---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

title: "Cancel Job"
description: ""
---

## Prerequisites

Before you can cancel a customization job, 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

```bash
export NMP_BASE_URL="https://your-nmp-base-url"
```

---

## To Cancel a Customization Job

Running jobs may be cancelled. A cancelled job does not upload checkpoints. Customization jobs run on the platform's Jobs service, so you cancel them through that service (the same way for both backends) using the job's name and workspace. You can get these from [List Active Jobs](/documentation/customizer-reference/manage-customization-jobs/list-active-jobs).

Use the SDK to cancel a job:

```python
import os
from nemo_platform import NeMoPlatform

# Initialize the client
client = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)

# Cancel a job (use the job name and workspace from List Active Jobs)
job_name = "automodel-a1b2c3d4e5f6"
workspace = "default"
cancelled_job = client.jobs.cancel(name=job_name, workspace=workspace)

print(f"Job {cancelled_job.name} has been cancelled")
print(f"Current status: {cancelled_job.status}")
print(f"Updated at: {cancelled_job.updated_at}")
```

<Accordion title="Example Response">

:open:

```json
{
  "name": "automodel-a1b2c3d4e5f6",
  "workspace": "default",
  "id": "platform-job-2k8i3i1HqJHHPVB5M6Bk9Z",
  "source": "automodel",
  "status": "cancelled",
  "spec": {
    "model": "default/qwen3-1.7b",
    "dataset": { "training": "default/my-training-dataset" },
    "training": {
      "training_type": "sft",
      "finetuning_type": "all_weights",
      "max_seq_length": 4096
    },
    "schedule": { "epochs": 3 },
    "batch": { "global_batch_size": 16, "micro_batch_size": 1 },
    "optimizer": { "learning_rate": 1e-05 },
    "parallelism": {
      "num_gpus_per_node": 2,
      "tensor_parallel_size": 2
    },
    "output": {
      "name": "my-finetuned-qwen",
      "type": "model",
      "fileset": "my-finetuned-qwen-a1b2c3d4e5f6"
    }
  },
  "created_at": "2026-02-09T10:30:00.000Z",
  "updated_at": "2026-02-09T10:35:00.000Z"
}
```

</Accordion>
