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notable co-releases along with PyTorch 1.5:

- TorchServe: model serving infrastructure for scalable model deployment

- TorchElastic w/Kubernetes: fault-tolerant "elastic" neural network training, allowing nodes to join and leave (for eg. to leverage spot pricing)

- Torch_XLA: updates for PyTorch TPU support

- New releases of torchvision, torchaudio and torchtext

Summary blogpost at https://pytorch.org/blog/pytorch-library-updates-new-model-s...

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Damn they’re on fire at the moment (pun sort of intended).

Particularly excited about the launch of Serve. I didn’t know about TorchElastic, the name makes me think of ElasticSearch but apart from that I’m keen to get stuck into that as well.

Edit: C++ API now having complete parity with Python API is pretty cool, hopefully when that flows through into the Rust binding crate, that should make writing nn applications in Rust nicer.

Anyone with more info about the difference between TorchElastic and PytorchOperator?

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