FlexAI Managed Checkpoints
AI developers often face challenges like manual data handling, unreliable storage, and data transfer bottlenecks in their training workflows.
FlexAI Managed checkpoints simplify AI training by providing secure, efficient, and reusable snapshots of model states. They support single-file checkpoints and multi-file or folder checkpoints produced by PyTorch, Hugging Face Transformers, PEFT, TRL, and Accelerate frameworks. FlexAI’s managed checkpoint service enhances reliability and usability, allowing developers to focus on training and experimentation without worrying about data loss, manual backups, or storage management.
Here’s how FlexAI Managed checkpoints make a difference.
1. Fast and Reliable Storage—No Maintenance Required
Section titled “1. Fast and Reliable Storage—No Maintenance Required”Forget storage limits or data transfer restrictions. Checkpoints are stored on FlexAI’s high-speed, scalable infrastructure, ensuring constant accessibility without manual setup or maintenance.
2. Reusable Across Workflows
Section titled “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
Section titled “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
Section titled “4. Unique Identification Without Naming Hassles”Say goodbye to manually managing checkpoint names. FlexAI automatically generates unique identifiers for checkpoints, even if your workload creates hundreds of checkpoints.
5. Faster Fine-Tuning From Pre-Trained Model
Section titled “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, S3, MinIO) to FlexAI once, and reuse it for fine-tuning workloads. Avoid repeated transfer times and egress fees.
6. Model Comparison Over Time
Section titled “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.
7. Flexible Formats
Section titled “7. Flexible Formats”Whether you save a single model.pt file or an entire folder of weights, tokenizer files, and optimizer states, FlexAI captures everything as one managed checkpoint so you never lose track of related files.