framework-reproducibility VS wandb

Even with a fixed seed there's no guarantee that you'll get the exact same results due to the fact that most floating operations are not deterministic when parallelized. You can enable determinism flags in your framework to try and mitigate that, but results may still vary depending on your model and how you're running it.

Setting the seed alone is not enough because there will be a randomness resulted from GPU operations (there is some way to eliminate randomness due to GPU operations like https://github.com/NVIDIA/framework-determinism, but I cannot make it work with the current latest version of TF). Another workaround is not using GPU, but the training time does not make sense as I need to iterate fast, trying new idea.

check out https://github.com/NVIDIA/framework-determinism, which should allow you to make fully reproducible to the bit code that runs on GPU. i've contributed to this repo and the author is extremely helpful.

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HuggingFace and wandb.ai (both competitors of OpenAI) both also have "do own research"

Wandb is the best! https://wandb.ai/

To track stuff I've used wandb.ai in a company in the past, as someone else pointed out. Regarding metrics... This is really specific to your domain, and it is such a broad question. You could count color pixels, the distribution of intensity histograms, etc etc.

Step 1: The colab notebook uses wandb.ai, so you need to register for a wandb.ai account beforehand if you want to use the colab notebook. After registering you need to go to your homepage and copy the API key and paste/keep it somewhere.

I want to take a look at tools like https://wandb.ai/ and they would integrate into some of the pipelines I'm playing with.

does current cgi and ai tech can bring back leslie nielsen? might use unreal engine and https://www.resemble.ai/ or https://wandb.ai/?

I'm currently working for a MLOps company so I'm heavily using their tools (Weights & Biases) but I've used custom C++ for deployment, Pytorch + fastai for quick experimentation, Weights & Biases for experiment tracking, hyper-parameter tuning + model versioning (hence why I went to work for them), custom database + data pipeline, HoloViz for data visualisation (really nice dashboarding tool), Jenkins for CI/CD, I also love Github Actions.