Hyper Parameter Optimization
TorchX integrates with Ax to let you scale hyper-parameter optimizations (HPO) by launching the search trials onto remote clusters.
File and Device Mounts
TorchX now supports remote filesystem mounts and custom devices. This enables your PyTorch jobs to efficiently access cloud storage such as NFS or Lustre. The device mounts enables usage of network accelerators like Infiniband and custom inference/training accelerators.
FBGemm v0.2.0
The FBGEMM library contains optimized kernels meant to improve the performance of PyTorch workloads. We’ve added a number of new features and optimizations over the last few months that we are excited to report.
Inference Table Batched Embedding (TBE)