python-feedstock
conda
| python-feedstock | conda | |
|---|---|---|
| 2 | 30 | |
| 44 | 6,092 | |
| - | 0.7% | |
| 7.8 | 9.8 | |
| 7 days ago | 7 days ago | |
| Shell | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
python-feedstock
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Python 3.11.0 final is now available
It's already there:
https://anaconda.org/conda-forge/python
https://github.com/conda-forge/python-feedstock/pull/577
Using mamba to create a new encoding called py311 with python 3.11:
mamba create -n py311 python=3.11 -
Notes from the Meeting on Python GIL Removal Between Python Core and Sam Gross
https://news.ycombinator.com/item?id=18040664
Today, conda-forge compiles CPython to relocatable platform+architecture-specific binaries with LLVM. https://github.com/conda-forge/python-feedstock/blob/master/...
Pyodide (JupyterLite) compiles CPython to WASM (or LLVM IR?) with LLVM/emscripten IIRC. Hopefully there's a clear way to implement the new GIL-less multithreading support with Web Workers in WASM, too?
The https://rapids.ai/ org has a bunch a fast Python for HPC; with Dask and pick a scheduler. Less process overhead and less need for interprocess locking of memory handles that transgress contexts due to a new GIL removal approach would be even faster than debuggable one process per core Python.
conda
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How to Create Virtual Environments in Python
Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.
- Why does creating my conda environment use so much memory?
- Installing Anaconda on ChromeOS using Linux
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PSA: conda-libmamba-solver can cut two hours off of your Anaconda install, but has only 47 GitHub stars. It deserves more praise.
conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.
- Is Anaconda still open source?
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How to get the best Conda environment experience in Codespaces
The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).
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What is the status of Python 3.11?
It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.
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Miniconda finally released for Python 3.10
It took some time but as great Christmas present Miniconda was finally released with Python 3.10!
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TW: ZSH (and BASH?) does not show current working dir etc anymore
The September update broke it.
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Python 3.11.0 is now available
According to this this issue is high on their priority list (whatever that means).
What are some alternatives?
nogil - Multithreaded Python without the GIL
mamba - The Fast Cross-Platform Package Manager
import-linter - Import Linter allows you to define and enforce rules for the internal and external imports within your Python project.
Poetry - Python packaging and dependency management made easy
django-stubs - PEP-484 stubs for Django
miniforge - A conda-forge distribution.
celery-types - :seedling: Type stubs for Celery and its related packages
PDM - A modern Python package and dependency manager supporting the latest PEP standards
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
public-conventions - In-house conventions and styles
pip - The Python package installer