DocumentationFine-tune ModelsManage Customization Jobs

List Active Jobs

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List active customization jobs and their high-level status. Customization jobs run on the platform’s Jobs service, so you list them through that service and filter by source and status. Platform job records use source="customization" for Automodel, Unsloth, and RL (the Jobs service does not store the training backend as source). status="active" excludes completed, failed, and cancelled jobs. Tell backends apart from the job name prefix (automodel-…, unsloth-…, rl-…) or from the backend you submitted to. Each entry includes the job definition (model, dataset, training configuration) and overall status.

To get detailed execution progress (step-by-step status, training metrics like loss/epoch/step), use Get Job Status instead.

Prerequisites

Before you can list active customization jobs, make sure that you have:

  • Obtained the base URL of your NeMo Platform.
  • Set the NMP_BASE_URL environment variable to your NeMo Platform endpoint
$export NMP_BASE_URL="https://your-nmp-base-url"

To List Active Customization Jobs

Use the SDK to list jobs, filtering by source="customization" and by status to return only active customization jobs:

1import os
2from nemo_platform import NeMoPlatform
3
4# Initialize the client
5client = NeMoPlatform(
6    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
7    workspace="default",
8)
9
10# List active customization jobs
11jobs = client.jobs.list(
12    workspace="default",
13    filter={
14        "source": "customization",
15        "status": "active",
16    },
17    page=1,
18    page_size=10,
19    sort="created_at",
20)
21
22print(f"Found {len(jobs.data)} jobs")
23for job in jobs.data:
24    print(f"Job {job.name}: {job.status}")
25
26# Add more filters (optional)
27# Valid filter fields: workspace, project, name, status, source, created_at, updated_at
28filtered_jobs = client.jobs.list(
29    workspace="default",
30    filter={
31        "source": "customization",
32        "status": "active",
33        "project": "my-finetuning-project",
34    },
35    sort="-created_at",  # Sort by created_at descending
36)
37
38print(f"Found {len(filtered_jobs.data)} jobs")
39for job in filtered_jobs.data:
40    print(f"Job {job.name}: {job.status}")

:open:

1{
2  "data": [
3    {
4      "id": "platform-job-QtyhRY5ub4t4tTLPY4sTkz",
5      "name": "automodel-99da3f7c1b2e",
6      "workspace": "default",
7      "source": "customization",
8      "created_at": "2026-02-09T22:12:45",
9      "updated_at": "2026-02-09T22:12:45",
10      "status": "active",
11      "status_details": {
12        "message": "Job is running"
13      },
14      "spec": {
15        "model": "default/qwen3-1.7b",
16        "dataset": { "training": "default/sft-dataset" },
17        "training": {
18          "training_type": "sft",
19          "finetuning_type": "all_weights",
20          "max_seq_length": 2048
21        },
22        "schedule": { "epochs": 2 },
23        "batch": { "global_batch_size": 64, "micro_batch_size": 1 },
24        "optimizer": { "learning_rate": 5e-05, "weight_decay": 0.01 },
25        "parallelism": {
26          "num_gpus_per_node": 1,
27          "num_nodes": 1,
28          "tensor_parallel_size": 1,
29          "pipeline_parallel_size": 1,
30          "context_parallel_size": 1
31        },
32        "output": {
33          "name": "customization-407790d32cfb",
34          "type": "model",
35          "fileset": "customization-407790d32cfb"
36        }
37      }
38    }
39  ],
40  "pagination": {
41    "page": 1,
42    "page_size": 10,
43    "current_page_size": 1,
44    "total_pages": 1,
45    "total_results": 1
46  },
47  "sort": "created_at",
48  "filter": {
49    "source": "customization",
50    "status": "active"
51  },
52  "search": {}
53}