Retrieve full training job detail with checkpoints and metrics

Use this endpoint to retrieve the full state of a training job. The response includes LoRA config, hyperparameters, the latest progress snapshot, MLflow run links, and any error messages.

Method + Path

GET /v1/jobs/:id

Authentication

Include your PIN API token or JWT in the Authorization header as a Bearer token.

Authorization: Bearer <token>

Path parameters

string

required

The job identifier (e.g., jb_abc123def456).

Request example

curl -s "https://api.ariacompute.com/v1/jobs/jb_abc123def456" \
  -H "Authorization: Bearer $PIN_API_TOKEN"

Response

Returns a standard PIN success envelope with the job object under data.

integer

required

0 on success, nonzero on error.

object

required

Show job object

string

Unique job identifier.

string

Training type: sft, opd, pretrain, distill, qat, grpo, dpo, kto, orpo, simpo, ppo.

string

Base model from the catalog.

string

Frozen teacher for OPD/distill.

object

LoRA/QLoRA/DoRA configuration.

Show properties

string

lora, qlora, dora, or none.

integer

LoRA rank (1 to 64).

integer

LoRA alpha (1 to 128).

number

Dropout rate (0 to 0.5).

array

Target module names.

string

4bit_nf4 for QLoRA.

object

Training hyperparameters.

object

Auto-intervention config if enabled.

string

Linked dataset ID.

string

Resolved storage path or HF repo.

string

Hugging Face dataset repo.

string

Dataset access mode.

string

Current status.

string

Controller dispatch state.

string

Agent executing the job.

integer

Assigned GPU index.

string

MLflow run ID.

string

Direct MLflow URL.

string

Last recorded error.

object

Latest step metrics.

Show properties

integer

Training step.

number

Latest loss.

number

Latest reward (RL jobs).

string

ISO 8601 creation timestamp.

string

Human-readable message (empty on success).

Example response

{
  "code": 0,
  "data": {
    "job_id": "jb_abc123def456",
    "type": "grpo",
    "base_model": "meta-llama/Llama-3-8B",
    "lora": {
      "method": "lora",
      "rank": 16,
      "alpha": 32,
      "lora_dropout": 0.05,
      "target_modules": ["q_proj", "v_proj"],
      "quantization": "4bit_nf4"
    },
    "hyperparams": {
      "epochs": 3,
      "lr": 0.00002,
      "max_length": 2048,
      "group_size": 4,
      "kl_coef": 0.1
    },
    "dataset_id": "ds_xyz789",
    "status": "running",
    "dispatch_status": "running",
    "agent_id": "ag_node01_001",
    "gpu_index": 0,
    "mlflow_run_id": "run_20250115_001",
    "mlflow_url": "http://mlflow:5000/#/experiments/0/runs/run_20250115_001",
    "progress": {
      "step": 2150,
      "loss": 0.6123,
      "reward": 0.8741
    },
    "created_at": "2025-01-15T09:23:17Z"
  },
  "message": ""
}

Errors

Code	HTTP	Meaning
401	401	Missing or invalid Bearer token.
404	404	Job not found or does not belong to you.
500	500	Internal server error.

PIN API

ROUTER API

AGENT API

Retrieve full training job detail with checkpoints and metrics

Method + Path

Authentication

Path parameters

Request example

Response

Example response

Errors

​Method + Path

​Authentication

​Path parameters

​Request example

​Response

​Example response

​Errors

Method + Path

Authentication

Path parameters

Request example

Response

Example response

Errors