> ## Documentation Index > Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create Dedicated Run > Dispatch a dedicated full-FT prime-rl run on a registered PrimeCluster. Access is gated by ClusterAllocation - the picker in `training_service.create_dedicated_run` rejects callers whose team/user has no matching allocation on a live PrimeCluster. When `team_id` is set the caller must additionally be a member of that team (mirrors the LoRA/shared dispatch path). Config validation (validator schema, hub env ids, HF repo names, per-run GPU cap) runs sync so bad configs return 400 immediately. Cluster-side probes (GPU capacity, model cache) and the helm install run async in a Cloud Task; callers poll `GET /api/v1/rft/runs/{run_id}` for status transitions PENDING -> CREATING -> RUNNING (or FAILED with errorMessage on a cluster-side rejection). ## OpenAPI ````yaml https://api.primeintellect.ai/openapi.json post /api/v1/training/runs openapi: 3.1.0 info: title: PI API version: 0.1.0 servers: - url: https://api.primeintellect.ai security: [] paths: /api/v1/training/runs: post: tags: - training summary: Create Dedicated Run description: |- Dispatch a dedicated full-FT prime-rl run on a registered PrimeCluster. Access is gated by ClusterAllocation - the picker in `training_service.create_dedicated_run` rejects callers whose team/user has no matching allocation on a live PrimeCluster. When `team_id` is set the caller must additionally be a member of that team (mirrors the LoRA/shared dispatch path). Config validation (validator schema, hub env ids, HF repo names, per-run GPU cap) runs sync so bad configs return 400 immediately. Cluster-side probes (GPU capacity, model cache) and the helm install run async in a Cloud Task; callers poll `GET /api/v1/rft/runs/{run_id}` for status transitions PENDING -> CREATING -> RUNNING (or FAILED with errorMessage on a cluster-side rejection). operationId: create_dedicated_run_api_v1_training_runs_post requestBody: content: application/json: schema: $ref: '#/components/schemas/CreateDedicatedRunRequest' required: true responses: '201': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/CreateDedicatedRunResponse' '401': description: Authorization failed '422': description: Invalid request data content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' security: - HTTPBearer: [] components: schemas: CreateDedicatedRunRequest: properties: config: type: object title: Config description: >- Full prime-rl-style TOML, parsed to a dict. Same shape as `prime-rl/examples/*/rl.toml`; the platform splits it into trainer / orchestrator / inference subconfigs and bakes each into the corresponding pod's startup command. mode: type: string enum: - rl - sft title: Mode description: >- Which prime-rl schema the `config` blob follows. 'rl' (default) is the RL mega-TOML (trainer + orchestrator + inference). 'sft' is the SFT mega-TOML (top-level model/data/optim blocks, trainer-only deployment): the validator validates it against SFTConfig and renders a trainer-only Helm release (no orchestrator, inference, or env-server pods). The caller declares the mode; the backend never sniffs the config shape. default: rl imageTag: anyOf: - type: string - type: 'null' title: Imagetag description: >- prime-rl container image tag on ghcr.io/primeintellect-ai/prime-rl. Omit to use the deployment default: the operator-configured RL_FFT_DEFAULT_IMAGE_TAG when set, otherwise the validator's pinned build for RL runs and `main` for SFT runs. An explicit value, including `main`, is always used as-is. sourceRef: anyOf: - type: string - type: 'null' title: Sourceref description: >- prime-rl git ref (branch, tag, or sha in the canonical PrimeIntellect-ai/prime-rl repo) the pods check out over the image's baked source at start, so a branch can be tested without building an image. The validator resolves it to a commit and validates the config against that commit's schema; the resolved sha is pinned on the run. Not generally available: only team runs (teamId) may send it, the team needs sourceRef access granted (contact support), and the backend needs RL_SOURCE_REF_ENABLED. name: anyOf: - type: string - type: 'null' title: Name description: Optional human-readable run name teamId: anyOf: - type: string - type: 'null' title: Teamid description: Owning team (defaults to caller's user) wandbApiKey: anyOf: - type: string - type: 'null' title: Wandbapikey description: >- W&B key. On the PENDING fast-path it's materialised straight into the run's k8s Secret. On the QUEUED path it's stashed AES-encrypted on the RFTRun row so the drainer can rebuild the same Secret at promotion time without re-prompting. hfToken: anyOf: - type: string - type: 'null' title: Hftoken description: >- HF token for gated/private model downloads. Same storage rules as wandbApiKey above. secrets: anyOf: - additionalProperties: type: string type: object - type: 'null' title: Secrets description: >- Arbitrary env-var secrets projected into trainer / inference / orchestrator / env-server pods (parity with the LoRA path). Keys must be uppercase POSIX env-var names (e.g. 'OPENAI_API_KEY'); WANDB_API_KEY / HF_TOKEN / PRIME_API_KEY are reserved — use the dedicated fields for those. Materialised into the per-run k8s Secret. Held encrypted on the RFTRun row only for the sync -> dispatch-task hop, then cleared once the run deploys (or on stop / delete / rollback); never returned by any read endpoint. volume: anyOf: - type: string - type: 'null' title: Volume description: >- Name of a volume in the caller's team (or personal) namespace. The run writes its outputs to `runs//` on it instead of to a per-run PVC. gpuType: anyOf: - type: string - type: 'null' title: Gputype description: >- Optional GPU type constraint (e.g. 'H200_141GB', 'B200_180GB'). When set, dispatch is restricted to PrimeClusters whose `gpuType` matches. When omitted, the picker chooses the oldest eligible cluster with no type preference. type: object required: - config title: CreateDedicatedRunRequest CreateDedicatedRunResponse: properties: runId: type: string title: Runid jobId: type: string title: Jobid tokenValue: type: string title: Tokenvalue description: >- PRIME_API_KEY for this run. Returned once — the platform stores only the token id. type: object required: - runId - jobId - tokenValue title: CreateDedicatedRunResponse ErrorResponse: properties: errors: items: $ref: '#/components/schemas/ErrorDetail' type: array title: Errors type: object required: - errors title: ErrorResponse ErrorDetail: properties: param: type: string title: Param details: type: string title: Details type: object required: - param - details title: ErrorDetail securitySchemes: HTTPBearer: type: http scheme: bearer ```` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.