Customization Job Reference

Use this page when you need field-level details for customization job specifications, the complete API schema, or integration options.

For concepts, see Customization Job overview.

Key Fields

A customization job is submitted with a backend-specific spec (AutomodelJobInput for the automodel backend, UnslothJobInput for the unsloth backend). Both specs share the same top-level envelope, but several field names differ between backends. The table below lists the common envelope; for the full per-backend field list, see Training Configuration.

Complete API Reference

For generated REST API details, see the Customizer API Reference and search for AutomodelJobInput or UnslothJobInput.

Weights & Biases Integration

Both backends accept the same integrations object on the job spec. Add a wandb block to request W&B tracking; the training runtime activates it when the required credentials are available.

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import os
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from nemo_platform import NeMoPlatform
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from nemo_automodel_plugin.schema import AutomodelJobInput
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client = NeMoPlatform(base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"), workspace="default")
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spec = AutomodelJobInput(
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    model="default/qwen3-1.7b",
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    dataset={"training": "default/my-dataset"},
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    training={"training_type": "sft", "finetuning_type": "lora"},
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    schedule={"epochs": 3},
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    integrations={
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        "wandb": {
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            "project": "my-finetuning-project",
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            "entity": "my-team",
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                "tags": ["fine-tuning", "qwen"],
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            "api_key_secret": "my-wandb-key",
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        }
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    },
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)
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job = client.customization.automodel.jobs.create(spec=spec, workspace="default", name="my-job")

The api_key_secret field references a stored secret containing your WANDB_API_KEY. Use the secret name (e.g., "my-wandb-key") to resolve it from the request workspace. To create the secret, see Weights & Biases Keys.

To view your training metrics in W&B after the job starts, see ft-tut-metrics-wandb.

MLflow Integration

Add an mlflow block to the same integrations object to request MLflow tracking:

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spec = AutomodelJobInput(
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    model="default/qwen3-1.7b",
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    dataset={"training": "default/my-dataset"},
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    training={"training_type": "sft", "finetuning_type": "lora"},
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    schedule={"epochs": 3},
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    integrations={
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        "mlflow": {
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            "experiment_name": "qwen-finetuning",
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            "tracking_uri": "http://mlflow.example.com:5000",
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        }
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    },
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)
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job = client.customization.automodel.jobs.create(spec=spec, workspace="default", name="my-job")

Next Steps

• Create a customization job: Start a job with a model, dataset, training configuration, and optional integrations.
• Monitor training metrics: View logs and metrics through MLflow or W&B.
• Manage secrets: Store credentials such as W&B API keys and provider tokens.
• Troubleshooting MLflow integrations: Diagnose failed or misconfigured customization jobs.