Vcpkg
tensorflow
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Vcpkg | tensorflow | |
---|---|---|
144 | 221 | |
21,191 | 181,467 | |
2.1% | 0.7% | |
10.0 | 10.0 | |
6 days ago | 7 days ago | |
CMake | C++ | |
MIT 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.
Vcpkg
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Dependencies Belong in Version Control
vcpkg may expire assets after 1.5 years, so achieve long-term reproducibility you will need to cache your dependencies.... Somewhere. Not sure what the expected solution is.
https://github.com/microsoft/vcpkg/pull/30546#issuecomment-1...
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My first Software Release using GitHub Release
There were various approaches recommended depending on our language and ecosystem. My classmates who developed using Node.js were recommended npm, and PyPI or poetry for Python. Since my program is written in C++, I was recommended to look into one of vcpkg or conan, but I ultimately did not use either package manager.
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Anyone else frustrated with Conan2?
Which dependencies are not in vcpkg? We can ask them to add it. It’s pretty easy just open an issue there https://github.com/microsoft/vcpkg/issues .
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hypergrep: A new "fastest grep" to search directories recursively for a regex pattern
CMake Error at scripts/cmake/vcpkg_execute_build_process.cmake:134 (message): Command failed: /usr/bin/cmake --build . --config Debug --target install -- -v -j25 Working Directory: /opt/vcpkg/buildtrees/hyperscan/x64-linux-dbg See logs for more information: /opt/vcpkg/buildtrees/hyperscan/install-x64-linux-dbg-out.log Call Stack (most recent call first): installed/x64-linux/share/vcpkg-cmake/vcpkg_cmake_build.cmake:74 (vcpkg_execute_build_process) installed/x64-linux/share/vcpkg-cmake/vcpkg_cmake_install.cmake:16 (vcpkg_cmake_build) ports/hyperscan/portfile.cmake:22 (vcpkg_cmake_install) scripts/ports.cmake:147 (include) error: building hyperscan:x64-linux failed with: BUILD_FAILED Please ensure you're using the latest port files with `git pull` and `vcpkg update`. Then check for known issues at: https://github.com/microsoft/vcpkg/issues?q=is%3Aissue+is%3Aopen+in%3Atitle+hyperscan You can submit a new issue at: https://github.com/microsoft/vcpkg/issues/new?title=[hyperscan]+Build+error&body=Copy+issue+body+from+%2Fopt%2Fvcpkg%2Finstalled%2Fvcpkg%2Fissue_body.md You can also sumbit an issue by running (GitHub cli must be installed): gh issue create -R microsoft/vcpkg --title "[hyperscan] Build failure" --body-file /opt/vcpkg/installed/vcpkg/issue_body.md
The hyperscan update to vcpkg seems to have happened from 5.4.0 to 5.4.2 in this commit on Apr 20.
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Configuring incomplete due to CMake Error(missing OpenCVConfig.cmake ProtobufConfig.cmake and TIFF etc.)
Dear Fictrac team, I am hoping to install Fictrac in our windows 11 x64 laptop (Visual Studio 2019, cMake 3.26.4). I followed the installation guideline on github page fictrac and used the latest vcpkg
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The Future of Boost by Vinnie Falco
unless you want to use clang-cl since it renames the output to make it work for MSVC which in return breaks FindBoost in cmake and requieres https://github.com/microsoft/vcpkg/pull/27694 to fix it. I have touched enough of vcpkg build scripts to know what works and what doesn't and the b2 build is one of the corners I strongly dislike.
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CMake and Linking External libraries is a kick in the nuts if i've ever seen it.
And then there's also Qt which has plugins. vcpkg Qt5 is nice enough to copy the plugins for you, but not with Qt6. The official answer seems to be "use windeployqt". So I do, and it copies plugins fine. But sqlite doesn't work, despite the plugin sqldrivers/qsqlite.dll being in the right location. Turns out that neither vcpkg or windeployqt copy sqlite3.dll. I switched to static libraries after that, it's a lot slower to link, clang doesn't work for some reason (but clang-cl does) but at least I don't have to worry about DLLs.
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tensorflow
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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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How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
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Ask HN: What is a AI chip and how does it work?
This is indeed the bread-and-butter, but there is use of all sorts of standard linear algebra algorithms. You can check various xla-related (accelerated linear algebra) folders in tensorflow or torch folders in pytorch to see the list of what is used [1],[2]
[1] https://github.com/tensorflow/tensorflow/tree/8d9b35f442045b...
[2] https://github.com/pytorch/pytorch/blob/6e3e3dd477e0fb9768ee...
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
2. TensorFlow Developed by the Google Brain team, TensorFlow is a powerful open-source machine learning framework that’s perfect for deep learning and neural network projects. With TensorFlow, you can build and train complex models using an intuitive and flexible API, making it an essential tool for any data scientist looking to delve into deep learning.
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Tensorflow V2 - LSTM Penn Tree Bank Dataset
I found the official Tensorflow V1 code from a Github branch here (https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/models/rnn/ptb/ptb_word_lm.py). All code necessary to run that file is in the /ptb folder (except data).
What are some alternatives?
conan - Conan - The open-source C and C++ package manager
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)
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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
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
PyBrain
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.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
MLflow - Open source platform for the machine learning lifecycle