Datarobot Workload Api

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/

The SKILL.md is the operational core. Detail an agent needs occasionally lives in references/:

• references/status-vocabulary.md — workload + proton status enums and lifecycle transitions
• references/common-error-patterns.md — CrashLoopBackOff / ImagePullBackOff / OOMKilled / probe failures / exec format error / pending pods
• references/schema-reference.md — OpenAPI schemas worth looking up, credential type → key mappings, public-spec path-key quirks
• references/lifecycle-flows.md — artifact draft → lock → production flow rules and behavioral gotchas
• references/code-to-workload.md — deploy from source code (no Dockerfile authoring): dr CLI commands, codeRef, Execution Environments, generated vs provided Dockerfile modes, iterate-rebuild loop

OpenAPI spec is source of truth

Published at https://docs.datarobot.com/en/docs/api/reference/public-api/openapi.yaml. The spec is ~5 MB — never dump it whole into context. Save once, then extract targeted slices with yq:

curl -sS "${DATAROBOT_ENDPOINT}/openapi.yaml" -o /tmp/wapi-spec.yaml
yq '.components.schemas.CreateWorkloadRequest' /tmp/wapi-spec.yaml
yq '.paths."/api/v2/workloads/{workload_id}/".patch' /tmp/wapi-spec.yaml
yq '.components.schemas | keys | .[]' /tmp/wapi-spec.yaml | grep -i workload   # discover

Python fallback: only print() the specific key, never the parsed dict. All workload paths in the public spec are keyed with /api/v2/ prefix — see references/schema-reference.md.

---

1. Create / configure / scale

Run a container as a workload (the 90% case)

# 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"}

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.

"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:

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:

"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

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; here's the model for ambiguous output or one-off calls.

1. GET /workloads/{id}/ — status, statusDetails.logTail (~30 lines), statusDetails.conditions. Scan logTail for error / exception / traceback / killed / permission denied / connection refused. Guard statusDetails with (w.get("statusDetails") or {}) — it can be null during submitted / provisioning.
2. GET /workloads/{id}/events/ — flag any event with type: Warning or reason containing Failed / Error / Kill / OOM. The last Warning before errored is usually the trigger.
3. GET /workloads/{id}/protons/ — response: {"data": [{"id": "...", "role": "active"|"candidate"|"retiring", "createdAt": "..."}]}. Pick role: "active"; during a rolling replacement debug the candidate if that's what's failing. If no active role, take the most recent createdAt.
4. GET /workloads/{id}/protons/{pid}/statusDetails/ — returns 204 while still initializing; that's not an error. Once populated, read in this order: replicas[*].containers[*].status + restartCount (the headline) → replicas[*].conditions[*] (any value: false is a smoking gun) → overallStatus.summary (DataRobot's human-readable interpretation).
5. Application logs — section 3.

Common patterns (CrashLoopBackOff, ImagePullBackOff, OOMKilled, probe failures, pending pods, exec format error) and their fix paths: 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

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:

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

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

Metrics unit conversions before display: bytes → MB (/ 1024**2); nanocores → cores (/ 1_000_000); percentage already a %.

Service stats response shape:

stats = httpx.get(f"{base}/workloads/{wid}/stats/", headers=headers).json()
# Returns {"period": {start, end}, "metrics": {totalRequests, serverErrors, userErrors,
#   slowRequests, responseTime, requestsPerMinute, concurrentRequests, totalErrorRate,
#   serverErrorRate, userErrorRate}}.  /workloads/stats/ (aggregate) same shape.

> Warning — destructive. DELETE /workloads/{id}/stats/?metricName= zeroes a metric's history. Only call when the user explicitly asks to reset stats.

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

To change a running workload's image / env vars / probes / port, find its current artifact (workload["artifactId"]) and check artifact["status"]. If draft: PATCH in place → POST /workloads/{id}/replacement/. If locked: clone → PATCH the clone → lock it → POST /workloads/{id}/replacement/.

Lock an artifact: dr artifact lock (v0.2.74+) — equivalent to PATCH /artifacts/{id}/ {"status": "locked"}. No POST /artifacts/{id}/lock/. For a draft already running on a workload, use promote (no restart).

Replacement status-match rule: 400 {"detail": "Artifact status mismatch: ..."} unless the new artifact's status matches the running one's. draft↔draft, locked↔locked. Check before calling.

Promote (no restart): POST /workloads/{wid}/promote/ (empty body, returns 200) locks the draft the workload is currently running, in place. No pod restart. Only valid if the workload already runs that artifact — for a different one, use replacement.

For runtime-only changes (replicas / resources / autoscaling), use section 1's PATCH /workloads/{id}/settings/; don't touch the artifact. Patching an artifact does NOT affect running workloads — trigger a replacement (or promote) to apply changes to live workloads. Full code in references/lifecycle-flows.md.

How does your image get to DataRobot?

The Workload API does **not yet accept i

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: datarobot-oss
• Source: datarobot-oss/datarobot-agent-skills
• License: Apache-2.0
• Homepage: https://datarobot.com

Install and usage instructions live in the source repository linked above.