Training

Subsections:

• Model parallelism

• Performance

• Fault Tolerance

• Reproducibility

• Instabilities

• Checkpoints

• Training hyper-parameters and model initializations

• Tensor precision / Data types

• Emulate a multi-node setup using just a single node - instructions on how to emulate a multi-node setup using just a single node - we use the deepspeed launcher here.

• Re-train HF hub models from scratch using finetuning examples

• Datasets

Tools:

• printflock.py - a tiny library that makes your print calls non-interleaved in a multi-gpu environment.

• multi-gpu-non-interleaved-print.py - a flock-based wrapper around print that prevents messages from getting interleaved when multiple processes print at the same time - which is the case with torch.distributed used with multiple-gpus.