Getting a Training Job's Output

Training Job’s Checkpoints

You can retrieve checkpoints generated by FlexAI’s Managed Checkpoints at any point, which allows you to go back to a previous point in the past to resume training, to test your model, or to use it for inference.

Listing Checkpoints

You can list all available checkpoints for a specific Training Job by running the flexai training checkpoints command:

flexai training checkpoints quickstart-training-job

This will return a table with a list of Checkpoint IDs and their corresponding creation timestamps, similar to the following:

 ID                                   │ TIMESTAMP
──────────────────────────────────────┼────────────────────────────────────
 50e5ec69-32b6-e483-9c49-38a73cc34294 │ 2025-06-30 12:42:55.214 +0100 WEST
 82d21263-8ba8-dd73-9c61-732d3b7b0adc │ 2025-06-30 12:43:01.77 +0100 WEST
 32d07a60-61cc-4598-b4f6-2073a4f8d0af │ 2025-06-30 12:43:14.734 +0100 WEST

Fetching a Checkpoint

Once you have the desired Checkpoint ID, you can download it to your host machine using the flexai checkpoint fetch command:

flexai checkpoint fetch 32d07a60-61cc-4598-b4f6-2073a4f8d0af

Writing in:  /home/diego/ckpt.pt
Progress: 0.4% (1.31 MB / 343.79 MB)
// ...
Progress: 100% (343.79 MB / 343.79 MB)

You can use this checkpoint file to resume training from the exact point it was saved, or to evaluate the model’s performance on a validation dataset.

Exporting a Checkpoint

Use the flexai checkpoint export command to export a Checkpoint to a remote location by using a previously registered Remote Storage Connection, such as S3, GCS, MinIO or R2.

This allows you to store your checkpoints in a more permanent location for later use.

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A Training Job’s output

Any data written to the /output directory will be compressed into a zip file and made available to you via the flexai training fetch command:

flexai training fetch quickstart-training-job

This will download a .zip file to the current working directory on your host machine.

Once extracted you’ll get a local directory named output it will contain any files written to the /output directory by the training scripts.

Note

For this quickstart example we configured the training code to write to the checkpoints directory /output-checkpoint directory when we set the value of --out_dir, so the /output directory won’t be used.

However, you can run another Training Job that instead writes out to the /output directory. In this case, only the last checkpoint will be saved in the /output directory as a result of the training process.

Note that your code can use both locations (/output and /output-checkpoint) simultaneously if needed.

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Success!

You're ready to get started!

You’ve learned how to upload a Dataset and then use it to run a Training Job using training code hosted on a public GitHub repository.

Now you can start exploring the FlexAI platform and its capabilities.

Celebrate!

You now have the knowledge required to create run your own Training Jobs on FlexAI by integrating your own public or private Code Repositories and loading your datasets.

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Next Steps

Private Code Repositories

You can use any public or private GitHub repository as the source of your training code when using the --repository-url flag.

However, you can also use the flexai code-registry command to connect your GitHub account to FlexAI and use any of your private repositories as well.

The Code Registry Command reference page includes all the information you need.

Add yours now!

Dataset Upload

FlexAI makes it easy to upload Datasets from your host machine through the flexai dataset push command.

But wait, there’s more! You can also push Datasets from remote sources, such as S3, GCS, MinIO or R2.

Visit the Uploading Datasets guide to learn more!

Learn how to do it

Interactive Training

With FlexAI you can run an “Interactive Training Job session” that allows you SSH into a Training Environment where you have access to the entire system by using the flexai training debug-ssh command.

This is useful for debugging and testing purposes, allowing you to test your training code in the environment it’ll be running on, reducing iteration times.

The Interactive Training Session guide explains how to leverage this useful feature.

Find out more

CLI Command Reference

Explore the CLI Command Reference pages to learn about all the ways you can use the FlexAI CLI to manage your workloads.

You will find a page for each CLI Command along with each of its subcommands, example usage, recommendations, flags you can use, output messages, and more!

Start Exploring