dh-virtualenv
tensorflow
dh-virtualenv | tensorflow | |
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4 | 223 | |
1,602 | 182,575 | |
0.2% | 0.5% | |
0.0 | 10.0 | |
8 days ago | 1 day ago | |
Python | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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dh-virtualenv
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PyPi root metapackage?
This is how Azure package functions, iirc. I think I used this https://github.com/spotify/dh-virtualenv for similar purpose some time ago.
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What to do about GPU packages on PyPI?
I'm in a devops role where we actually reroll the Tensorflow whl in-house (to get a few tweaks like specific AVX flags turned on), but because the rest of our deployment is apt/debs, we then turn around and wrap that whl in a deb using Spotify's excellent dh-virtualenv:
https://github.com/spotify/dh-virtualenv
There's no expertise for Bazel in-house; when we run the build, it seems to fail all its cache hits and then spend 12-13h in total compiling, much of which appears to be recompiling a specific version of LLVM.
Every dependency is either vendored or pinned, including some critical things that have no ABI guarantees like Eigen, which is literally pinned to some a random commit, so that causes chaos when other binaries try to link up with the underlying Tensorflow shared objects:
https://github.com/tensorflow/tensorflow/blob/master/third_p...
And when you go down a layer into CUDA, there are even more support matrixes listing exact known sets of versions of things that work together:
https://docs.nvidia.com/deeplearning/tensorrt/support-matrix...
Anyway, I'm mostly just venting here. But the whole thing is an absurd nightmare. I have no idea how a normal distro would even begin to approach the task of unvendoring this stuff and shipping a set of normal packages for it all.
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Working with Rust in VSCode’s devcontainer: Seamlessly run your Rust programs under a development container in Visual Studio Code.
Rarely do I develop software at my host level, I'm almost always doing it inside of a container or a virtual machine (local or remote), and part of that is simple muscle memory to solve several problems that you've mentioned in your experience with Python, as well as other languages (bonus points for me that I was never plagued by a horrid npm bug). Those are problems that are easily solvable with Docker and a properly formatted requirements.txt (which should only be used in development — use pip/setup.py for proper deployment) and for the rare times when I do need to use Python at the host level of a Linux system, I use Spotify's dh-virtualenv.
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Is there an excellent Python equivalent for glitch.com?
Maybe I misunderstood the question, but this would be the point of CI/CD and containerization as far as industry standards are concerned. If you had to deploy at host level, then this would be the point of dh-virtualenv that’s tied in by your CI/CD system.
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?
PyInstaller - Freeze (package) Python programs into stand-alone executables
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)
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
PyOxidizer - A modern Python application packaging and distribution tool
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
pyarmor - A tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine or expire obfuscated scripts.
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.
pynsist - Build Windows installers for Python applications
scikit-learn - scikit-learn: machine learning in Python
py2exe - modified py2exe to support unicode paths
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.