Create Job

Prerequisites

Before you can create a customization job, make sure that you have:

• Obtained the base URL of your NeMo Customizer service.

• Obtained a list of customization configurations to find the configuration you want to use.

• Determined the hyperparameters you want to use for the customization job.

---

Create a Customization Job

API

1. Perform a POST request to the /v1/customizer/jobs endpoint.

   cURL

   curl -X POST \
     "https://${CUSTOMIZER_HOSTNAME}/v1/customization/jobs" \
     -H 'accept: application/json' \
     -H 'Content-Type: application/json' \
     -H 'wandb-api-key: <YOUR_WANDB_API_KEY>' \
     -d '{
       "name": "<NAME>",
       "description": "<DESCRIPTION>",
       "project": "<PROJECT_NAME>",
       "config": "<CONFIG_NAME>",
       "hyperparameters": {
         "finetuning_type": "lora",
         "training_type": "sft",
         "batch_size": 8,
         "epochs": 50,
         "learning_rate": 0.0001,
         "log_every_n_steps": 0,
         "val_check_interval": 0.01,
         "weight_decay": 0,
         "sft": {
           "hidden_dropout": 1,
           "attention_dropout": 1,
           "ffn_dropout": 1
         },
         "lora": {
           "adapter_dim": 8,
           "adapter_dropout": 1
         }
       },
       "output_model": "<OUTPUT_MODEL_NAME>",
       "dataset": "<DATASET_NAME>",
       "ownership": {
         "created_by": "",
         "access_policies": {}
       }
     }' | jq
   

2. Review the returned customization job.

   Example Response

   {
     "id": "cust-JGTaMbJMdqjJU8WbQdN9Q2",
     "created_at": "2024-12-09T04:06:28.542884",
     "updated_at": "2024-12-09T04:06:28.542884",
     "config": {
       "schema_version": "1.0",
       "id": "af783f5b-d985-4e5b-bbb7-f9eec39cc0b1",
       "created_at": "2024-12-09T04:06:28.542657",
       "updated_at": "2024-12-09T04:06:28.569837",
       "custom_fields": {},
       "name": "meta/llama-3_1-8b-instruct",
       "base_model": "meta/llama-3_1-8b-instruct",
       "model_path": "llama-3_1-8b-instruct",
       "training_types": [],
       "finetuning_types": [
         "lora"
       ],
       "precision": "bf16",
       "num_gpus": 4,
       "num_nodes": 1,
       "micro_batch_size": 1,
       "tensor_parallel_size": 1,
       "max_seq_length": 4096
     },
     "dataset": {
       "schema_version": "1.0",
       "id": "dataset-XU4pvGzr5tvawnbVxeJMTb",
       "created_at": "2024-12-09T04:06:28.542657",
       "updated_at": "2024-12-09T04:06:28.542660",
       "custom_fields": {},
       "name": "default/sample-basic-test",
       "version_id": "main",
       "version_tags": []
     },
     "hyperparameters": {
       "finetuning_type": "lora",
       "training_type": "sft",
       "batch_size": 16,
       "epochs": 10,
       "learning_rate": 0.0001,
       "lora": {
         "adapter_dim": 16
       }
     },
     "output_model": "test-example-model@v1",
     "status": "created",
     "project": "test-project",
     "custom_fields": {},
     "ownership": {
       "created_by": "me",
       "access_policies": {
         "arbitrary": "json"
       }
     }
   }