mutmut
tensorflow
mutmut | tensorflow | |
---|---|---|
4 | 223 | |
862 | 182,693 | |
- | 0.6% | |
6.9 | 10.0 | |
19 days ago | 3 days ago | |
Python | C++ | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
mutmut
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Scientist: A Ruby library for carefully refactoring critical paths
I wrote one (https://github.com/boxed/scientist) as I found the existing ones very complicated and that just gives me a bad feeling. Since I'm the author of mutmut (https://github.com/boxed/mutmut), I also made sure my implementation was 100% mutation tested before I used it in production.
I used my implementation to replace number parsing in my work project: https://kodare.net/2021/04/04/safe_number_parsing.html
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Mutmut introduces a clever approach to scrutinizing your tests. It evaluates the effectiveness of your test suite by slightly altering the code after the tests have been written. If a test fails after a minor change, that's a good sign; it means the test is robust enough to catch those changes. But if the test passes even after the code change, it indicates that the test isn't effectively detecting that alteration – this is what Mutmut terms a "surviving mutant."
- Boring Python: Code Quality
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Python toolkits
mutmut for mutation testing.
tensorflow
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Side Quest Devblog #1: These Fakes are getting Deep
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- 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? :)
What are some alternatives?
cookiecutter-hypermodern-python - Hypermodern Python Cookiecutter
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)
reorder-python-imports - Rewrites source to reorder python imports
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
go-scientist
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
Poetry - Python packaging and dependency management made easy
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.
hypothesis - Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
scikit-learn - scikit-learn: machine learning in Python
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.