- Add a Per-Job Environment section explaining that runtime.environment reaches only the GPUs declared in the same job YAML, with one-YAML-per-GPU-group examples for local, cross-PCIe workaround, and NVSwitch NVLink tuning setups
- Replace the NCCL workaround pair in the runtime schema example with ASTR_LOG_LEVEL and ASTR_BACKEND and document value semantics (str() rendering, null exports empty, no host-shell passthrough)
- Comment out the blanket NCCL exports in the get-started multi-GPU example so they are opt-in per docs/guides/distributed.md
- Add a hard rule against copying NCCL workarounds into every training config
- serve_runtime.py and train_runtime.py are host-side Docker helpers, so they join train-entrypoint.sh and lib/ under scripts/docker/
- scripts/tools/ now contains only in-container CLIs
- update wrapper call sites, test import, and docker guide references
- serve.sh/train.sh no longer pass --build on up/run; the build subcommand is the only path that rebuilds
- compose services pin image: astrai:latest so run reuses the existing image instead of triggering a rebuild
- runtime parsers leave CUDA_VISIBLE_DEVICES unset for gpu.devices: all; an empty string hid every GPU inside the container
- server service reserves count: all GPUs so CUDA_VISIBLE_DEVICES performs the only filtering, matching the trainer
- wrapper compose() strips an empty host CUDA_VISIBLE_DEVICES before invoking docker compose
- scripts/train.sh load_infra() parses the top-level infra: section of TRAIN_CONFIG_FILE
- exports TRAIN_JOB_NAME/DATA/MODEL/CHECKPOINT_DIR/TRAIN_GPU_COUNT/CUDA_VISIBLE_DEVICES
- infra overrides .env.train via compose interpolation precedence; keys absent fall back
- train.yaml is now the single per-job config: host mounts, GPU filter, and hyperparameters
- requires host python3 with PyYAML when TRAIN_CONFIG_FILE is set; errors fail fast
- docs: docker-training.md documents the infra overrides and precedence
- register yaml 1.2 float resolver so scientific notation (2e-5) becomes float, not str
- replaces the decimal-point workaround in train configs
- add containerized training doc under docs/developer
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