Tao Run Platform

TAO Execution SDK

The SDK is the optional Python layer for users who need job handles, S3 I/O wrapping, or platform-specific features (SLURM/Lustre queues, Kubernetes Jobs, local Docker debugging, Brev instance reuse). Most TAO skills run with just docker run and don't need it. Reach for the SDK when:

• You want a Job handle to poll status and stream logs over time.
• You need S3-aware input download / output upload baked into the entrypoint.
• You're chaining multiple jobs and want persisted state.

Preflight

Install nvidia-tao-sdk[all] before using this platform — the [all] extra pulls in every platform-specific dependency (Brev, S3 utilities, etc.). If it is missing, install it by default in the active Python environment and rerun the import check:

python -c "import tao_sdk" 2>/dev/null || {
  echo "Installing missing Python requirement: nvidia-tao-sdk[all]"
  python -m pip install "nvidia-tao-sdk[all]"
}
python -c "import tao_sdk"

The package index is environment-specific — the runner/container is expected to have a working pip configuration (e.g. ~/.pip/pip.conf, PIP_INDEX_URL, PIP_EXTRA_INDEX_URL, or proxy). If the install fails for index/network reasons, that's a runner setup issue; this skill stays agnostic to the registry.

Missing pip requirements are installed automatically by default and reported in the run log. Non-pip/system prerequisites still require a normal preflight failure and user-visible remediation.

Setup

Credentials come from environment variables — read from the session environment (export them in your shell before launching).

from tao_sdk.platforms.brev   import BrevSDK     # Brev GPU instances

sdk = BrevSDK()      # reads BREV_API_TOKEN (optional — falls back to brev login)

The SDK validates credentials lazily on first use and raises CredentialError with a clear message if a required env var is missing. Required env vars:

The agent never reads credential values — it only checks presence with [ -n "$VAR_NAME" ].

Workflow Launch Intake

For any TAO workflow or action launch, first confirm the user goal. Then ask for platform and monitoring preferences before credentials or launch details. Generate the supported platform choices from the packaged helper, not by scanning platform docs or folders:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_platforms.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --format text

Ask:

1. Which supported platform should run this workflow?
2. Should long-running monitoring stay enabled? Default: enabled. This means the agent remains attached and posts status until terminal state, including long PENDING queue waits.
3. How many minutes between status updates? Default: 5 minutes.

After the model/action are known, resolve the default container image from the packaged metadata and ask the user to confirm it or provide image=<override> before creating runner files:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/resolve_tao_image.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} \
  --model <network_arch> --action <action> --format text

For train-capable model workflows, inspect model-level AutoML metadata before creating a plain training job:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_models.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} \
  --scope automl --format json

If the selected model has automl_enabled: true and a valid train schema, route training through skills/applications/tao-run-automl by default with automl_policy: on. A workflow should only bypass AutoML when its run settings include automl_policy: off, the user explicitly asks for a plain run, or the model metadata says AutoML is enabled but the train schema is not packaged yet.

After the platform is selected, get the credential filter:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_platforms.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} \
  --platform <platform> --format text

Ask only for credentials returned for the selected platform. For example, SLURM needs SLURM_USER and SLURM_HOSTNAME; it does not need Brev credentials. Kubernetes and local Docker do not need Brev or SLURM credentials. Ask storage credentials such as S3 keys only when the selected platform and the data/result URIs require them.

Core API

All platform SDKs implement the same core shape:

sdk.create_job(image, command, gpu_count=1, env_vars=None, inputs=None, outputs=None, **kwargs) -> Job
sdk.get_job_status(job_id) -> JobStatus
sdk.get_job_logs(job_id, tail=None) -> str
sdk.cancel_job(job_id) -> bool
sdk.get_failure_analysis(job_id) -> dict | None
sdk.get_job_results_dir(job_id) -> str
sdk.check_path(remote_path) -> bool
sdk.list_path(remote_path) -> list[str]

Brev-only:

• sdk.delete_instance(instance_id) — clean up an ephemeral instance.
• sdk.list_instances() — list active instances.

Submitting a Job

The agent always constructs the container command via build_entrypoint before calling create_job. The agent reads the action's schema from skill_info.yaml (command, mode, config_format, inputs, outputs, upload_excludes) and passes those fields as kwargs. build_entrypoint then bakes:

1. The in-container script_runner runtime (inlined as a base64 heredoc — no need for tao_sdk to be installed in the container).
2. The CLI invocation that, at runtime in the container, will: download declared inputs (S3 / HF-Hub / NGC), write the spec file at {config_path} with remote URIs rewritten to local paths, run the user command, and upload outputs.

Output destinations are resolved at runtime from env vars the SDK injects (see "Where outputs go" below). The platform SDK's create_job runs the resulting command as-is — no inputs/outputs kwargs, no implicit wrapping. The data flow is visible in the agent's code.

Where outputs go (resolved at runtime — agents don't manage it)

The SDK injects TAO_JOB_ID (matches Job.id) and, when a persistent mount is attached, TAO_RESULTS_ROOT into the container env. Inside the container, script_runner resolves output destinations:

Per-platform policy:

Agents who want a custom destination can put an s3://... URI or absolute path directly at the output spec key — explicit values override the auto-fill. Otherwise, model-natural defaults like cosmos-rl's output_dir: "output" or DINO's empty results_dir are auto-rewritten by script_runner.

The spec is nested dicts, NOT flat dotted keys

This is the most common mistake when constructing a spec. The dotted notation that appears in skill_info.yaml's inputs: / outputs: blocks (e.g. section.subsection.key) is a path into a nested spec — script_runner looks values up at that path. It's not the spec's own shape. The spec mirrors whatever shape the model's container expects (typically a nested TOML/YAML).

# ✓ CORRECT — nested dicts
specs = {
    "section": {
        "subsection": {"key": "value"},
    },
}

# ✗ WRONG — flat top-level key with dots. TOML/YAML emits this as a
# quoted bare-string key, the model sees an empty `section` table, and
# any input declared at "section.subsection.key" silently fails to
# download because _get_nested(specs, "section.subsection.key") → None.
specs = {
    "section.subsection.key": "value",
}

The two shapes look superficially similar but mean different things. When in doubt, open the model's references/ directory (e.g. a default-spec TOML or YAML) — that's the literal nested structure the spec dict needs to mirror. The inputs: / outputs: declarations in skill_info.yaml are paths into the nested spec, not key names.

Constructing the spec / args

The skill's action declares its config mechanism in skill_info.yaml's actions.<action>.mode field. Treat missing mode as invalid metadata and fix the skill instead of inferring a default. Read actions.<action>.mode first, then pass the matching argument shape to build_entrypoint:

Do not infer mode from missing metadata. Missing mode means the skill contract is stale.

See references/spec-construction.md for the per-mode construction strategy, the recommended decision order, and worked build_entrypoint examples for spec-driven jobs (config file) and path-keyed jobs (no config file).

Resolving container images

Skills declare images either by key (tao_toolkit.pyt) or as an absolute URI (nvcr.io/...). Use resolve_container_image() to handle both:

from tao_sdk.versions import resolve_container_image
image = resolve_container_image(skill_info["container_image"])

Behind the scenes it walks versions.yaml for keys; absolute URIs are returned as-is.

Monitoring

status = sdk.get_job_status(job.id)
print(status.status)   # Pending, Running, Complete, Error, Canceled
print(status.message)  # platform-specific detail

logs = sdk.get_job_logs(job.id, tail=200)
print(logs)

On failure, get_failure_analysis() classifies the root cause:

analysis = sdk.get_failure_analysis(job.id)
if analysis:
    print(analysis["err_class"])   # ERR_PROGRAM, ERR_INFRA, etc.
    print(analysis["suggestion"])  # human-readable fix
    for event in analysis.get("job_failure_by_node_event", []):
        print(event["node_event_name"], event["message"])  # OOM, GPU error, etc.

Polling pattern

For interactive runs where the user wants to watch:

import time
status_interval_minutes = status_interval_minutes or 5
while True:
    status = sdk.get_job_status(job.id)
    if status.status in ("Complete", "Error", "Canceled"):
        break
    print(f"  {status.status}")
    time.sleep(status_interval_minutes * 60)

if status.status == "Error":
    print(sdk.get_job_logs(job.id, tail=100))
    print(sdk.get_failure_analysis(job.id))

With long-running monitoring enabled, do not stop after 30 minutes or after a few unchanged polls. Keep emitting updates every status_interval_minutes until the job finishes, fails, is canceled, or the user asks to detach/stop. If the chat/runtime cannot remain open that long, say so explicitly and provide the durable workflow/log path for manual status refresh.

Do not use a final response for non-terminal monitored jobs. Finalizing the turn detaches the chat watcher. Keep non-terminal status messages in progress updates and continue polling; only finalize at terminal state, explicit user detach/stop, or a real runtime limit that prevents further polling.

For background runs, persist job.id and the state_file path, then re-attach later by constructing the same SDK and calling get_job_status(job_id) — job state is read from the on-disk store.

Orchestration patterns

Multi-step workflows, parallel sweeps, and run-folder durability via ActionWorkflow live in references/orchestration-patterns.md. Read it before chaining create_job calls, sweeping a parameter, or persisting run state across context breaks.

Dataset utilities

When the skill's documented filenames don't match the user's layout, list the dataset to confirm:

assert sdk.check_path("s3://my-bucket/coco/")
files = sdk.list_path("s3://my-bucket/coco/train/")
# Use the actual paths to set spec fields.

For S3 paths, strip trailing slashes when concatenating to avoid //:

base = dataset_uri.rstrip("/")
specs["dataset"]["train_csv"] = f"{base}/train.csv"   # nested — see "spec is nested dicts"

Platform-specific notes

See references/platform-notes.md for per-platform behavior, kwargs, and credential scoping: Brev (instance_id/gpu_type/cloud_cred_id/workspace_group_id, ready-wait timeouts), SLURM (sbatch over SSH, Lustre paths, queue defaults), Kubernetes (kubeconfig, GPU Operator), and local Docker (single-host, multi-GPU).

Error patterns

SDK error → root cause → fix mappings are in references/error-patterns.md. Read when you hit a CredentialError, image-pull failure, stuck-Pending job, or similar — the entries map exception text to the underlying cause.

What the SDK does NOT do

The SDK does not read/interpret skills, run AutoML on its own, decide spec contents, select platforms, or orchestrate multi-step workflows — those stay the agent's responsibility. See references/scope.md for the full scope guardrails, including the model-level AutoML policy (automl_enabled: true → skills/applications/tao-run-automl unless automl_policy: off or the user asks for a plain single run).