TAO Inference Microservice

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Instructions

To start an inference service:

1. Collect required inputs (Section 1) and resolve the container image (Section 2).
2. Build the job payload and inner command (Sections 3–4.1); use references/code-templates.yaml → job_payload_builder.
3. Read skills/platform/<platform>/SKILL.md and start the container (Section 4.2).
4. Write the service registry and poll readiness (Section 4.3); use references/code-templates.yaml → registry_write.<platform> and readiness_check.

To send an inference request:

1. Resolve which service receives the request per Section 6.0 (by job_id, by network_arch, or by explicit user choice when multiple services run — never silently default to "latest" when more than one service exists), then read the endpoint from references/code-templates.yaml → request.registry_read with the resolved job_id.
2. Before building the request body, prompt the user for the vLLM-style sampling parameters (Section 6.1). Present max_tokens, top_p, temperature (and any per-arch extras) with their defaults; let the user override or skip each one to accept the default. Never silently use defaults.
3. Build and send the body per Section 6.2; handle the response per Section 6.3.

To stop a service: Read references/code-templates.yaml → stop.registry_read to resolve the job_id, read skills/platform/<platform>/SKILL.md, then follow Section 5.

Reference data (schemas, mappings, valid values — no instructions):

• references/service.yaml — image mappings, valid network_arch names, job payload schema, env var names, secrets classification.
• references/request.yaml — endpoint definition, request field schema, response shapes, code examples.
• references/code-templates.yaml — Python templates for payload building, registry writes, readiness checks, and stop/request flows.

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Secrets rule (applies to every generated code block in this skill)

Never ask the user to type a secret value into a prompt. For every secret value:

1. Tell the user which environment variable to set — export HF_TOKEN=... in their shell, or a KEY=value line in a user-approved env file loaded with set -a; source /path/to/.env; set +a.
2. Never print, cat, or Read such a file — verify presence only: [ -n "$HF_TOKEN" ] && echo SET || echo UNSET.
3. Generate code that reads it with os.environ["VAR_NAME"] — never hard-code, interpolate, or prompt for the value.

Secret env vars (full list in references/service.yaml → secrets_handling): HF_TOKEN, WANDB_API_KEY, CLEARML_API_ACCESS_KEY, CLEARML_API_SECRET_KEY, TAO_API_KEY, TAO_USER_KEY.

Safe to collect in the prompt: network_arch, model_path, num_gpus, prompt text, WANDB_* config URLs, CLEARML_*_HOST URLs.

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1. What to collect from the user

Input	Role
network_arch	Chooses container image, the per-arch inner command shape (references/service.yaml → container_commands.<network_arch>), and neural_network_name in the job JSON when applicable. Must match a basename in valid_network_arch_config_basenames in references/service.yaml (e.g. cosmos-rl, cosmos-predict2.5).
model_path	The trained model checkpoint. Valid forms: hf_model://<org>/<model> (HuggingFace Hub — set HF_TOKEN for gated models) or a local container filesystem path. Cloud URIs (s3://, gs://, az://) are NOT supported — the inference service has no cloud-storage dependency. Always ask the user; never substitute a placeholder. See references/service.yaml → model_path_protocols.
platform	Compute platform: local-docker, brev, slurm, or kubernetes.
num_gpus	Defaults to 1; minimum 1 for inference.

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2. Image resolution

Each network_arch has a sidecar config file named {network_arch}.config.json. Resolve the container image as follows:

1. Read {network_arch}.config.json and take api_params.image (e.g. COSMOS_RL). This selects an entry from docker_image_defaults in references/service.yaml.
2. If the host env var IMAGE_<KEY> is set (e.g. IMAGE_COSMOS_RL), it overrides the packaged default.
3. For a key under model_skill_defaults, call scripts/resolve_tao_image.py with its model, action, and backend. This keeps the exact Cosmos image in the Cosmos model skill's references/skill_info.yaml.
4. For a key under mapping, resolve the dotted value against the repo-root versions.yaml with scripts/resolve_versions_key.py. Absolute environment overrides pass through unchanged. The Python examples live in references/code-templates.yaml.
5. If the config file is missing or api_params.image is empty, fall back to the COSMOS_RL key.

The config file also has spec_params.inference.model_path which drives folder vs file path semantics: if the value contains the substring folder, the container treats the path as a directory.

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3. Environment variables (no callbacks)

Set these in env_payload before encoding env_json. Do not set TAO_LOGGING_SERVER_URL or TAO_ADMIN_KEY.

TAO_EXECUTION_BACKEND — must match the platform:

Platform	TAO_EXECUTION_BACKEND value
local-docker	local-docker
brev	local-docker
slurm	slurm
kubernetes	local-k8s

CLOUD_BASED — always "False" for this skill (disables callback posting to TAO_LOGGING_SERVER_URL).

GPU env vars — only needed when the platform skill does not handle GPU injection automatically:

• Tegra / Jetson: --runtime=nvidia with NVIDIA_DRIVER_CAPABILITIES=all and NVIDIA_VISIBLE_DEVICES=<ids>.
• Standard x86 + nvidia-container-toolkit: use Docker device_requests. The platform skill handles this.

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4. Executing across platforms

The job payload and inner command (Sections 1–3) are platform-agnostic. For each platform, read skills/platform/<name>/SKILL.md for preflight checks and credentials before generating any execution code.

4.1 Build the inner command (per arch)

The inner-command shape is per network_arch — there is no uniform template. Look up the per-arch entry in references/service.yaml → container_commands.<network_arch>; if not present, the arch is unsupported — stop and ask. Pick the matching sub-block in references/code-templates.yaml → job_payload_builder.<network_arch>. Prefix the command with umask 0 && and keep it identical across platforms (local-docker, brev, slurm, kubernetes).

Common across arches:

• job_id: fresh uuid.uuid4() — becomes the container name and registry key.
• image: resolve per Section 2.
• Secrets (access_key, secret_key, HF_TOKEN, etc.) are read from env vars at runtime — never hard-code, never log or print.

Arch-specific notes (full details in references/service.yaml → container_commands):

• cosmos-rl — single --job '<JOB_JSON>' --docker_env_vars '<ENV_JSON>' blob; json.dumps(...) + shlex.quote(...). env_payload carries TAO_EXECUTION_BACKEND (per Section 3 table), TAO_API_JOB_ID, CLOUD_BASED=False. The inference service has no cloud-storage dependency; HF_TOKEN is the only cred env var that ever applies (for gated HuggingFace models).
• cosmos-predict2.5 — flag-style cosmos_predict inference_microservice start ... --port 8080 (no setup. prefix; uses tyro.conf.OmitArgPrefixes). --job/--docker_env_vars are not accepted. Translate model_path to --checkpoint-path (local path) or --model <registered_key> (hf_model://); cloud URIs are rejected. The only cred env var that ever applies is HF_TOKEN for gated HuggingFace models. Per-request params (prompt, inference_type, num_output_frames, guidance, seed, num_steps, negative_prompt) go in the request body, not at startup. TAO_EXECUTION_BACKEND/TAO_API_JOB_ID/CLOUD_BASED are unused and may be omitted.

4.2 Delegate execution to the platform skill

Read skills/platform/<platform>/SKILL.md and follow it to start the container.

Base parameters (all platforms):

Parameter	Value
image	resolved container image (Section 2)
command	inner — the shell string built in Section 4.1
gpu_count	num_gpus
env_vars	env_payload
job / container name	job_id — must equal the UUID from 4.1 so the registry can reference it
host_port (local-docker, brev)	host-side port to bind to container port 8080. Default 8080, but must be unique per concurrent service — see the port-allocation rule below.

Platform-specific additional inputs:

Platform	Additional inputs
local-docker	None beyond base
brev	instance_id (optional — reuse an existing instance); on multi-credential / multi-workspace accounts also cloud_cred_id and workspace_group_id for first-create — see skills/platform/tao-run-on-brev/SKILL.md
slurm	partition and account — check SLURM_PARTITION/SLURM_ACCOUNT env vars; ask user if unset
kubernetes	namespace (default: default); image_pull_secret (required for nvcr.io images)

Port binding (local-docker and brev): use direct docker run so that -p <host_port>:8080 can be passed and the container name equals job_id exactly.

Port allocation rule (local-docker and brev, REQUIRED for concurrent services): Before starting a service, read the registry (/tmp/tao-inf-ms-state.json) and collect the set of host_port values from every existing entry on the same platform (and, for brev, the same instance_id). Pick the lowest free port starting from 8080 that is not in that set — e.g. host_port = next(p for p in range(8080, 8200) if p not in used_ports). The default 8080 only applies when no other service is running. This is what makes "start 3 services, each reachable at a distinct host_url" work; without it, services 2 and 3 fail with bind: address already in use. SLURM and kubernetes get distinct endpoints from their own platform mechanisms and do not need this step.

4.3 After start: service registry and endpoint

Write the service registry immediately after the platform confirms the container is running. The registry (/tmp/tao-inf-ms-state.json) is keyed by job_id; "latest" always points to the most recently started service.

See references/code-templates.yaml → registry_write.<platform> for the Python template.

Platform	host_url	platform_job_id	Extra step before writing
local-docker	http://localhost:{host_port}	—	None
brev	http://{brev_ip}:{host_port}	—	brev ls → get instance IP (localhost is invalid on remote VM)
slurm	http://localhost:{host_port}	SLURM scheduler job ID	Wait until Running; SSH port-forward localhost:{host_port}→{node}:8080
kubernetes	http://{external_ip}:8080	k8s job name	kubectl expose job … --type=LoadBalancer; wait for external IP

After writing the registry, print the job_id and URL:

print(f"Inference service started.")
print(f"  Job ID : {job_id}")
print(f"  Arch   : {network_arch}")
print(f"  URL    : {state[job_id]['host_url']}/v1/chat/completions")
print(f"Use this Job ID to send requests or stop the service.")

Then poll for readiness — see references/code-templates.yaml → readiness_check. The container loads the model in the background; do not send requests before it returns 200.

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5. Stopping the inference service

Ask the user for the job_id to stop. If they don't provide one, default to state["latest"] and confirm which job_id is being stopped. Read the registry using references/code-templates.yaml → stop.registry_read, then read skills/platform/<platform>/SKILL.md and use its cancellation / stop mechanism.

Platform	Identifier to pass	Extra cleanup
local-docker	job_id_to_stop — container name	None
brev	job_id_to_stop — container name	None
slurm	entry["platform_job_id"] — SLURM job ID	pkill -f "ssh.*-L.*{entry['host_port']}"
kubernetes	entry["platform_job_id"] — k8s job name	kubectl delete svc {entry["platform_job_id"]} -n <namespace>

where entry = state[job_id_to_stop]. After stopping, clean up the registry: references/code-templates.yaml → stop.registry_cleanup.

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6. Sending inference requests

6.0 Resolve which service receives this request (REQUIRED)

Each request must be routed to the specific service that runs the matching model. Routing happens by job_id — the registry stores network_arch per entry, so you can resolve a target by arch when the user names a model instead of a job_id. Apply these rules in order:

1. User provided an explicit job_id → use it. Verify it exists in state.
2. User named a network_arch (e.g. "send this to the cosmos-rl service") → look up matching entries: candidates = [j for j, e in state.items() if j != "latest" and isinstance(e, dict) and e["network_arch"] == arch].
   • Exactly one match → use it.
   • Multiple matches → prompt the user with the candidate job_ids and their started_at; do not auto-pick.
   • No match → stop and tell the user no service for that arch is running.
3. No job_id and no network_arch → count non-"latest" entries in state:
   • Exactly one running service → use it.
   • Two or more → do not silently default to state["latest"]. Prompt the user with the full list (job_id, network_arch, host_url) and require an explicit choice. The "latest" pointer is a convenience for single-service workflows, not a routing fallback when multiple services coexist.
   • Zero → stop and tell the user to start a service first.

After resolving, read the endpoint from the registry (references/code-templates.yaml → request.registry_read), passing the resolved job_id as user_provided_job_id. Confirm to the user: "Sending to job_id=… arch=… url=…". If the service may still be loading, poll readiness first (references/code-templates.yaml → readiness_check).

Cross-check before sending: if the user-supplied request body contains arch-specific fields (e.g. guidance / num_steps / seed / negative_prompt → cosmos-predict2.5; required image_url/video_url content items → cosmos-rl), verify they are consistent with state[job_id]["network_arch"]. On mismatch, stop and ask — sending a cosmos-predict2.5 body to a cosmos-rl service will fail at the container with a 4xx/5xx that is harder to diagnose than catching it here.

6.1 Sampling parameters — REQUIRED user prompt before each request

Before constructing the request body, you MUST explicitly prompt the user for the vLLM-style sampling parameters. Do not silently apply defaults. Use a structured prompt, one question per field, that:

1. Lists every applicable field with its type and default value.
2. Lets the user skip / accept any field to take that field's default — entering a value is never required.
3. Collects all fields in one round.

After the prompt, apply each user-entered value verbatim and substitute the default for any skipped field. Do not invent values or silently clamp.

Field list, defaults, and per-arch applicability: references/request.yaml → chat_completions_request_body (base sampling fields: max_tokens, top_p, temperature) and network_arch_constraints.<network_arch> (per-arch overrides and extras such as guidance/num_steps/seed/negative_prompt for cosmos-predict2.5). If a field is marked unsupported for the active arch, do not prompt for it and do not include it in the body.

6.2 Request format

Send a POST to {BASE_URL}/v1/chat/completions with Content-Type: application/json and a timeout of at least 300 s. The body is OpenAI-compatible (vLLM chat completions); see references/request.yaml → chat_completions_request_body for the full field schema and content-item shapes (text / image_url / video_url), and code_examples for ready-to-run Python and curl samples.

Constraints: only the first user message is processed. No secret values in request bodies. Per-network constraints (e.g. cosmos-rl requires every request to include an image or video; cosmos-rl rejects data: URIs) are in references/request.yaml → network_arch_constraints.

6.3 Response handling

HTTP status	Meaning	Action
200	Success — choices[0].message.content has the generated text	Read result
202	Server still initializing or model still loading	Retry after a delay
503	Initialization failed, model load failed, or model not yet ready	Inspect error.type: model_not_ready → retry; initialization_error / model_load_error → give up and check logs
400	Missing or empty JSON body	Fix request
500	Unhandled exception during inference	Check container logs

For 202 and 503, the body contains {"error": {"type": "<error_type>", "message": "<reason>"}}. See container_response_shapes in references/request.yaml for error type strings.