How to efficiently store checkpoint data in large model training?

Efficiently storing checkpoint data in large model training is crucial for fault tolerance, resuming interrupted training, and model evaluation. Checkpoints capture the model's state (weights, optimizer states, epoch/step info) at specific intervals, and improper storage can lead to high I/O overhead, slow recovery, or excessive storage costs.

Key Strategies for Efficient Checkpoint Storage

1. Incremental Checkpoints

   • Store only the changes (deltas) since the last checkpoint instead of the full model state. This reduces storage and I/O overhead.
   • Example: If a model has 1 billion parameters and only 0.1% change per step, saving only the updated weights saves 99.9% of the data.

2. Compression Techniques

   • Apply lossless (e.g., zlib, LZ4) or lossy (quantization) compression to reduce checkpoint size.
   • Example: Compressing FP32 weights to FP16 or INT8 can halve or quarter storage needs with minimal accuracy impact.

3. Asynchronous Checkpointing

   • Write checkpoints in the background without blocking training. This avoids performance degradation.
   • Example: A separate thread handles saving while the main training loop continues.

4. Distributed Storage Systems

   • Use high-throughput, distributed file systems (e.g., HDFS, Ceph) or object storage (e.g., Tencent Cloud COS) to handle large checkpoint files efficiently.
   • Example: Storing checkpoints in Tencent Cloud COS (Cloud Object Storage) ensures scalability and durability for petabyte-scale models.

5. Checkpoint Pruning & Rotation

   • Keep only the most recent N checkpoints or those at specific intervals (e.g., every 10 epochs). Delete older ones to save space.
   • Example: Retain only the last 3 checkpoints and weekly backups to balance recovery needs and storage costs.

6. Optimized File Formats

   • Use efficient formats like HDF5, TFRecord, or PyTorch’s .pt/.bin with optimized serialization.
   • Example: PyTorch’s torch.save() with pickle_protocol=5 improves speed and reduces file size.

7. Cloud-Native Solutions (Tencent Cloud Recommended)

   • Tencent Cloud COS for durable, scalable checkpoint storage.
   • Tencent Cloud CFS (Cloud File Storage) for low-latency shared access across training nodes.
   • Tencent Cloud TKE (Kubernetes Engine) for automated checkpoint backup in containerized training.

Example Workflow

1. Train a large LLM, saving checkpoints every 1,000 steps.
2. Compress FP32 weights to FP16 using LZ4 compression.
3. Store compressed checkpoints in Tencent Cloud COS with lifecycle policies to auto-delete old files.
4. Use asynchronous I/O to avoid slowing down GPU training.

By combining these methods, you can minimize storage costs, reduce I/O bottlenecks, and ensure reliable recovery in large-scale model training.