hummingbird VS password-manager-resources

If are interested in just predictions you can try Hummingbird. It is part of the PyTorch ecosystem. We get already trained scikit-learn models and translate them into PyTorch models. From them you can run your model on any hardware support by PyTorch, export it into TVM, ONNX, etc. Performance on hardware acceleration is quite good (orders of magnitude better than scikit-learn is some cases)

I think Rapids AI's cuML tried to go into this direction (essentially scikit-learn on the GPU): https://docs.rapids.ai/api/cuml/stable/api.html#logistic-reg.... For some reason it never took really off though.

Btw., going on a tangent, you might like Hummingbird (https://github.com/microsoft/hummingbird). It allows you trained scikit-learn tree-based models to PyTorch. I watched the SciPy talk last year, and it's a super smart & elegant idea.

ONNX opens an avenue for direct inference using a number of languages and platforms. For example, a model could be run directly on Android to limit data sent to a third party service. ONNX is an exciting development with a lot of promise. Microsoft has also released Hummingbird which enables exporting traditional models (sklearn, decision trees, logistical regression..) to ONNX.

And Python.

For inference, you can convert your models to other formats that support GPU acceleration. See Hummingbird https://github.com/microsoft/hummingbird

The surprising thing is that Hummingbird can be faster than the GPU implementation of LightGBM (and XGBoost) if you use tensor compilers such as TVM. [The paper](https://www.usenix.org/conference/osdi20/presentation/nakandala) describes our findings. We have also open sourced the [benchmark code](https://github.com/microsoft/hummingbird/tree/main/benchmarks) so you try yourself!

I took their sample code from Github and tweaked it to spit out times for each model's prediction, as well as increase the number of rows to 5 million. I used Google's Colab and selected GPU for my hardware accelerator. This gives an option to run code on GPU, not that all computations will happen on the GPU.

Even Apple was so annoyed at this themselves that they actually went for a full open-source open-for-contributions GitHub repository at https://github.com/apple/password-manager-resources to get around these issues.

> Many password managers generate strong, unique passwords for people so that they aren't tempted to create their passwords by hand, which leads to easily guessed and reused passwords. Every time a password manager generates a password that isn't compatible with a website, a person not only has a bad experience but a reason to be tempted to create their password. Compiling password rule quirks helps fewer people run into issues like these while also documenting that a service's password policy is too restrictive for people using password managers, which may incentivize the services to change.

Check out https://github.com/apple/password-manager-resources

Apple has already created the database for this and made it open source: https://github.com/apple/password-manager-resources

Something like this?

Password rules are really all over the place. Based on the sampling available on Apple's password rules database, seems that the majority of sites would accept a 12-character password (although ironically, most websites that restrict the password to be shorter than 12 characters seem to be banks...).

There are still some things in Keychain that feel stupid. For example, Keychain won't merge https://www.google.co.uk and https://www.google.com accounts into one and you can't do it by yourself, and it will even warn about duplicated passwords for these two websites — that's very stupid especially because Apple maintains open database for password managers which solves the problem of alias domains. But that's the most annoying thing for me.

https://github.com/apple/password-manager-resources/blob/mai...

For being "quite obscure", I've at least heard of most of these sites before. Banks with "maxlength: 8", you love to see it.

FWIW, Apple asks users to tell them the password requirements to websites they notice the "Strong Password" feature doesn't work correctly.

The password complexity rule set is open source, you can contribute requirements for specific sites: https://github.com/apple/password-manager-resources