--- name: datarobot-workload-api description: >- Use when the user wants to create, configure, scale, debug, observe, or roll out container workloads on DataRobot's Workload API. Triggers include: deploying a container as a managed service, listing/starting/stopping workloads, changing replica counts or autoscaling, picking CPU/GPU compute bundles, injecting DataRobot credentials as env vars, diagnosing workloads that are stuck / errored / crash-looping (CrashLoopBackOff, ImagePullBackOff, OOMKilled, probe failures, exec format error), pulling application logs / OpenTelemetry traces / metrics / request stats, creating or iterating container artifacts, building images server-side, locking artifacts for production, or doing a zero-downtime rolling artifact replacement. --- # DataRobot Workload API Run container images as managed, autoscalable services on DataRobot. One skill, four jobs — pick the section by user intent: 1. **Create / configure / scale** — deploy a container; change replicas, resources, autoscaling, bundle; inject credentials 2. **Diagnose** — workload is stuck, errored, or crash-looping 3. **Observe** — logs, traces, metrics, service stats for a running workload 4. **Artifact lifecycle** — iterate drafts, build images, lock for production, roll out new versions ## Prerequisites `DATAROBOT_ENDPOINT` (must end in `/api/v2`) and `DATAROBOT_API_TOKEN` must be set. Run `datarobot-setup` if not. Auth header: `Authorization: Bearer ${DATAROBOT_API_TOKEN}`. The Workload API is not in the `datarobot` Python SDK — call REST directly. **Transport.** Examples use Python `httpx` (`pip install httpx`). The API is plain HTTP, so equivalent calls work via `curl` or the `pulumi-datarobot` Pulumi provider declaratively. The skill teaches the model; transport is interchangeable. ## Bundled scripts Runnable Python in `scripts/` (this skill's folder). Each uses `httpx` and reads `DATAROBOT_ENDPOINT` + `DATAROBOT_API_TOKEN`: - `wait_for_running.py ` — poll until `running`; exit 2 on terminal failure, 3 on timeout - `diagnose_workload.py ` — run the 5-step debug flow, print a structured diagnosis (`--json` for machine-readable) - `wait_for_build.py ` — poll a server-side image build; dumps last 2KB of logs on `FAILED` - `wait_for_replacement.py ` — poll a rolling replacement; handles the 404-when-cleared case - `check_limits.py` — print the user's effective org-set scaling limits via `/account/info/` ## Deeper docs in references/ SKILL.md is the operational core; occasional detail lives in `references/`: - `status-vocabulary.md` — workload + proton status enums and transitions - `common-error-patterns.md` — CrashLoopBackOff / ImagePullBackOff / OOMKilled / probe / exec-format / pending - `schema-reference.md` — schemas to look up, credential-type→key maps, public-spec path quirks - `lifecycle-flows.md` — artifact draft→lock→prod rules, replacement preconditions, redeploy matrix, `imageUri` gotchas - `code-to-workload.md` — deploy from source: `dr` CLI, `codeRef`, Execution Environments, iterate-rebuild loop - `web-uis-behind-the-edge.md` — browser-facing web app through the endpoint: prefix stripping, auth gate, `Authorization` hijack, shim, CSRF, WebSockets ## OpenAPI spec is source of truth At `${DATAROBOT_ENDPOINT}/openapi.yaml`. **~5 MB — never dump it whole.** Save once, then slice with `yq` (or `print()` only the specific key in Python): ```bash curl -sS "${DATAROBOT_ENDPOINT}/openapi.yaml" -o /tmp/wapi-spec.yaml yq '.components.schemas.CreateWorkloadRequest' /tmp/wapi-spec.yaml yq '.components.schemas | keys | .[]' /tmp/wapi-spec.yaml | grep -i workload # discover ``` All workload paths are keyed with the `/api/v2/` prefix — see `references/schema-reference.md`. --- # 1. Create / configure / scale ## Run a container as a workload (the 90% case) ```yaml # spec.yaml — JSON also accepted; spec is sent verbatim name: my-api-service importance: low artifact: name: my-api-service-artifact spec: type: service containerGroups: - name: default containers: - name: main imageUri: ghcr.io/org/my-app:latest port: 8000 primary: true readinessProbe: {path: /readyz, port: 8000, initialDelaySeconds: 10} livenessProbe: {path: /healthz, port: 8000, initialDelaySeconds: 30} runtime: containerGroups: - name: default # must match artifact.spec.containerGroups[].name (above) replicaCount: 1 containers: - name: main resourceAllocation: {cpu: 1, memory: "512MB"} ``` ```bash dr workload create --spec-file spec.yaml # v0.2.74+; 4xx: 400=schema/limit, 403=cap (run check_limits.py), 409=name conflict dr workload get # or `dr workload status` — poll until status=running ``` Lifecycle one-liners (v0.2.74+): `dr workload {stop|start|delete|endpoint|list} `. Raw fallback when CLI unavailable: `httpx.post(f"{base}/workloads/", headers=headers, json=spec)` + `r.raise_for_status()` + `r.json()["id"]`. Then `python scripts/wait_for_running.py `. **Critical gotchas:** - `importance`: `low`/`moderate`/`high`/`critical`; `type`: `service` (default) or `nim`. Exactly one container per group has `primary: true`. - `cpu` is cores (float OK). `memory` accepts decimal string (`"512MB"`, units B/KB/MB/GB) or byte integer; Kubernetes binary suffixes (`Mi`/`Gi`) NOT supported. - `port` MUST be `>= 1024`. The container must actually listen on it (set via image env vars or entrypoint). - Image must include a **linux/amd64** manifest. Apple Silicon defaults to ARM64 and crash-loops with `exec format error`. Build with `docker buildx build --platform linux/amd64,linux/arm64 -t --push .`. - Status lifecycle: `submitted` → `provisioning` → `launching` → `running` (happy path); `updating` during rolling redeploys; `errored` recoverable; `failed`/`terminated` unrecoverable. Full table in `references/status-vocabulary.md`. ## Serving a browser-facing web UI through the endpoint If the container serves a **web app (UI + its own backend/API/WebSocket)** opened in a browser via `dr workload endpoint ` (not a headless service), the DataRobot edge gateway serves it under a path prefix and: **strips the prefix inbound** (no outbound rewrite — the app must be sub-path aware); **is the auth gate** (DataRobot login required) and **hijacks the `Authorization` header** (→ `401 {"message":"Invalid API key"}`, never reaching the container); **passes WebSockets through**. Winning pattern: set the app's base-path to the prefix + re-add it inbound (derive it from the injected `WORKLOAD_ID`), **disable the app's own auth (trust the edge)**, disable CSRF, probe an unauthenticated path. Full guidance, shim code, and per-symptom diagnostics: `references/web-uis-behind-the-edge.md`. ## "Update the workload" disambiguation | User intent | Endpoint | Effect | |---|---|---| | Rename / redescribe / change importance | `PATCH /workloads/{id}/` | Metadata only — no restart | | Change replicas / resources / autoscaling on the same artifact | `PATCH /workloads/{id}/settings/` | Triggers rolling redeploy | | Deploy a different artifact (new image / version) | `POST /workloads/{id}/replacement/` | Rolling swap — see section 4 | ## Replicas, resources, autoscaling `PATCH /workloads/{wid}/settings/` with full body shape — use exactly one of `replicaCount` or `autoscaling`. Read settings first via `GET /workloads/{wid}/settings/`, then PATCH back: ```python httpx.patch( f"{base}/workloads/{wid}/settings/", headers=headers, json={ "runtime": { "containerGroups": [ { "name": "default", "replicaCount": 3, "containers": [ { "name": "main", "resourceAllocation": {"cpu": 2, "memory": "1GB"}, } ], # OR: "autoscaling": {"enabled": True, "policies": [{ # "scalingMetric": "cpuAverageUtilization", # "target": 70, "minCount": 1, "maxCount": 10}]} } ] } }, ) ``` Valid `scalingMetric` values: `cpuAverageUtilization`, `httpRequestsConcurrency`, `gpuCacheUtilization`, `gpuRequestQueueDepth`, or a custom NIM metric. Settings updates are **rolling**; zero-downtime only with `replicaCount >= 2` (or autoscaling `minCount >= 2`). ## Org-set scaling limits — check before scaling Two admin-set caps: `maxConcurrentWorkloads` and `maxWorkloadReplicas`. Value `0` = unlimited; users can't change them. Read via **`GET /account/info/`** — response includes `{"limits": {"maxConcurrentWorkloads": N, "maxWorkloadReplicas": M}}` (or `python scripts/check_limits.py`). The spec's `/users/{uid}/` and `/organizations/{id}/` paths require Admin API access. Exceeding either limit returns **HTTP 403** with `{"detail": "Requested replicas (N) exceeds the maximum allowed (M)."}` — check limits first, then propose the max allowed or flag that admin help is needed. ## GPU type / VRAM — set via compute bundle, not direct `resourceAllocation` only accepts `cpu`, `memory`, `gpu` (count). There is NO `gpuType` or `gpuMemory` field. To target a GPU model / VRAM size: `GET /mlops/compute/bundles/` lists bundles (`cpu.small`, `gpu.l4.small`, `gpu.a10g.medium`); pass via `"resourceBundles": ["gpu.l4.small"]` (a list, but exactly ONE bundle allowed) under the container group. When a bundle is set, CPU/memory in `resourceAllocation` are ignored — the bundle defines them. ## Credential injection — never hardcode secrets DataRobot credentials are stored centrally and injected into `environmentVars` by reference: ```python "environmentVars": [ {"name": "PLAIN_VAR", "value": "literal-value"}, {"source": "dr-credential", "name": "AWS_ACCESS_KEY_ID", "drCredentialId": "", "key": "awsAccessKeyId"}, ] ``` Workflow: `GET /credentials/?limit=50` → note the credential's `credentialType` → look up the valid `key` field names for that type in `references/schema-reference.md` (covers `s3`, `basic`, `api_token`, `bearer`, `oauth`, `gcp`, `azure_*`, `databricks_*`, `snowflake_*`, …). ## Create from an existing artifact Provide `artifactId` instead of the inline `artifact` block. The `containerGroups[].name` and `containers[].name` in `runtime` must match what the artifact defines. --- # 2. Diagnose — workload is stuck, errored, or crash-looping ## One command for the full diagnosis ```bash python scripts/diagnose_workload.py ``` Runs all 5 steps below, prints a structured report (status / logTail signals / flagged events / proton K8s detail / evidence / recommended next step / console URL). `--json` for machine-readable. If `Evidence` is empty, pull application logs via section 3 — don't guess from status alone. ## The 5-step flow The script encapsulates this; use the model below for ambiguous output or one-off calls. 1. **`GET /workloads/{id}/`** — `status`, `statusDetails.logTail` (~30 lines; scan for `error`/`exception`/`traceback`/`killed`/`permission denied`/`connection refused`), `statusDetails.conditions`. Guard `statusDetails` — it's `null` during `submitted`/`provisioning`. 2. **`GET /workloads/{id}/events/`** — flag `type: Warning` or `reason` with `Failed`/`Error`/`Kill`/`OOM`; the last Warning before `errored` is usually the trigger. 3. **`GET /workloads/{id}/protons/`** — pick `role: "active"` (or the `candidate` during a rolling replacement; else newest `createdAt`). 4. **`GET /workloads/{id}/protons/{pid}/statusDetails/`** — `204` while initializing (not an error). Read `replicas[*].containers[*].status`+`restartCount` → `replicas[*].conditions[*]` (any `value:false`) → `overallStatus.summary`. 5. **Application logs** — section 3. Common patterns (`CrashLoopBackOff`, `ImagePullBackOff`, `OOMKilled`, probe/pending, `exec format error`) and fixes: `references/common-error-patterns.md`. ## Reporting findings ``` Workload {id} — Diagnosis - Status: {current} - Root cause: {one sentence} - Evidence: {the specific logTail line, condition, container reason, or event} - Recommended fix: {actionable next step — section 1 (settings), section 4 (artifact), or app code} - Console: https://app.datarobot.com/console-nextgen/workloads/{id}/overview ``` --- # 3. Observe — logs, traces, metrics, service stats | Stream | Endpoint | Needs app instrumentation? | |---|---|---| | Logs | `/otel/workload/{id}/logs/` | No — auto from stdout/stderr | | Traces | `/otel/workload/{id}/traces/` | **Yes** (OTEL spans) | | Metrics | `/otel/workload/{id}/metrics/autocollectedValues/` | Partially | | Service stats | `/workloads/{id}/stats/` | No — DataRobot edge proxy | | Replacement history | `/workloads/{id}/history/` | No — platform | | Lifecycle events | `/workloads/{id}/events/` | No — platform | Always check `r.status_code` before `.json()`: 401 = bad token; 404 = workload not found; 429 = rate limited (exponential backoff). All list endpoints accept `limit` + `offset`. ## Logs ```bash dr workload logs --level error --limit 100 # v0.2.74+; --follow streams; --output-format json ``` `--level` is an EXACT severity match (not a threshold). For substring filtering on the message body, or proton-scoped logs (find proton IDs in section 2), drop to REST — `dr workload logs` doesn't expose those filters: ```python r = httpx.get( f"{base}/otel/workload/{wid}/logs/", headers=headers, params=[ ("searchKeys", "proton_id"), ("searchValues", pid), ("searchKeys", "level"), ("searchValues", "error"), ], ) ``` `searchKeys` / `searchValues` are positional parallel lists — pass a **list of tuples** to httpx (dict can't repeat keys). `includes=` does case-sensitive substring filtering on the message body. ## Traces ```python traces = httpx.get(f"{base}/otel/workload/{wid}/traces/", headers=headers).json()[ "data" ] # summary: traceId, rootSpanName, rootServiceName, duration (NANOSECONDS), spansCount, errorSpansCount trace_id = next( (t["traceId"] for t in traces if t.get("errorSpansCount", 0) > 0), traces[0]["traceId"], ) trace = httpx.get( f"{base}/otel/workload/{wid}/traces/{trace_id}/", headers=headers ).json() ``` > **`duration` is NANOSECONDS** on summaries AND spans. Divide by 1,000,000 for ms before display. Empty `data` = app isn't instrumented; direct the user to wire up OTEL. ## Metrics + service stats Convert before display: `bytes`→MB (`/1024**2`), `nanocores`→cores (`/1_000_000`), `percentage` already %. ```python stats = httpx.get(f"{base}/workloads/{wid}/stats/", headers=headers).json() # {"period": {...}, "metrics": {totalRequests, serverErrors, userErrors, slowRequests, # responseTime, requestsPerMinute, concurrentRequests, *ErrorRate}}. /workloads/stats/ = aggregate. ``` > **Destructive:** `DELETE /workloads/{id}/stats/?metricName=` zeroes a metric's history — only on explicit request. ## Presenting results Logs: `timestamp | level | message`, ERROR/CRITICAL first. Traces: table sorted by errors desc then recency. Metrics: apply unit conversion before display. Service stats one-liner: *"`{totalRequests}` requests, `{totalErrorRate*100:.2f}%` errors, `{responseTime:.1f}` ms avg, `{requestsPerMinute}` req/min."* Empty data → say *why* (not running, not instrumented, empty window), don't just "no data". --- # 4. Artifact lifecycle An **artifact** is the immutable-after-lock definition of what a workload runs (image, port, env vars, probes). A **workload** is the running instance + its runtime (replicas, resources, autoscaling). Resources do NOT belong on the artifact. ## Picking the right path Find the running artifact (`workload["artifactId"]`), check `artifact["status"]`. A running workload does **not** auto-adopt a rebuild until you redeploy. - **Same draft (the C2W loop) — in-place change or rebuild.** PATCH/rebuild the draft, then roll onto it with `PATCH /workloads/{id}/settings/`: re-send the runtime body (even unchanged values trigger a rolling `202` redeploy that re-reads the current spec + latest `COMPLETED` build). Zero-downtime at ≥2 replicas. (`POST /replacement/` onto the same draft also works.) - **Different / locked artifact.** `POST /replacement/` onto the other artifact ID. Locked in-place edit: clone → PATCH clone → lock → replace onto the clone. **Lock:** `dr artifact lock ` (= `PATCH /artifacts/{id}/ {"status":"locked"}`). **Promote** (`POST /workloads/{wid}/promote/`, 200) locks the running draft in place, no restart. Runtime-only changes (replicas/resources/autoscaling) → `PATCH /settings/`; a PATCH to the artifact doesn't affect live workloads until you redeploy. Preconditions (status-match, same-artifact rule) and the full redeploy matrix: `references/lifecycle-flows.md`. ## How does your image get to DataRobot? The artifact's `imageUri` must point at a registry DataRobot can pull from (image-pull creds aren't accepted at workload creation yet). Two paths: 1. **Bring your own image** — public registry or one the admin pre-configured. `docker buildx ... --platform linux/amd64`, push, set `imageUri`. Default flow. 2. **Code-to-Workload (C2W)** — no local Docker / no public registry: `dr artifact code init` + `sync`, then `dr artifact build create` builds server-side, pushes to DataRobot's internal registry, and populates `imageUri`. Full flow in `references/code-to-workload.md`. Poll builds with `python scripts/wait_for_build.py `; only drafts build. **`imageUri` is build-managed** — never PATCH it by hand (`422` "not permitted on this cluster"), and never PATCH the spec *mid-build* (a whole-spec write clobbers the pending build image → redeploys the old one). Sequence spec edits before `build create` or after `COMPLETED`. > **C2W is preview / feature-flagged** — `ENABLE_WORKLOAD_API_CONTAINERS=true` (org) + `DATAROBOT_CLI_FEATURE_WORKLOAD=true` (client). ## Rolling artifact replacement ```python httpx.post( f"{base}/workloads/{wid}/replacement/", headers=headers, json={ "artifactId": new_artifact_id, "strategy": "rolling", # only "rolling" supported "config": {"warmupDurationMinutes": 2, "keepOldVersionMinutes": 5}, # optional # "runtime": {...} # optional; same shape as PATCH /settings/ }, ) ``` Monitor with `python scripts/wait_for_replacement.py `. Preconditions: status must match (draft↔draft / locked↔locked, else `400`); same-artifact replacement 422s for locked but works for drafts — to roll the same draft without replacement use `PATCH /settings/`. **Not idempotent** (a second `POST` queues another swap); `GET .../replacement/` `404` = none in progress; `DELETE` to cancel. Detail in `references/lifecycle-flows.md`. --- ## Related skills - `datarobot-setup` — install SDK, configure auth, set env vars - `datarobot-app-framework-cicd` — declarative artifact + workload management via Pulumi and CI/CD - `datarobot-external-agent-monitoring` — instrument arbitrary agent code with OTEL → DataRobot