hydra-torch
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pytorch
Configuration classes enabling type-safe PyTorch configuration for Hydra apps
I have done something similar in a recent project using Hydra/PyTorch and I'm evaluating if it makes sense for me to switch to this (I'm trying to simplify the code...
Since this repository contains N packages each corresponding to a collection of configs for their corresponding libraries, we should write up a version controlled overview of the design decisions and...
https://github.com/facebookresearch/hydra-torch/blob/691a390abd2edf764f9431a56b8058ff2c12eb0c/tests/test_instantiate.py#L40 Check minimal tests. Is this the right way to confirm our configs instantiate the correct object? From previous PR discussion: > Ideally I wanted these tests to do 3...
Tests for examples from tutorial: [examples/mnist_00.py](https://github.com/pytorch/hydra-torch/blob/master/examples/mnist_00.py) and [examples/mnist_01.py](https://github.com/pytorch/hydra-torch/blob/master/examples/mnist_01.py). Something along these lines: https://github.com/facebookresearch/hydra/tree/master/tests/test_examples
Generate these module confs, brainstorm robust testing method.
Distributed processing with Hydra in single-node multi-GPU setting, as mentioned [here](https://github.com/pytorch/hydra-torch/pull/38#issuecomment-738991461). - [ ] Explain PyTorch's distributed processing/training. - [ ] Simple demonstration of various distributed communication primitives. - [...
An example of DDP for ImageNet using multirun, as discussed with @omry in the Hydra repo.
Pickup where we left off in Basic Tutorial To address: * Configuring the model * Configuring the dataset * Swapping in and out different Optimizers/Schedulers Another thing to think about...