snorkel VS pytorch-lightning

The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel

Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo

Pytorch lightning benchmarks against pytorch on every PR (benchmarks to make sure that it is mot slower.

There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.

Is the problem the sheer number of options, or the fact that they are all together in one place? Would it be better if they were organized into the different trainer entrypoints (fit, validate, ...)? If that is the case, there was an RFC proposing this which you might find interesting, feel free to drop by and comment on the issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/10444

I wanted to make a quick performance comparison between the GPU (Tesla K80) and TPU (v2-8) available in Google Colab with PyTorch. To do so quickly, I used an MNIST example from pytorch-lightning that trains a simple CNN.

We've noticed GPU 0 on our 3 GPU system is sometimes idle (which would explain performance differences). However its unclear to us why that may be. Similar to this issue

If you want a good example for reference, take a look at Pytorch Lightning's readme (https://github.com/PyTorchLightning/pytorch-lightning) It answers the 3 questions of "what is this", "why should I care", and "how do i use it" almost instantly

Lower precision training can help and on pytorch lightning is just a simple flag you can set

First of all, use high-level ML frameworks (AllenNLP, PyTorch-Lightning). No need to write boilerplate code and implement standard ML approaches from scratch. Here are some suggestions (thought more NLP-focused) that I feel improved my research coding experience a lot.

According to the README it's patent pending, but I learned about that from this HN thread. Funny thing is I didn't even remember there was a snafu about patents, but looked it up because of some vague recollection of the PL founder getting into a tussle about some other trivial topic (apparently it was how well PyTorch works on TPUs).