Skip to content
FlexAI Logo FlexAI Logo FlexAI Docs

Creating a Fine-tuning Job

With a Dataset available on your FlexAI account, you can now create a Fine-tuning Job that will use it.

The Model's repository

For this tutorial we will use the FlexAI fork of the nanoGPT repository 🔗, originally created by Andrej Karpathy 🔗.

A Fine-tuning Job requires at least a Name, a link to a GitHub repository where its code resides, and the path to the entry point script that will initiate the Workload.

In addition, the entry point script can be followed by any arguments required, such as configuration files or Hyperparameters.

Entry Point script arguments

The entry point script path for this quickstart tutorial is ./train.py 🔗, and it expects the following arguments:

  • config/train_shakespeare_char.py:A configuration file, which contains the default Workload Parameters.
  • --dataset_dir: The path within the /input directory of the Workload Runtime where the Dataset files are located.
  • --out_dir: The output directory, which will be mounted into the Workload Runtime as /output-checkpoint.
  • --max_iters: The maximum number of iterations to run the Workload script for (optional).
Entry Point script arguments details

These include any Environment Settings and Hyperparameters the entry point script may require. For this tutorial:

ParameterTypeDescription
config/train_shakespeare_char.pyEnvironment SettingA positional argument pointing to a configuration file used by nanoGPT’s train.py script to set the default Workload Parameters
--out_dir=/output-checkpointEnvironment SettingThe output directory where the Workload script will write checkpoint files. In order to take advantage of FlexAI’s Managed Checkpoints feature, this should always be /output-checkpoint
--max_iters=1500HyperparameterThe maximum number of iterations to run the Workload script for. This is an optional hyperparameter that can be used to tweak the Workload execution

Starting a new Fine-tuning Job

The Start a new Fine-tuning job form consists of a set of required and optional fields that you can use to customize your deployment.

To open the Start a new Fine-tuning job form

Either:

Or

  1. Navigate to the Fine-tuning section from either the navigation bar or the card on the home page.

  2. Select the New button to display the creation form.

A drawer menu with the creation form will be displayed.

Required Fields

  • Name: Your Fine-tuning Job name. Should follow the resource naming conventions.
  • Repository URL: The URL of the Git repository containing your Fine-tuning code.`.
  • Entry Point: The path to the entry point script in your repository that will initiate the Fine-tuning Job.
    • The entry point script can be followed by any arguments you want to pass to it, such as configurations and Hyperparameters. Value: train.py config/train_shakespeare_char.py --dataset_dir=my_dataset --out_dir=/output-checkpoint --max_iters=1500.

Other fields

  • Repository Revision: The Git revision (branch, tag, or commit) you want to use for this Fine-tuning Job. The main branch will be used by default.

  • Node Count: The number of nodes you want to use for this Fine-tuning Job. Defaults to 1.

    • This will determine the amount of Accelerators you will have available for your Fine-tuning Job:
      • 1 node will allow you to use up to 8 Accelerators.
      • Using more than 1 node will make all 8 Accelerators per Node available to your Fine-tuning Job.

  • Accelerator Count: The number of Accelerators you want to use for this Fine-tuning Job. Must follow the logic described above. Defaults to 1.

  • Datasets: Can be selected from a dropdown list of the datasets you want to use for this Fine-tuning Job. You can add multiple datasets as well as specify the mount path within the Fine-tuning Runtime (they will be mounted under /input). You can read more about this in the Pushing a Dataset guide.

  • Environment Variables & Secrets: Add any environment variables you want to set for this Fine-tuning Job. These will be available to your Fine-tuning code as environment variables within the Training Runtime.

    • You can also reference Secrets, which will be securely injected into the Fine-tuning Job’s Runtime.

  • Cluster: The cluster where the Fine-tuning workload will run on. It can be selected from a dropdown list of available clusters in your FlexAI account. A default cluster will be automatically selected for you if none is specified.

Form Values

Field NameValue
NamenanoGPT-flexai-console
Repository URLhttps://github.com/flexaihq/nanogpt
Repository Revisionmain
Node Count1
Accelerator Count1
Entry Pointtrain.py config/train_shakespeare_char.py --dataset_dir=my_dataset --out_dir=/output-checkpoint --max_iters=1500
DatasetsDataset: nanoGPT-dataset (from the CLI quickstart),
Mount Directory: my_dataset
ClusterYour organization’s designated cluster

Entry Point script arguments

The entry point script for this Fine-tuning Job is train.py, and it expects the following arguments:

  • config/train_shakespeare_char.py:A configuration file, which contains the default Fine-tuning Parameters.
  • --dataset_dir: The path within the /input directory of the Fine-tuning Runtime where the Dataset files are located.
  • --out_dir: The output directory, which will be mounted into the Fine-tuning Runtime as /output-checkpoint.
  • --max_iters: The maximum number of iterations to run the Fine-tuning script for (optional).
Entry Point script arguments details

These include any Environment Settings and Hyperparameters the Fine-tuning script may require. For this tutorial:

ParameterTypeDescription
config/train_shakespeare_char.pyEnvironment SettingA positional argument pointing to a configuration file used by nanoGPT’s train.py script to set the default Fine-tuning Parameters
--out_dir=/output-checkpointEnvironment SettingThe output directory where the Fine-tuning script will write checkpoint files. In order to take advantage of FlexAI’s Managed Checkpoints feature, this should always be /output-checkpoint
--max_iters=1500HyperparameterThe maximum number of iterations to run the Fine-tuning script for. This is an optional hyperparameter that can be used to tweak the Fine-tuning Job execution

After filling out the form, select the Submit button to start the Fine-tuning Job. You should get a confirmation message indicating that the Fine-tuning Job creation process has been initiated successfully.

The Start a new training job form will close and you will be redirected to the Fine-tuning Jobs list page, where you can see your newly created Fine-tuning Job in the list.

Up next

Next you'll learn how to get a Fine-tuning Job's details and monitor its progress.