # torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 388.00 MiB. GPU 0 has a total capacity of 8.00 GiB of which 0 bytes is free. Including non-PyTorch memory, this process has 17179869184.00 GiB memory in use. Of the allocated memory 37.87 GiB is allocated by PyTorch, and 102.37 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
    # Persistiu
      #- PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128
      - NVIDIA_VISIBLE_DEVICES=all