tensorflow_macos
MMdnn
| tensorflow_macos | MMdnn | |
|---|---|---|
| 33 | 3 | |
| 2,887 | 5,780 | |
| - | 0.1% | |
| 3.4 | 0.0 | |
| almost 3 years ago | 7 months ago | |
| Shell | Python | |
| GNU General Public License v3.0 or later | MIT License |
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tensorflow_macos
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Updated Apple Silicon Guide for M2 Pro and M2 Max Chips
https://github.com/apple/tensorflow_macos is no longer needed
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The hunt for the M1’s neural engine
Tensorflow has a CoreML enabled version which run on ANE.
https://github.com/apple/tensorflow_macos
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M1 Mac users
Apple released a guide on how to use the M1's integrated Neural Chip in TensorFlow. Have a look at this Apple documentation page (and maybe also this GitHub that talks about TensorFlow together with Apple's own ML Compute platform).
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MacBook Air or Wait for new potential MacBook Air with M2
Tensorflow does work on Apple Silicon
- Kernels dying when using tensorflow in Jupyter Notebooks.
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Main PyTorch maintainer confirms that work is being done to support Apple Silicon GPU acceleration for the popular machine learning framework.
Apple did some work to optimize tensorflow for M1, can be found here https://github.com/apple/tensorflow_macos It's alpha, but works fine, I tried it
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The M1 Max is the fastest GPU we have ever measured in Affinity Photo benchmark
https://github.com/apple/tensorflow_macos/issues/25
https://forums.macrumors.com/threads/apple-silicon-deep-lear...
It is expected that the M1 Max should have similar performance to a RTX-2080 or Titan X.
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MacBook Pro M1 Pro benchmark
In case anyone is interested, in ran a fairly simple MNIST benchmark (proposed here : https://github.com/apple/tensorflow_macos/issues/25) on my recently acquired M1 Pro MBP (16-core GPU, 16GB RAM).
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Error while installing tensorflow on Mac M1
The only method I know of to download tensorflow on M1 macs is the one documented here: https://github.com/apple/tensorflow_macos
- How exactly does the Neural Engine benefit the consumer?
MMdnn
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[R] Importing TensorFlow neural networks stored as *.h5
The only way I found online is to convert the *.h5 files to MXNet files using Microsoft model management of deep neural networks (link)
- [D] Tools for converting TF code to Pytorch
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Apple’s New M1 Chip is a Machine Learning Beast
Yes. But that's missing the point. Almost everything in the space uses a framework other than Core ML. Therefore most people need good development support for PyTorch/Tensorflow etc., not Core ML. The fact that Apple has a tool to import/convert models is nice, but not relevant. Also, there is onnx as an exchange format between the frameworks, and tools like MMdnn and others.
What are some alternatives?
miniforge - A conda-forge distribution.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
keras-onnx - Convert tf.keras/Keras models to ONNX
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
redisai-examples - RedisAI showcase
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
torchinfo - View model summaries in PyTorch!
Python-docker - Docker Official Image packaging for Python
keras-ncp - PyTorch and TensorFlow implementation of NCP, LTC, and CfC wired neural models