benchmarking-public
miniforge
| benchmarking-public | miniforge | |
|---|---|---|
| 3 | 56 | |
| 68 | 5,507 | |
| - | 7.0% | |
| 9.9 | 7.7 | |
| 7 days ago | 10 days ago | |
| Python | Shell | |
| - | 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.
benchmarking-public
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Python 3.12
Benchmarks aren't too promising[0].
I wonder if the original 500% improvement they targeted at the start of the `faster cpython` project is still a realistic target.
[0] https://github.com/faster-cpython/benchmarking-public
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Python 3.12.0rc2
Benchmarks [aren't too promising](https://github.com/faster-cpython/benchmarking-public).
I wonder if the original 500% improvement they targeted at the start of the `faster cpython` project is still a realistic target.
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Faster CPython at PyCon, part two
It is unclear to me whether Python 3.12 will receive significant improvements. Based on the information from https://github.com/faster-cpython/benchmarking-public, it appears that there may be a 2% performance enhancement. Is this the anticipated result, or are there additional developments awaiting merger?
Initially, the "Shannon Plan" (https://github.com/markshannon/faster-cpython/blob/master/pl...) aimed for a 50% improvement with each release. Has this goal been deemed unattainable, or are there adjustments being made to the plan?
miniforge
- Python 3.12
- Installing Anaconda on ChromeOS using Linux
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What is the difference between chat, cai-chat, and instruct, and how to use them?
Nope they don't use venv for any of the oobabooga# variants nor is it recommended for the git version. I'm using https://github.com/conda-forge/miniforge#mambaforge-pypy3 (better version of the recommended conda) for the git variant. The oobabooga* variant uses micro/miniconda (I suck with names) which you can easily drop into with cmd_?something? and does it all internally. Like whoever built that whole environment setup for the _windows/linux/mac did a great job.
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Build llama.cpp on Jetson Nano 2GB
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh .
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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.
Mambaforge!
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A quick guide to using mamba-forge for python virtual environment management
Just to further clarify: you don't need mamba to avoid the Anaconda distribution. The place you get mambaforge also supplies (and originally supplied) miniforge, which is miniconda with conda-forge set as the default channel. All the *forge installers do in this regard is automatically set conda-forge as the default (and only) channel, which is something one can do manually with miniconda.
- Recommendations for Data Science Workflow
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path issue - cannot import modules in jupyter installed via pip3 (m1 mac)
I'd recommend using miniforge if you're comfortable with CLIs, otherwise https://www.anaconda.com/.
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How to get the best Conda environment experience in Codespaces
Tip 1: To use less of your Codespaces resources start with a smaller image like Miniconda or Miniforge and install only what you need.
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Ask HN: Programs that saved you 100 hours? (2022 edition)
miniforge, no need to deal with conda environments anymore. https://github.com/conda-forge/miniforge
What are some alternatives?
faster-cpython - How to make CPython faster.
mamba - The Fast Cross-Platform Package Manager
PyO3 - Rust bindings for the Python interpreter
pyenv - Simple Python version management
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
ipyflow - A reactive Python kernel for Jupyter notebooks.
Poetry - Python packaging and dependency management made easy
CPython - The Python programming language
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
asdf - Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
pip-tools - A set of tools to keep your pinned Python dependencies fresh.