Description
Blobs to Clips: Efficient End-to-End Video Data Loading - Andrew Ho & Ahmad Sharif, Meta
The PyTorch team has improved training speed by an order of magnitude for teams at Meta working on Small-to-Large-Scale MultiModal Video models. In this talk we’ll share our learnings on reducing GPU starvation by overcoming data loading challenges such as dealing with large distributed datasets, worker imbalance, compute-bottlenecks due to parallel video decoding and sampling, checkpointing, and debuggability. As part of our commitment to open-source, we are releasing a new decoding library and updating existing PyTorch libraries on GitHub, and invite feedback and contributions from the community.