ViperEkura
1c04a0b9fa
- 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
112 lines
4.2 KiB
Markdown
112 lines
4.2 KiB
Markdown
# Containerized Serving Deployment
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AstrAI uses one serving YAML as the declaration for both host-side container
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runtime settings and in-container server settings. `scripts/serve.sh` wraps the
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Compose commands so preflight validation and container lifecycle stay
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consistent with the trainer.
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## Architecture
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```text
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serve.yaml
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├── runtime parsed on the host before Docker starts
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└── server parsed by server.py inside the container
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│
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scripts/serve.sh preflight, Compose wrapper, lifecycle
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└── docker-compose.yml GPU passthrough, mounts, image, port mapping
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└── server.py --config /run/astrai/serve.yaml
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```
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`scripts/docker/serve_runtime.py` reads `runtime:` plus the two container-side
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values Compose needs (`server.port` for the port mapping, `server.device` for
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the preflight GPU check). `scripts/tools/server.py --config` reads `server:`.
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Explicit CLI arguments to `server.py` override `server:` YAML values.
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## Runtime Schema
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```yaml
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runtime:
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job_name: serve
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port: 8000
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paths:
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param: ./params
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gpu:
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enabled: true # false → cpu profile (server-cpu service)
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devices: all # all | [0]
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container:
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cuda_tag: cu128
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# environment:
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# TOKENIZERS_PARALLELISM: "false"
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server:
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host: 0.0.0.0
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port: 8000
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device: cuda # cuda | cpu
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dtype: bfloat16 # bfloat16 | float16 | float32
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max_batch_size: 16
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max_seq_len: null # falls back to model config
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```
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- Relative paths resolve from the YAML file's directory, not the current shell.
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- `runtime.port` is the host publish port; `server.port` is the port the
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container listens on. The Compose mapping is
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`${SERVE_PORT}:${SERVE_CONTAINER_PORT}`.
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- `runtime.gpu.enabled: true` (default) selects the `server` service with an
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NVIDIA device reservation; `false` selects `server-cpu` (no GPU passthrough).
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When disabled, `server.device` must be `cpu`.
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- `runtime.gpu.devices` is `all` (default) or a single-device list such as `[0]`;
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the list becomes `CUDA_VISIBLE_DEVICES`. Compose passes `count: all`; the
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env var performs the only filtering.
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- `environment` values are explicitly passed to the serving container. Keep
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host-specific settings here; they are not universal defaults.
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- `server.device` must agree with `runtime.gpu.enabled`; `preflight` enforces it.
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## Fixed Container Paths
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| Runtime path | Container path | Access |
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|---|---|---|
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| `runtime.paths.param` | `/app/params` | read-only |
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| the selected YAML | `/run/astrai/serve.yaml` | read-only |
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`server.param_path` is optional: the server default is
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`project_root/params`, which is exactly `/app/params` inside the container
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(the working directory is `/app`). Set it explicitly only when serving from a
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different location; in Docker it must be a container path.
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## Operations
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The config argument defaults to `./serve.yaml`:
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```bash
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bash scripts/serve.sh init [CONFIG]
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bash scripts/serve.sh preflight [CONFIG]
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bash scripts/serve.sh up [CONFIG]
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bash scripts/serve.sh run [CONFIG]
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bash scripts/serve.sh down [CONFIG]
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bash scripts/serve.sh restart [CONFIG]
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bash scripts/serve.sh logs [CONFIG]
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bash scripts/serve.sh status [CONFIG]
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```
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`preflight` validates Docker, the model directory
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(`config.json` + `model.safetensors`), GPU/device consistency, and the
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rendered Compose configuration. `up` starts the container detached; `run`
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keeps it in the foreground. Both reuse the existing image; run
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`bash scripts/serve.sh build [CONFIG]` after code changes. The wrapper manages a fixed container name
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(`astrai-server` or `astrai-server-<job_name>`); the plain
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`docker compose up -d` / `docker compose --profile cpu up -d` path keeps
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working with defaults (port 8000, `./params`).
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## Hard Rules
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1. Keep Docker settings in `runtime` and server settings in `server`.
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2. Filter GPUs once: Compose passes `count: all`; a `devices`
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list becomes `CUDA_VISIBLE_DEVICES`.
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3. `runtime.gpu.enabled: false` requires `server.device: cpu`.
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4. In Docker, `server.port` must match the published container port (default
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`8000`); change `runtime.port` to publish on a different host port.
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5. The image user is built with the host UID/GID so the mounted model
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directory stays readable.
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> Document Update Time: 2026-08-22
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