Checking Your Customization Job Metrics

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import os
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from nemo_platform import NeMoPlatform
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client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
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)
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# Get job status with metrics
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job_name = "my-sft-job"
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status = client.customization.jobs.get_status(name=job_name, workspace="default")
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print(f"Job: {status.name}")
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print(f"Status: {status.status}")
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# Check training step progress
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for step in status.steps or []:
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    if step.name == "customization-training-job":
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        for task in step.tasks or []:
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            details = task.status_details or {}
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            print(f"Training Phase: {details.get('phase')}")
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            print(f"Step: {details.get('step')}/{details.get('max_steps')}")
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            print(f"Epoch: {details.get('epoch')}/{details.get('num_epochs')}")
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            print(f"Training Loss: {details.get('loss')}")
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            print(f"Validation Loss: {details.get('val_loss')}")
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            print(f"Learning Rate: {details.get('lr')}")
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            print(f"Gradient Norm: {details.get('grad_norm')}")

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client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
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)
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# Create a customization job with W&B integration
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job = client.customization.jobs.create(
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    name="my-wandb-job",
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    workspace="default",
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    spec={
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        "model": "default/llama-3-2-1b",
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        "dataset": "fileset://default/my-dataset",
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        "training": {
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            "type": "sft",
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            "peft": {"type": "lora"},
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            "epochs": 3,
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            "batch_size": 16,
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            "learning_rate": 1e-4,
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        },
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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", "llama"],
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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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)
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print(f"Created job: {job.name}")
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print(f"Status: {job.status}")