speech-enhancement VS NumPy

I find writing smoke tests (described here, code examples: model tests + training loop tests) and running them on every commit in CI (eg GitHub Actions) catches a lot of problem. Using pydantic is good for keeping any config files valid is good - you can smoke test those as well.

Even if detailed unit testing is hard, you can smoke test your models in CI to make sure that they're at least not crashing. More on smoke tests here. Some example smoke tests for a neural net here. Running your tests in GitHub Actions is relatively easy (here).

use Packer to pre-build an Amazon Machine Image (AMI) to pre-install all the garbage you need to run your code (eg NVIDIA stuff)

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]