Run Fine-Tuning

Fine-tuning is like giving your smart model a bit of extra training on a specific set of examples. Imagine it as a way to teach your model to become even better at a particular task. In Embedding Studio, we use fine-tuning to make our search experience sharper and more accurate.

Think of it this way: you've got this smart model that's learned a lot of things (we call it pre-trained), and now you want it to specialize in something unique to your needs. Fine-tuning helps it become a real pro at that specific job.

Here's an overview of what to expect during the fine-tuning process:

1. Initial Model Setup: The process begins by downloading and locally saving either the initial model or the model from the most recent iteration.
2. Hyperparameter Selection: Next, subsets of hyperparameters are defined for exploration. These may be initial sets or derived from the last iteration.
3. Iteration Creation: An iteration is created, referred to as an experiment in MLFlow terminology.
4. Hyperparameter Runs: For each subset of hyperparameters (termed fine-tuning params in our context), a run is initiated.
5. Executing Fine-Tuning: The fine-tuning run starts, using the specified hyperparameters.
6. Model Evaluation and Saving: After each run, the main metric is evaluated. If the result is the best so far, the model is saved, and the previously best model is deleted, if feasible.
7. Iteration Completion: Upon completing all runs, the previous iteration is removed from the system.
8. Model Cleanup: Finally, the locally saved model is deleted.

Once you've gathered enough data, you can initiate the fine-tuning process.

Create task

To run fine-tuning, send a POST request to the /api/v1/fine-tuning/task endpoint:

curl -X POST http://localhost:5000/api/v1/fine-tuning/task \
  -H 'Content-Type: application/json' \
  -d '{
    "fine_tuning_method": "Default Fine Tuning Method",
    "batch_id":  "65844a671089823652b83d43",
    "metadata": {},
    "idempotency_key": "833fb6b260ff41a88cb75c4280e2e270"
}'

where

• fine_tuning_method - name of your fine-tuning script, taken from the meta.name field.
• batch_id - (optional) id of released clickstream session batch. If parameter is not sent, current clickstream batch will be released and used automatically.
• metadata - (optional) any meta data
• idempotency_key - (optional) idempotency key

In response, you will receive task id:

{
  "fine_tuning_method": "Default Fine Tuning Method",
  "status": "processing",
  "created_at": "2023-12-21T14:30:25.823000",
  "updated_at": "2023-12-21T14:32:16.673000",
  "batch_id": "65844a671089823652b83d43",
  "id": "65844c019fa7cf0957d04758"
}

Task status

Using this task ID, you can directly monitor the fine-tuning progress by sending a GET request to the endpoint /api/v1/fine-tuning/task/{task_id}:

curl -X GET http://localhost:5000/api/v1/fine-tuning/task/65844c019fa7cf0957d04758

In response, you will receive:

{
  "fine_tuning_method": "Default Fine Tuning Method",
  "status": "processing",
  "created_at": "2023-12-21T14:30:25.823000",
  "updated_at": "2023-12-21T14:32:16.673000",
  "batch_id": "65844a671089823652b83d43",
  "id": "65844c019fa7cf0957d04758"
}

where:

• fine_tuning_method - method used for fine-tuning the model. We'll discuss this further later on.
• status - status of the task. Possible values: pending, processing, done, canceled, error
• created_at - task creation date.
• updated_at - last task update date.
• batch_id - batch identifier indicating a gathered clickstream sessions.
• id - task identifier.

A more convenient way to track status is to use MLflow (more details in Progress tracking with MLflow).

Once the process is finished, you can tell the best model. See How to get best model?