mindspore
tensorflow
Our great sponsors
mindspore | tensorflow | |
---|---|---|
6 | 222 | |
4,052 | 182,456 | |
2.6% | 0.8% | |
9.9 | 10.0 | |
4 days ago | 4 days ago | |
C++ | C++ | |
Apache License 2.0 | 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.
mindspore
-
Give us your feedback, by submitting any issues or PRs on GitHub
Attention developers: ready to get in the holiday spirit with #MindSpore! Give us your feedback, by submitting any issues or PRs on #GitHub: https://github.com/mindspore-ai/mindspore/issues. And let us know how do you plan to celebrate :
-
MindSpore Technical Forum: New Trend of AI for Science
To learn more about MindSpore, please visit the following links: Official website: https://www.mindspore.cn/en Gitee: https://gitee.com/mindspore/mindspore GitHub: https://github.com/mindspore-ai/mindspore
-
MindSpore Technical Forum: Will Foundation Models Lead the Future of AI?
Foundation models are an important and emerging trend. Despite recent best practices, these models still face great challenges, such as system emergent behavior and homogenization. We hope to hear from leading voices on foundation models for future cooperation. To learn more, visit MindSpore official website, Gitee and GitHub.
-
Support for macOS Added in MindSpore 1.6
GitHub: https://github.com/mindspore-ai/mindspore
-
Anyone can Succeed with MindSpore's Probabilistic Deep Learning Framework
To learn more, please visit our MindSpore GitHub repository at https://github.com/mindspore-ai/mindspore.
-
[D] Keras: Killed by Google
Our team recently released a new high level API project called TinyMS which runs on MindSpore, a new JAX-ish open source deep learning framework. Feel free to check out the TinyMS Documentation and leave us feedback via ISSUE or on slack :)
tensorflow
-
Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
-
🔥🚀 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
-
10 Github repositories to achieve Python mastery
Explore here.
-
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.
-
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.
-
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:
-
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? :)
-
How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
What are some alternatives?
models - Models and examples built with TensorFlow
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)
tinyms - Easy-to-Use deep learning development toolkit.
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
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.
PyBrain
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration