java VS hyperfine

There are Java bindings for TensorFlow, but that's quite low level. I tried to see if I can get some Keras API for Scala, but I'm no expert and haven't had enough time to invest in this, so it's stuck in alpha. Maybe I develop it slow burning over the next year. A bit envious that Kotlin has a Keras-like library.

Hope this is not too late to answer your question. In theory there are no official Java tutorials for Tensorflow 2. The Java implementation is still under development at https://github.com/tensorflow/java

To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.

Here's the latest java git repo https://github.com/tensorflow/java

Benchmarking tool: hyperfine

> On the flip side (making things more random as opposed to less), something that randomizes the initial stack pointer would be nice, as I've sometimes seen this go really, really wrong (renaming a binary from foo to foo_new made it run >1% slower!).

This is something we do already. We set a `HYPERFINE_RANDOMIZED_ENVIRONMENT_OFFSET` environment variable with a random-length value: https://github.com/sharkdp/hyperfine/blob/87d77c861f1b6c761a...

One rarely mentioned tool I absolutely love is hyperfine: https://github.com/sharkdp/hyperfine

It’s a benchmarking CLI tool that can be used as an alternative to `time`. I often use it to detect flacky tests, I run something like `hyperfine —show-output -n=100 'go test ./… -count=1' and it helps me catch tests that fail unreliably

Hope that preliminary benchmarks listed in the README.md could be implemented in a proof-of-concept scheme rewarding functions returning the correct variable within a blockchain.

https://github.com/sharkdp/hyperfine

Dasel MIT License Copyright (c) 2020 Tom Wright

Check out the official hyperfine Github repo

And then I used hyperfine to run the benchmarks on my MacBook Pro 14 M2 Max, and here are the results:

Search "benchmarking tools for linux" and decide that hyperfine is good for what I'm doing. Run Jennifer's new python script against my refactored perl and find that the python is 1.26 times faster for k=3 and 1.47 times faster for k=4. For the Covid-19 sequence, these are both on the order of hundreds of milliseconds.