6 ms·

Looks awesome. I find that pip is usually pretty fast for me, and when it's not, it is mostly because it has to download so much data or wait for native librari

Looks awesome. I find that pip is usually pretty fast for me, and when it's not, it is mostly because it has to download so much data or wait for native libraries to compile in the background (or anything involving cuda which always seems to take forever). What really needs some help with speed is conda, which is just so absurdly slow for literally anything, even on ridiculously powerful machines.

paulddraper 3y ago
Yeah, I'm curious how much uv is actually faster. npm -> Yarn was life changing performance-wise. I wonder what pip -> uv is.
- the_mitsuhiko 3y ago
  From my testing in rye it’s significantly faster in day to day. There are numbers in the blog post obviously but it’s the feeling you get using it locally that makes it much more fun to use.
- laborcontract 3y ago
  Bun, ruff/uv, polars.. all have been major qol improvements. I’m loving the pace of release with this crop of speed obsessed projects and I cannot wait for astral to tackle typing.
  - laborcontract 3y ago
    Ok, here are some benchmarks installing an application that has a couple build-heavy dependencies: Installation times (MM:SS): # No cache - uv: 01:05 - pip: 01:56 # Cache: - uv: 00:02 - pip: 00:42
- eigenvalue 3y ago
  npm seems to have gotten a lot faster lately. All that competition from yarn and now bun seems to have pushed them to focus on optimization.
  - paulddraper 3y ago
    Oh certainly. Npm has improved greatly. Npm features follow a two-step process: (1) Get implemented in Yarn (2) Get implemented in npm.
- driverdan 3y ago
  I did some basic testing today and uv was around 2x faster than pip for a clean venv and cold cache on a real, decent sized project. With warm uv cache it was incredibly fast, under 10 sec.
rogue7 3y ago
For conda there is mamba [0], a drop-in replacement that's really fast. By the way, the creator of mamba started his own company at https://prefix.dev/ https://prefix.dev/ They want to essentially leverage the conda(-forge) infrastructure to build a new cross-platform, cross-language, cargo-like package manager: pixi [0] https://github.com/mamba-org/mamba https://github.com/mamba-org/mamba
- maxnoe 3y ago
  Since last year, conda itself is actually using the mamba solver: https://conda.org/blog/2023-11-06-conda-23-10-0-release/ https://conda.org/blog/2023-11-06-conda-23-10-0-release/
daniel_grady 3y ago
What are some of the reasons that teams use conda (and related tools) today? As a machine learning scientist, I used conda exclusively in the mid-2010s because it was the only framework that could reliably manage Python libraries like NumPy, PyTorch, and so on, that have complex binary dependencies. Today, though, pip install works fine for those packages. What am I missing?
- Ringz 3y ago
  Unfortunately, far too often: tradition. Using only „Pythons native tools“ like pip and venv simply works nowadays so good that I wonder about the purpose of many tools like poetry etc. etc.
  - pininja 3y ago
    Another reason I used to use conda was for easy native Windows installation. GPU accelerated packages like OpenCV were especially difficult when I used use it 6 years ago. Now there’s Linux subsystem.. has pip support dramatically improved on Windows?
  - smohare 3y ago
    [dead]
  - nateglims 3y ago
    The biggest advantage for poetry I found, working with a lot of non-traditional software people, is that it does a lot of things by default like pin versions and manage virtual envs. Unfortunately, it does complicate some things.
    - Ringz 3y ago
      I can understand that well. A few articles from ByteCode! helped me to "follow my intuition" and do as much as possible with native Python tools. https://www.bitecode.dev/p/back-to-basics-with-pip-and-venv https://www.bitecode.dev/p/back-to-basics-with-pip-and-venv https://www.bitecode.dev/p/relieving-your-python-packaging-pain https://www.bitecode.dev/p/relieving-your-python-packaging-p...

paulddraper 3y ago

Yeah, I'm curious how much uv is actually faster. npm -> Yarn was life changing performance-wise. I wonder what pip -> uv is.

the_mitsuhiko 3y ago
From my testing in rye it’s significantly faster in day to day. There are numbers in the blog post obviously but it’s the feeling you get using it locally that makes it much more fun to use.
laborcontract 3y ago
Bun, ruff/uv, polars.. all have been major qol improvements. I’m loving the pace of release with this crop of speed obsessed projects and I cannot wait for astral to tackle typing.
- laborcontract 3y ago
  Ok, here are some benchmarks installing an application that has a couple build-heavy dependencies: Installation times (MM:SS): # No cache - uv: 01:05 - pip: 01:56 # Cache: - uv: 00:02 - pip: 00:42
eigenvalue 3y ago
npm seems to have gotten a lot faster lately. All that competition from yarn and now bun seems to have pushed them to focus on optimization.
- paulddraper 3y ago
  Oh certainly. Npm has improved greatly. Npm features follow a two-step process: (1) Get implemented in Yarn (2) Get implemented in npm.
driverdan 3y ago
I did some basic testing today and uv was around 2x faster than pip for a clean venv and cold cache on a real, decent sized project. With warm uv cache it was incredibly fast, under 10 sec.

rogue7 3y ago

For conda there is mamba [0], a drop-in replacement that's really fast. By the way, the creator of mamba started his own company at https://prefix.dev/ https://prefix.dev/ They want to essentially leverage the conda(-forge) infrastructure to build a new cross-platform, cross-language, cargo-like package manager: pixi [0] https://github.com/mamba-org/mamba https://github.com/mamba-org/mamba

maxnoe 3y ago
Since last year, conda itself is actually using the mamba solver: https://conda.org/blog/2023-11-06-conda-23-10-0-release/ https://conda.org/blog/2023-11-06-conda-23-10-0-release/

daniel_grady 3y ago

What are some of the reasons that teams use conda (and related tools) today? As a machine learning scientist, I used conda exclusively in the mid-2010s because it was the only framework that could reliably manage Python libraries like NumPy, PyTorch, and so on, that have complex binary dependencies. Today, though, pip install works fine for those packages. What am I missing?

Ringz 3y ago
Unfortunately, far too often: tradition. Using only „Pythons native tools“ like pip and venv simply works nowadays so good that I wonder about the purpose of many tools like poetry etc. etc.
- pininja 3y ago
  Another reason I used to use conda was for easy native Windows installation. GPU accelerated packages like OpenCV were especially difficult when I used use it 6 years ago. Now there’s Linux subsystem.. has pip support dramatically improved on Windows?
- smohare 3y ago
  [dead]
- nateglims 3y ago
  The biggest advantage for poetry I found, working with a lot of non-traditional software people, is that it does a lot of things by default like pin versions and manage virtual envs. Unfortunately, it does complicate some things.
  - Ringz 3y ago
    I can understand that well. A few articles from ByteCode! helped me to "follow my intuition" and do as much as possible with native Python tools. https://www.bitecode.dev/p/back-to-basics-with-pip-and-venv https://www.bitecode.dev/p/back-to-basics-with-pip-and-venv https://www.bitecode.dev/p/relieving-your-python-packaging-pain https://www.bitecode.dev/p/relieving-your-python-packaging-p...