geospatial-data-lake
mamba
geospatial-data-lake | mamba | |
---|---|---|
5 | 34 | |
32 | 6,280 | |
- | 2.9% | |
0.0 | 9.5 | |
about 1 year ago | 4 days ago | |
Python | C++ | |
MIT License | BSD 3-clause "New" or "Revised" License |
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.
geospatial-data-lake
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A curated list of questionable installation instructions
One option is to trust on first use, checksum the installation script and at least casually verify the diff each time the checksum changes[1].
Pros:
- Protects against simple hijacking.
- Reproducible as long as the installer doesn't also call out to a moving target, such as example.com/releases/latest.
Cons:
- Build breaks as soon as the installer is bumped. If it's bumped often (or just before an important release) this can cause pain.
- TOFU may not be acceptable, but of course you could review the code thoroughly before even the first use.
[1] https://github.com/linz/geostore/blob/b3cd162605109da8a3a688...
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Ask HN: Good Python projects to read for modern Python?
I'd recommend a project from work, Geostore[1]. Highlights:
- 100% test coverage (with some typical exceptions like `if __name__ == "__main__":` blocks)
- Randomises test sequence and inputs reproducibly
- Passes Pylint with max McCabe complexity of 6
- Passes `mypy --strict`
- Formatted using Black and isort
[1] https://github.com/linz/geostore
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Python Best Practices for a New Project in 2021
The current work project[1] has all of these: Pyenv, Poetry, Pytest, pytest-cov with 100% branch coverage, pre-commit, Pylint rather than Flake8, Black, mypy (with a stricter configuration than recommended here), and finally isort. These are all super helpful.
There's also a simpler template repo[2] with almost all of these.
[1] https://github.com/linz/geostore/
[2] https://github.com/linz/template-python-hello-world
- Codecov bash uploader was compromised
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AWS CloudFormation Best Practices
As someone who's used CDK for a few months and never handcoded CF, that sounds completely correct. If you're comfortable with Python, here's a simple but non-trivial architecture you can check out: https://github.com/linz/geospatial-data-lake/blob/master/app....
mamba
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Minimal implementation of Mamba, the new LLM architecture, in 1 file of PyTorch
>"everyone" seems to know Mamba. I never heard of Mamba
Only the "everybody who knows what mamba is" are the ones upvoting and commenting. Think of all the people who ignore it. For me, Mamba is the faster version of Conda [1], and that's why I clicked on the article.
https://github.com/mamba-org/mamba
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Towards a New SymPy
Yes, this is a big disadvantage. But have you tried Mamba that aims at implementing Anaconda more efficiently? It works really well in most cases.
https://mamba.readthedocs.io/
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Why are the bioconda bioconductor packages so slow to update?
Because conda is very slow at resolving dependencies. Mamba (https://github.com/mamba-org/mamba) is faster if that is your goal
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Is pip gaining on conda for python libs?
use mamba instead
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Real-world examples of std::expected in codebases?
We started using tl::expected in https://github.com/mamba-org/mamba/ since the beginning of this year and some other related projects like https://github.com/mamba-org/powerloader . I don't know much other big open-source codebases that use that specific lib.
- Mamba: A Drop-In Replacement for Conda Written in C++
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What's Great about Julia?
Great writeup. Minor comment about the portion of the post mentioning Conda being glacially slow: Mamba [1] is a much better drop-in replacement written in C++. Not only is it significantly faster, but error messages are much more sane and helpful.
That being said, I do agree that Pkg.jl is much more sleek and modern than Conda/Mamba.
[1]: https://github.com/mamba-org/mamba
- Mamba Reaches 1.0
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Given Rust’s rapidly growing popularity and wide range of use cases, it seems almost inevitable that it will overtake Python in the near future.
I thought that python could live a little longer when I learned about mamba. But then I found out it is written in C++? Why write a package manager for a dying language in a language that is almost dead???
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Does anyone use virtual environments (Conan's virtual env. or Conda's) for C++
Yes, I use Conda enviroments (actually I use Mamba to manage them now).
What are some alternatives?
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
miniforge - A conda-forge distribution.
template-python-hello-world - :triangular_ruler: Python Hello World | Minimal template for Python development
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
asgi-correlation-id - Request ID propagation for ASGI apps
pip - The Python package installer
aws-cdk - The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
pyenv - Simple Python version management
dev-tasks - Automated development tasks for my own projects
conda-lock - Lightweight lockfile for conda environments
pyre-check - Performant type-checking for python.