JAX Vs TensorFlow Vs PyTorch: A Comparative Analysis

Deep learning owes a lot of its success to automatic differentiation. Popular libraries such as TensorFlow and PyTorch keep track of gradients over neural network parameters during training with both comprising high-level APIs for implementing the commonly used neural network functionality for deep learning. JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research. Along with a Deep Learning framework, JAX has created a super polished linear algebra library with automatic differentiation and XLA support.
Read more: https://analyticsindiamag.com/jax-vs-tensorflow-vs-pytorch-a-comparative-analysis/

#tensorflow #pytorch

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JAX Vs TensorFlow Vs PyTorch: A Comparative Analysis

JAX Vs TensorFlow Vs PyTorch: A Comparative Analysis

Deep learning owes a lot of its success to automatic differentiation. Popular libraries such as TensorFlow and PyTorch keep track of gradients over neural network parameters during training with both comprising high-level APIs for implementing the commonly used neural network functionality for deep learning. JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research. Along with a Deep Learning framework, JAX has created a super polished linear algebra library with automatic differentiation and XLA support.
Read more: https://analyticsindiamag.com/jax-vs-tensorflow-vs-pytorch-a-comparative-analysis/

#tensorflow #pytorch

How PyTorch Is Challenging TensorFlow Lately

  • PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.

Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground.

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Interest over time for TensorFlow (top) and PyTorch (bottom) in India (Credit: Google Trends)

PyTorch–a framework for deep learning that integrates with important Python add-ons like NumPy and data-science tasks that require faster GPU processing–made some recent additions:

  • Enterprise support**: **After taking over the Windows 10 PyTorch library from Facebook to boost GPU-accelerated machine learning training on Windows 10’s Subsystem for Linux(WSL), Microsoft recently added enterprise support for PyTorch AI on Azure to give PyTorch users a more reliable production experience. “This new enterprise-level offering by Microsoft closes an important gap. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments,” Jeremy Jancsary, a senior principal research scientist at Nuance, said.
  • PyTorchVideois a deep learning library for video understanding unveiled by Facebook AI recently. The source code is available on GitHub. With this, Facebook aims to support researchers develop cutting-edge machine learning models and tools. These models can enhance video understanding capabilities along with providing a unified repository of reproducible and efficient video understanding components for research and production applications.
  • PyTorch Profiler: In April this year, PyTorch announced its new performance debug profiler, PyTorch Profiler, along with its 1.8.1 version release. The new tool enables accurate and efficient performance analysis in large scale deep learning models.

#opinions #deep learning frameworks #machine learning pytorch #open-source frameworks #pytorch #tensorflow #tensorflow 2.0

Justyn  Ortiz

Justyn Ortiz

1610436416

Guide to Conda for TensorFlow and PyTorch

Learn how to set up anaconda environments for different versions of CUDA, TensorFlow, and PyTorch

It’s a real shame that the first experience that most people have with deep learning is having to spend days trying to figure out why the model they downloaded off of GitHub just… won’t… run….

Dependency issues are incredibly common when trying to run an off-the-shelf model. The most problematic of which is needing to have the correct version of CUDA for TensorFlow. TensorFlow has been prominent for a number of years meaning that even new models that are released could use an old version of TensorFlow. This wouldn’t be an issue except that it feels like every version of TensorFlow needs a specific version of CUDA where anything else is incompatible. Sadly, installing multiple versions of CUDA on the same machine can be a real pain!

#machine-learning #pytorch #tensorflow #pytorch

Arno  Bradtke

Arno Bradtke

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How Does It Stack Up Against Autograd, TensorFlow, and PyTorch?

In this article, take a look at accelerated automatic differentiation with Jax and see how it stacks up against Autograd, TensorFlow, and PyTorch.

Differentiable Programming With JAX

Automatic differentiation underlies the vast majority of success in modern deep learning. This makes a big difference in development time for researchers iterating over models and experiments. Before widely available tools for automatic differentiation, programmers had to “roll their own” gradients, which is not only time-consuming but introduces a substantial coding surface that increases the probability of accumulating disastrous bugs.

Libraries like the well-known TensorFlow and PyTorch keep track of gradients over neural network parameters during training, and they each contain high-level APIs for implementing the most commonly used neural network functionality for deep learning. While this is ideal for production and scaling models to deployment, it leaves something to be desired if you want to build something a little off the beaten path. Autograd is a versatile library for automatic differentiation of native Python and NumPy code, and it’s ideal for combining automatic differentiation with low-level implementations of mathematical concepts to build not only new models, but new types of models (including hybrid physics and neural-based learning models).

While it is a flexible library with an inviting learning curve (NumPy users can jump in at the deep end), Autograd is no longer under active development and it tends to be too slow for medium to large-scale experiments. Development for running Autograd on GPUs was never completed, and therefore training is limited by the execution time of native NumPy code. Consequently JAX is a better choice of automatic differentiation libraries for many serious projects, thanks to just-in-time compilation and support for hardware acceleration.

#machine learning #artificial intelligence #tensorflow #jax #pytorch #machine learning libraries #autograd

Pytorch vs Tensorflow vs Keras | Deep Learning Tutorial (Tensorflow, Keras & Python)

We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.

#pytorch #tensorflow #keras #python #deep-learning