tolstoyevsky

I haven't experienced any real issues with GPU availability on Colab, I suggest that you just go ahead and use it and wait with the premature optimization until you actually hit a wall and need it.

For general advice focused on beginners and ESPECIALLY practical, cheap and efficient methods and hacks to do DL, I recommend searching in https://www.fast.ai/ and their forums https://forums.fast.ai/

I'll try to search inside fast.ai if there is a more specific link to give. I know that one of their chief pieces of advice has been to use Colab and take advantage of the 300$ free credit you get (per credit card) when signing up to Google Cloud, which you can use for DL.

Disclaimer - I'm one of the creators of DagsHub, we created the platform especially to help people like you with the difficulties of managing things like data and model versioning, experiment tracking, labeling, etc. we'd love to have you onboard, and thanks for reading until the end :)

I don't have official confirmation, but my experience is that at least the IntelliJ plugin version 100% seems to be using or remembering context from other parts of the project. It will know how to complete pieces of code in a brand new file, in ways that are very peculiar to my specific project.

I understood exactly the opposite point from the article - it's saying the exact thing you're saying, people will accept a CONSTANT AMOUNT of crap to get to their goals, and technology doesn't change this. It only changes how far people can get with that constant amount of crap they're willing to put up with.

Looks very interesting! Could be nice to get links in the body of the article for further reading and definitions of technical terms.

Recommended listening to get historical proportions: https://allthingscomedy.com/podcasts/431---year-of-the-locus...

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I think maybe this could better illustrate what can be done using DVC: https://dagshub.com/DAGsHub-Official/DAGsHub-Tutorial-MNIST

You can navigate branches, and be able to download the data, model, and intermediate pipeline files from a shared team AWS,GS,Hadoop, or plain NFS or SSH server, as they were in a specific commit. Also compare metrics between branches for comparison of different experiments, etc. A team member can checkout a branch, immediately get the relevant files which were already computed by someone else, modify the training code, reproduce the out-of-date parts of the pipeline using dvc repro, and then git commit the resulting metrics + dvc push the resulting model back to the shared team storage.