Managed Checkpoints

AI developers often face challenges like manual data handling, unreliable storage, and data transfer bottlenecks in their training workflows.

FCS-Managed Checkpoints simplify AI training by providing secure, efficient, and reusable snapshots of model states. These checkpoints enhance reliability and usability, allowing developers to focus on training and experimentation without worrying about data loss, manual backups, or storage management.

Here’s how FCS-managed checkpoints make a difference:

1. Fast and Reliable Storage—No Maintenance Required

Forget storage limits or data transfer restrictions. Checkpoints are stored on FCS's high-speed, scalable infrastructure, ensuring constant accessibility without manual setup or maintenance.

2. Reusable Across Workflows

Since checkpoints are stored independently of compute clusters, they persist across jobs and infrastructure changes. Whether you switch clusters, rent new GPUs, or run different tasks, you can reuse checkpoints without re-uploading or downloading them. This eliminates the risk of data loss tied to temporary storage.

3. Effortless Export for External Systems

Easily integrate checkpoints into external pipelines or systems. Export them to your local machine or remote cloud storage (e.g., AWS) for seamless interoperability.

4. Unique Identification Without Naming Hassles

Say goodbye to manually managing checkpoint names. FCS automatically generates unique identifiers for checkpoints, even if your workload creates hundreds of checkpoints.

5. Faster Fine-tuning From Pre-Trained Model

Bring a pre-trained model checkpoint from your local machine or remote cloud storage (e.g., GCS) to FCS once, and reuse it for fine-tuning workloads. Avoid repeated transfer times and egress fees.

6. Model Comparison Over Time

Storing multiple checkpoints regularly allows you to compare model accuracy at different training phases. This lets you compare metrics (e.g., accuracy) across different checkpoints, This helps you optimize the training process by pinpointing where improvements or adjustments can make the biggest impact.

FCS-managed checkpoints to ensure developers can focus on building and improving models, reducing errors, saving time, and enabling a faster path to production.

See the command reference to use the Managed Checkpoints feature.