flake8-bugbear
tensorflow
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| flake8-bugbear | tensorflow | |
|---|---|---|
| 2 | 221 | |
| 1,037 | 182,456 | |
| 1.2% | 0.7% | |
| 8.3 | 10.0 | |
| 5 days ago | about 9 hours ago | |
| Python | C++ | |
| MIT License | Apache License 2.0 |
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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.
flake8-bugbear
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Bugbear is not specifically a security tool but serves as an effective guard against common coding errors and pitfalls. It pinpoints and rectifies frequent mistakes like setting a list as a default value for a parameter and cautions against such practices, enhancing code robustness.
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Python toolkits
flake8-bugbear for finding likely bugs and design problems in your program. flake8-bugbear - Finding likely bugs and design problems in your program.
tensorflow
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
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How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
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When the documentation has TODOs
Since you've specifically mentioned ML, here's Tenserflow's GitHub. I'm sure a quick glance through that will change your mind.
What are some alternatives?
Flake8 - flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Pylint - It's not just a linter that annoys you!
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
pycodestyle - Simple Python style checker in one Python file
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
megalinter - 🦙 Mega-Linter analyzes 49 languages, 22 formats, 21 tooling formats, excessive copy-pastes, spelling mistakes and security issues in your repository sources with a GitHub Action, other CI tools or locally. [Moved to: https://github.com/oxsecurity/megalinter]
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
megalinter - 🦙 MegaLinter analyzes 50 languages, 22 formats, 21 tooling formats, excessive copy-pastes, spelling mistakes and security issues in your repository sources with a GitHub Action, other CI tools or locally.
scikit-learn - scikit-learn: machine learning in Python
wemake-python-styleguide - The strictest and most opinionated python linter ever!
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.