PyTorch Release 1.11
As usual, the odd version is a bit more future oriented / experimental:
- github.com/pytorch…/v1.11.0
- https://pytorch.org/blog/pytorch-1.11-released/
Summary:
- TorchData is a new library for common modular data loading primitives for easily constructing flexible and performant data pipelines.
The rationale seems a bit weird:
We have found that the existing DataLoader bundled too many features together and can be difficult to extend.
Looks like they just want to collect all of their primitives for their docs and examples in one place. But I am not sure what this is solving for the end user, since in real life you end up rewriting such things anyway.
- functorch, a library that adds composable function transforms to PyTorch, is now available in beta.
Composable function transforms can help with a number of use cases that are tricky to do in PyTorch today:
computing per-sample-gradients (or other per-sample quantities)
running ensembles of models on a single machine
efficiently batching together tasks in the inner-loop of MAML
efficiently computing Jacobians and Hessians as well as batched ones
Also not sure
- Distributed Data Parallel (DDP) static graph optimizations available in stable.
Looks like it is required for huge models?
- Some interesting quantization fixes and additions
#deep_learing
