Helping you deploy with confidence, ship higher quality code, and speed up your application.| Python⇒Speed
Sometimes you can trick the compiler into generating more efficient code. How can you test this optimization continues to be applied?| Python⇒Speed
Helping you deploy with confidence, ship higher quality code, and speed up your application.| Python⇒Speed
There are many approaches to speeding up Python code; applying multiple approaches can make your code even faster.| Python⇒Speed
Helping you deploy with confidence, ship higher quality code, and speed up your application.| Python⇒Speed
Vectorization allows you to speed up processing of homogeneous data in Python. Learn what it means, when it applies, and how to do it.| Python⇒Speed
Vectorization is a great way to speed up your Python code, but you’re limited to specific operations on bulk data. Learn how to get pass these limitations.| Python⇒Speed
Parallelism isn’t the only answer: often you can optimize low-level code to get significant performance improvements.| Python⇒Speed
Multi-stage Docker image builds give you small images and fast builds, but only if takes extra steps prevent slowness due to caching problems.| Python⇒Speed
Pydantic’s JSON loading uses a huge amount of memory; here’s how to reduce it.| Python⇒Speed
If you want to save memory when reading from a BytesIO, getvalue() is surprisingly a good choice.| Python⇒Speed
Slow software performance may be due to CPU, I/O, locks, and more. Learn a quick heuristic to help you identify which it is.| Python⇒Speed
Installing packages with pip can be slow. Learn some ways to speed it up.| Python⇒Speed
Find and fix memory problems in your Python code with the open source Fil memory profiler.| Python⇒Speed
There are many ways Python out-of-memory problems can manifest: slowness due to swapping, crashes, MemoryError, segfaults, kill -9.| Python⇒Speed
If you have a good test suite, you may be able use pytest fixtures to identify memory and other resource leaks.| Python⇒Speed
Speeding up your software can reduce carbon emissions from running your software, two different ways.| Python⇒Speed
Ubuntu? Official Python images? Alpine Linux? Here’s how to choose a good base Docker image for your Python application container.| Python⇒Speed
You can write Python extensions with Cython, Rust, and many other tools. Learn which one you should use, depending on your particular needs.| Python⇒Speed
SIMD can speed up your code; here’s how to use it with stable Rust.| Python⇒Speed
The uv package manager can also install Python for you. Should you use this feature in production?| Python⇒Speed
Python code too slow? You can quickly create a Rust extension to speed it up.| Python⇒Speed
Optimizing your code is a valuable addition to multi-core parallelism. In this article, we’ll see an example using SIMD.| Python⇒Speed
Numba can make your numeric code faster, but only if you use it right.| Python⇒Speed
Will Jevons Paradox undo the benefits of efficient software? Not necessarily.| Python⇒Speed
Climate change is impacting the whole planet, and getting worse every year. What can you do to help?| Python⇒Speed
If you want to run GPU programs in CI, on Macs, and more, wgu-py is a good option.| Python⇒Speed
Are GPU replacements for CPU-based libraries really that much faster?| Python⇒Speed
Alpine Linux is often recommended as a smaller, faster Docker base image. But if you’re using Python, it will slow down your build and make your image larger.| Python⇒Speed
With a little understanding of how CPUs and compilers work, you can speed up NumPy with faster Numba code.| Python⇒Speed
Getting a externally-managed-environment/PEP 668 error when you pip install? Here’s how to fix it.| Python⇒Speed
Ruff is a new linter that is vastly faster than PyLint and flake8—with many of the same checks.| Python⇒Speed
Initial and exploratory data analysis have different requirements than production data processing; Polars supports both.| Python⇒Speed
Sometimes the way to speed up your application is a better data structures and more efficient code.| Python⇒Speed