flake8-comprehensions
tensorflow
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| flake8-comprehensions | tensorflow | |
|---|---|---|
| 2 | 223 | |
| 462 | 182,456 | |
| - | 0.8% | |
| 8.2 | 10.0 | |
| 8 days ago | 5 days ago | |
| Python | C++ | |
| MIT License | Apache License 2.0 |
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flake8-comprehensions
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Python toolkits
flake8-comprehensions for writing better list/set/dict comprehensions.
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Found out that using dict() instead of {} has a performance impact. Is there a way to transform every call of dict() to use {} in a automated way? codemod or pre-commit?
Found https://github.com/adamchainz/flake8-comprehensions.
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?
flake8-bandit - Automated security testing using bandit and flake8.
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PEP 8 Speaks - A GitHub :octocat: app to automatically review Python code style over Pull Requests
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
pyupgrade - A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.
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
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
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.
scikit-learn - scikit-learn: machine learning in Python
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.