AstrAI/docs/developer/docker-training.md

Containerized Training Deployment

Rules for running AstrAI distributed training in containers, distilled from real deployment failures. Read before touching Dockerfile, docker-compose.yml, scripts/train.sh, train-entrypoint.sh. AGENTS.md mirrors this locally; this file is the committed version.

Architecture

scripts/train.sh           host-side CLI: env loading, preflight, compose wrapper, lifecycle
  └── docker-compose.yml   GPU passthrough, mounts, in-container env vars, entrypoint
        └── train-entrypoint.sh   GPU-count resolution, parallel-mode selection, auto-resume
              └── train.py --config /run/astrai/train.yaml

Layer	Responsible for	NOT responsible for
train.sh	host paths, .env.train, preflight, lifecycle	training args, GPU selection, parallel mode
compose	GPU passthrough, mounts, in-container env (NCCL)	training args (beyond TRAIN_* forwarding)
entrypoint	--ckpt_dir/--nprocs/--parallel_mode/--param_path, resume	hyperparameters (YAML/CLI)
train.yaml	hyperparameters (_merge_yaml_into_kwargs, CLI wins)	container paths, process count

Path Conventions

Host var	Container	Perm	Purpose
TRAIN_DATA_DIR	/data	ro	dataset (data_root_path must be /data)
TRAIN_MODEL_DIR	/models/base	ro	base model (config.json + model.safetensors)
TRAIN_CHECKPOINT_DIR	/checkpoints	rw	checkpoint root, per-TRAIN_JOB_NAME subdirs
TRAIN_CONFIG_FILE	/run/astrai/train.yaml	ro	training YAML (mounted only on start)
code	/app	image	not a mount; rebuild image for code changes

Hard Rules

1. Filter GPUs once: compose passes the full physical set (count: all); CUDA_VISIBLE_DEVICES filters inside by physical index. Never count: N + physical indices (double filter leaves 1 card → device_id out of range).
2. In-container UID = host UID: Dockerfile builds the user via USER_UID/USER_GID args; train.sh injects ASTRAI_UID/GID (bash UID is readonly). compose user: alone does not create the /etc/passwd entry — torch's getpass.getuser() then dies with uid not found.
3. In-container env vars are explicit: .env.train (--env-file) is only compose's interpolation dictionary — never reaches the container. A var arrives only via a value-less environment entry (- VAR, read from the calling process env).
4. NCCL hang workaround (this host): NCCL_P2P_DISABLE=1 + NCCL_NET_GDR_LEVEL=0 must be in-container.
5. Checkpoint complete = meta.json + config.json + model.safetensors + optimizer.pt + scheduler.pt; start auto-resumes the latest complete one.
6. tqdm is silent without a TTY: add disable=False in astrai/trainer/train_callback.py; metric.jsonl (per step) works as progress evidence regardless.

Operations

bash scripts/train.sh init        # first run: dirs + .env.train (edit per machine)
bash scripts/train.sh preflight   # validate Docker/paths/GPU/model/YAML/compose
bash scripts/train.sh start       # build + start in background (auto-resume)
bash scripts/train.sh start --foreground -- --dry-run   # print plan only
bash scripts/train.sh logs | status | stop | restart
bash scripts/train.sh clean --keep 5   # prune old checkpoints (--force to delete)

Files

• docker-compose.yml — trainer service: count: all, ASTRAI_UID/GID build args + user:, env whitelist, mounts
• Dockerfile — production stage builds user from USER_UID/USER_GID; ENV HOME=/home/astrai; USER astrai
• scripts/train.sh — load_env filters UID= lines (readonly var); compose() injects ASTRAI_UID/GID
• scripts/docker/train-entrypoint.sh — GPU-count resolution, parallel mode, resume
• .env.train, train.yaml — host-specific; templates from scripts/train.sh init; scientific-notation floats (2e-5) parse correctly since train.py uses the YAML 1.2 float schema