tensorflow VS Pytorch

Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env.

I would like to use batch normalization in TensorFlow. I found the related C++ source code in core/ops/nn_ops.cc. However, I did not find it documented on tensorflow.org.

For reference: In Tensorflow and JAX, for example, the tensor gets compiled to the intermediate XLA format (https://www.tensorflow.org/xla), then passed to the XLA complier (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla/service) or the new TFRT runtime (https://github.com/tensorflow/runtime/blob/master/documents/tfrt_host_runtime_design.md), or some more esoteric hardware (https://github.com/pytorch/glow).

Looks like it's tensorflow-related, however I have verified that tensorflow is working properly on my system. I could find nothing on Google, there's just one thread discussing exactly the same error (here), however there is still no solution as well. I am aware the error is not symlink related, but just in case this information is needed : (or see in gist)

I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org

I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource?

Maybe related: https://github.com/pytorch/pytorch/issues/12873

Let's dive into the main topic of this article, we are going to train a transformer model for Swahili news classification, Since transformers are large to make the task simple we need to select a wrapper to work with, if you are good with PyTorch you can use PyTorch Lightning a wrapper for high-performance AI research, to wrap the transformers but today lets go with ktrain from Tensorflow Python Library.

The second option is to train your own model using machine learning frameworks like Tensorflow and Pytorch.

The goal of this approach is to replace the intermediate steps with one algorithm. The deep learning approach has achieved state-of-the-art results in speech transcription tasks and is replacing the traditional methods used in ASR. It is also simpler because there are fewer steps involved and does not require as much expertise. The implementation of this approach requires a knowledge understanding of deep learning tools such as PyTorch, Tensorflow, DeepSpeech, etc.

> Is too limited? Too hard to interact with? Not worth the effort?

IIRC the only way to access ANE is through the Accelerate framework, and it seems to have pretty severe limitations[0].

Apple has developed a tensorflow plugin[1] but i can't tell you if it uses ANE. Earlier this year they also published a job offer talking about accelerating PyTorch with BNNS and Accelerate[2]. Apparently PyTorch already uses Accelerate and AMX (the matrix coprocessor).

So might indeed be that ANE is too limited and Accelerate never gets to use it.

[0] https://github.com/hollance/neural-engine

[1] https://developer.apple.com/metal/tensorflow-plugin/

[2] https://github.com/pytorch/pytorch/issues/47702#issuecomment...

What is the requirement of something to be compatible with PyTorch? You can in fact ask them about this specifically, they are always a helpful bunch https://github.com/pytorch/pytorch

PyTorch GitHub

What is PyTorch