Tips and tricks for performing large model checkpointing

Model checkpointing is crucial in machine learning for saving the state of a model during training to enable recovery from interruptions and to preserve the trained model. Checkpoints typically include metadata and the training state, encompassing model parameters and optimizer states, saved in formats like JSON, YAML, or binary. Different frameworks offer various methods for checkpointing, with asynchronous checkpointing allowing continued training while checkpointing occurs in the background. The size of checkpoints is influenced by the model's parameters, with large language models like LLaMa and GPT-3 requiring substantial storage due to their parameter count. Cloud storage solutions, such as network disks and S3-compatible object storage, offer different benefits and limitations for handling checkpoints, emphasizing the importance of understanding storage types and ensuring parallel IO operations for efficiency. A strategic checkpointing schedule is vital, balancing the trade-off between frequent checkpoints for minimal data loss upon failure and the increased training time due to the overhead of saving checkpoints. Storing multiple checkpoints can also safeguard against issues like gradient explosions by providing fallback options, though this must be weighed against the storage costs.

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