---
name: tao-run-on-local-docker
description: Run TAO SDK jobs as Docker containers on a local or remote Docker daemon with NVIDIA GPU support, including preflight checks and credential handling.
license: Apache-2.0
---

# Local Docker

Single-node execution platform that runs TAO jobs as named Docker containers on
a Docker daemon. The daemon can be local to the agent host or remote through
`DOCKER_HOST=ssh://user@host` / a Docker context. It is useful for development,
debugging, small runs, and workflows where a local coding agent submits jobs to
a remote GPU box.

Use local Docker when the data is local to the Docker host or accessible through
mounted volumes/cloud credentials. Do not use it for remote cluster scheduling,
multi-node training, or jobs that need SLURM queueing.

Use remote Docker when the agent is running on a workstation or laptop but the
Docker daemon and GPUs are on another single GPU server. In remote Docker mode,
all local filesystem paths in specs are interpreted on the remote Docker host,
not on the agent machine.

## Preflight

The workflow must verify the host GPU runtime before starting Docker jobs. If
the check fails, prompt the user to approve the install, run the printed install
command, and rerun the preflight.

```bash
# Host GPU runtime: NVIDIA driver 580, CUDA 13.0, NVIDIA Container Toolkit 1.19.0.
TAO_SKILL_BANK_ROOT="${TAO_SKILL_BANK_ROOT:-$PWD}"
SETUP_SCRIPT="${TAO_SKILL_BANK_ROOT}/skills/platform/tao-setup-nvidia-gpu-host/scripts/setup-nvidia-gpu-host.sh"

bash "$SETUP_SCRIPT" --backend docker --check-only || {
  echo "MISSING: TAO GPU host runtime is not ready."
  echo "After user approval, run:"
  echo "  bash \"$SETUP_SCRIPT\" --backend docker --install --yes"
  exit 1
}

# Mode 1 — direct docker (no Python). All you need is docker + the GPU runtime.
docker info >/dev/null 2>&1 || { echo "MISSING: docker daemon not reachable. Start Docker."; exit 1; }
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi >/dev/null 2>&1 || {
  echo "MISSING: NVIDIA Container Toolkit not installed/configured. See:"
  echo "  bash \"$SETUP_SCRIPT\" --backend docker --install --yes"
  exit 1
}

# Mode 2 — TAO SDK wrapper. Adds Job handles, S3 I/O wrapping, ActionWorkflow.
# Skip this block if Mode 1 is sufficient for the user's request.
# When Mode 2 is in scope, read `tao-skill-bank:tao-run-platform` for the DockerSDK
# kwarg contract, build_entrypoint, and monitoring patterns.
# nvidia-tao-sdk is on public PyPI; pin lives in versions.yaml (wheels.tao_sdk_docker).
PIN=$("${TAO_SKILL_BANK_PATH:?}/scripts/resolve_versions_key.py" wheels.tao_sdk_docker)
python -c "import tao_sdk" 2>/dev/null || python -m pip install "$PIN"
python -c "import docker" 2>/dev/null || python -m pip install "$PIN"
python -c "import tao_sdk, docker"

# DockerSDK attaches every job container to ${DOCKER_NETWORK:-tao_default}.
# Create the network if it is missing; the operation is local and idempotent.
DOCKER_NETWORK_NAME="${DOCKER_NETWORK:-tao_default}"
docker network inspect "$DOCKER_NETWORK_NAME" >/dev/null 2>&1 || \
  docker network create "$DOCKER_NETWORK_NAME" >/dev/null
```

If a check fails, the agent prompts the user to authorize the install/fix via Bash before proceeding. Pip-installable Python requirements and Docker network creation above are exceptions: install/create them automatically, then rerun preflight.

## Credentials

There are no platform credentials required beyond access to the Docker daemon.

Optional environment:

- **DOCKER_HOST**: Optional Docker daemon URL. If unset, the SDK uses the
  Docker Python client's normal environment/default socket resolution. Required
  for the `remote-docker` platform option.
- **DOCKER_NETWORK**: Docker network for job containers. Default is
  `tao_default`.
- **DOCKER_USERNAME**: Registry username. Default is `$oauthtoken` for NGC.
- **NGC_KEY**: Used when pulling private images from `nvcr.io`.
- **HOST_SSH_PATH**: Mounted into AutoML brain containers when they need SSH keys
  to monitor remote SLURM child jobs.
- **ACCESS_KEY**, **SECRET_KEY**, **S3_ENDPOINT_URL**, **S3_BUCKET_NAME**:
  Optional S3-compatible storage settings for jobs that still read/write cloud
  storage from a local container.

## Launch Preflight

Before generating scripts or starting containers:

1. Verify the Docker daemon is reachable, NVIDIA Container Toolkit is registered
   as a Docker runtime, GPUs and driver version are reported, and a smoke
   container can see GPUs before launch. For remote Docker, query GPUs through
   `docker run ... nvidia-smi` against the remote daemon; do not use local
   `nvidia-smi` from the agent machine.
2. Verify every local/file dataset annotation and media path exists on the
   Docker host.
3. For `s3://` datasets/results, verify `ACCESS_KEY` and `SECRET_KEY` are set
   and the exact paths are readable with `aws s3 ls`. If `aws` is missing,
   report the missing dependency and ask before installing it; rerun preflight
   after installation.
4. Verify model-specific credentials such as `HF_TOKEN` before launch.
5. Check current GPU occupancy with `nvidia-smi` and avoid GPUs already used by
   other running jobs when the user requested that constraint. Show the selected
   GPU ids in the launch review.
6. For model/container combinations with known architecture limits, compare
   host GPU compute capability with the container stack before launch. If the
   selected image cannot JIT or run kernels for the host architecture, block
   early and ask for a compatible image or platform.

Use the packaged helper for these checks when possible:

```bash
${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/check_tao_launch_preflight.py \
  --platform local-docker \
  --container-image "<selected-image>" \
  --path train_annotation=/abs/path/to/annotations.json \
  --path train_media=/abs/path/to/media
```

For a remote Docker daemon, use the `remote-docker` platform and pass or export
`DOCKER_HOST`. The helper verifies remote GPU/runtime readiness and checks
remote-host dataset paths through read-only bind mounts:

```bash
${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/check_tao_launch_preflight.py \
  --platform remote-docker \
  --docker-host ssh://user@gpu-host \
  --container-image "<selected-image>" \
  --gpu-smoke-image ubuntu:22.04 \
  --path train_annotation=/remote/data/train/annotations.json \
  --path train_media=/remote/data/train
```

The `--path` values above must exist on the remote Docker host. Do not pass
paths that exist only on the local laptop or Codex host.

## Multi-GPU and multi-node

**Multi-node is not supported on local Docker.** One job runs on the local Docker daemon's host with no cross-host coordination.

Multi-GPU **on the local host** is supported via the NVIDIA Container Toolkit's `--gpus` flag (`--gpus all` or `--gpus '"device=0,1,2,3"'`). `DockerSDK.create_job(gpu_count=N)` plumbs through to `--gpus`. Single-host distributed init uses `localhost`; `torchrun --nproc-per-node=N` or PyTorch DDP work as usual.

## Backend Details

Use the SDK backend value `local-docker`. The local backend schema has no extra
backend details, so most routing is controlled by environment and job
parameters:

```json
{
  "backend_type": "local-docker",
  "num_gpu": 1
}
```

Following the Brev SDK design, platform/control-plane values stay in SDK
state and Docker labels. The SDK does not inject `BACKEND`, `HOST_PLATFORM`,
`MONGOSECRET`, `DOCKER_HOST`, or `DOCKER_NETWORK` into the training container.

## Container Execution

The TAO SDK local Docker handler starts containers through the Docker Python
client:

- Backend job name uses the `tao-job-<job_id>` form used by SDK handlers.
- Command is usually `["/bin/bash", "-c", "<job command>"]`.
- Containers run detached. The SDK keeps containers by default so status and
  logs remain inspectable, unless `DOCKER_AUTO_REMOVE=true`.
- `/dev/shm` is mounted as tmpfs.
- The configured Docker network is applied by the Docker daemon for the job
  container; it is not passed through as a process environment variable.
- Existing containers with the same job id are stopped and removed before a
  replacement starts.

For GPU access, the handler auto-detects the host type:

- Tegra or Jetson hosts use `runtime="nvidia"` plus
  `NVIDIA_VISIBLE_DEVICES` and `NVIDIA_DRIVER_CAPABILITIES=all`.
- Standard x86 hosts use Docker `device_requests` with GPU capabilities.

If `num_gpus` is `0`, no GPUs are assigned. If `num_gpus` is `-1`, all visible
GPUs are requested. Prefer explicit GPU counts for shared development machines.
When explicit device ids are available, prefer them over count-only selection
on shared machines so the launch does not steal GPUs occupied by other tasks.

## Storage

Local Docker accepts local and `file://` paths because the container runs on the
same Docker host. Make sure every path in the spec is either:

- mounted into the container by the handler or surrounding service,
- reachable from inside the container already, or
- a cloud URI with matching credentials.

For remote/shared filesystems, prefer the platform that owns that filesystem.
For example, use SLURM plus `lustre:///...` for Lustre paths on a cluster.

## Monitoring

- The SDK handler maps Docker container state directly: created -> Pending,
  running/restarting -> Running, paused -> Paused, exit code 0 -> Complete,
  nonzero exit -> Error.
- Logs come directly from the named container through the Docker Python client
  (`docker logs tao-job-<job_id>`).

If the container has exited, died, is being removed, or cannot be found, status
reconciliation treats the backend process as terminated.

## Cancellation

Cancellation stops the named container. GPU ownership is managed by Docker /
the NVIDIA runtime, not by TAO Core's local GPU manager.

## Optional: via the TAO SDK

If you want Job handles, S3 I/O wrapping via the SDK's `script_runner`, or
durability across sessions:

```python
from tao_sdk.platforms.docker import DockerSDK

sdk = DockerSDK()  # reads DOCKER_HOST, NGC_KEY, S3 creds from env
job = sdk.create_job(
    image='nvcr.io/nvidia/tao/tao-toolkit:6.26.3-pyt',
    command='dino train -e /tmp/spec.yaml',
    gpu_count=1,
    inputs={'/data/train.json': 's3://bucket/coco/train.json'},
    outputs=['/results/'],
)

status = sdk.get_job_status(job.id)
logs = sdk.get_job_logs(job.id, tail=200)
```

This wraps the same `docker run` invocation under a `Job` handle and routes
the entrypoint through `script_runner` so `inputs`/`outputs` get downloaded
from / uploaded to S3 automatically. If you don't need those, just use
`docker run` directly — no SDK install required.

## Failure Modes

**Docker client not initialized**: Verify the Docker Python package is installed,
set `DOCKER_HOST` if you are not using the default local socket, and confirm the
process can talk to the daemon.

**GPU assignment failed**: Requested GPUs are unavailable, the NVIDIA Container
Toolkit is not configured, or the Docker daemon cannot create GPU device
requests. Use fewer GPUs, wait for another job to finish, or verify
`docker run --gpus ...` works on the host.

**Image pull auth failed**: Set a valid `NGC_KEY` for private `nvcr.io` images
or run `docker login nvcr.io -u '$oauthtoken'` on the Docker host.

**Container exited unexpectedly**: Check `docker logs tao-job-<job_id>`, the
configured `DOCKER_NETWORK`, and the command produced by the SDK action runner.

**Path missing inside container**: A local path on the host is not necessarily
mounted into the job container. Use a path convention supported by the action
runner or configure an explicit volume through the surrounding service.