A distributed storage system supports large model training by providing scalable, high-performance, and fault-tolerant data access, which is critical for handling the massive datasets and model checkpoints involved in training large-scale AI models. Here’s how it works and why it’s essential:
Large model training requires vast amounts of training data (e.g., terabytes or petabytes of images, text, or audio). A distributed storage system can scale out by adding more storage nodes, ensuring that data throughput keeps up with the training pipeline. Instead of relying on a single storage server, data is partitioned and stored across multiple nodes, allowing parallel access.
Example: When training a large language model (LLM) on billions of text tokens, the dataset is split into chunks and distributed across multiple storage servers. Training workers can fetch different data shards simultaneously, reducing I/O bottlenecks.
Model training involves frequent read/write operations for loading batches of data and saving checkpoints. Distributed storage systems optimize data access patterns by using parallel I/O techniques, such as striping data across multiple disks or nodes. This ensures that GPUs/TPUs are not idle waiting for data.
Example: A distributed file system like HDFS or a cloud-native object storage solution can deliver high bandwidth by serving multiple data requests in parallel, ensuring that the training cluster maintains high utilization.
Training large models takes days or weeks, and failures (e.g., node crashes, network issues) can occur. Distributed storage systems replicate data across multiple nodes or availability zones, ensuring that no data is lost and training can resume without interruption.
Example: If a storage node fails during training, the system automatically retrieves the required data from a replicated copy, minimizing downtime. Checkpoints of the model (which can be gigabytes in size) are also safely stored and recoverable.
During training, intermediate model states (checkpoints) are periodically saved to resume training in case of failures. Distributed storage systems provide fast write/read capabilities for these large files, ensuring that checkpoints are stored reliably and can be quickly retrieved.
Example: A distributed storage system can store multi-terabyte model checkpoints and allow the training job to restart from the latest checkpoint without reprocessing all previous data.
For large model training, Tencent Cloud Object Storage (COS) is ideal for storing massive datasets and model checkpoints due to its high durability and scalability. For low-latency access during training, Tencent Cloud File Storage (CFS) provides a shared file system that multiple training instances can access simultaneously, ensuring efficient data sharing.
By leveraging a distributed storage system, large model training becomes more efficient, reliable, and capable of handling the demands of modern AI workloads.