platform/aiml/mlops/docker/README.md

Elizabeth Containers for Vast.ai / Cast.ai

Images

• Serving image: docker/serve/Dockerfile
• Training image: docker/train/Dockerfile

Model Mount

• Mount your model directory into the container:
  • Host: /data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft
  • Container: /data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft
• Or mount at /models and set MODEL_PATH=/models/qwen3-8b-elizabeth-sft.

Serving Container

• Build:
  • docker build -f docker/serve/Dockerfile -t <repo>/elizabeth-serve:latest .
• Run (local test):
  • docker run --gpus all -p 8000:8000 -v /models/qwen3-8b-elizabeth-sft:/data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft:ro --env ELIZABETH_API_KEY=elizabeth-secret-key-2025 <repo>/elizabeth-serve:latest
• Fallback seeding:
  • From HF (private): set PREFER_HF=1 HF_TOKEN=<token> HF_ORG=LevelUp2x MODEL_NAME=qwen3-8b-elizabeth-checkpoints
  • From seed node: set SEED_HOST=user@host (rsync)

Training Container

• Build:
  • docker build -f docker/train/Dockerfile -t <repo>/elizabeth-train:latest .
• Run (mount ckpts):
  • docker run --gpus all -v /models/qwen3-8b-elizabeth-sft:/data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft:ro -v /data/ckpts/elizabeth:/data/ckpts/elizabeth <repo>/elizabeth-train:latest
• Exec your training:
  • accelerate launch your_train.py --model /data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft --output_dir /data/ckpts/elizabeth

Vast.ai

• Specify the image (pushed to Docker Hub or GHCR).
• Mount a persistent NVMe volume at /models with the model files.
• Env:
  • ELIZABETH_API_KEY=... (serving)
  • MODEL_PATH=/data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft
  • Optional: HF_TOKEN, PREFER_HF=1, SEED_HOST for bootstrap.

Promote Checkpoint

• After training, validate then promote:
  • bash scripts/promote_checkpoint.sh /data/ckpts/elizabeth/checkpoint-XXXX /models/qwen3-8b-elizabeth-sft
  • Restart serving container or let health-monitor reload.