tensorflow_macos
Pointnet_Pointnet2_pytorch
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| tensorflow_macos | Pointnet_Pointnet2_pytorch | |
|---|---|---|
| 33 | 3 | |
| 2,887 | 3,187 | |
| - | - | |
| 3.4 | 0.0 | |
| almost 3 years ago | 4 days 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?
Pointnet_Pointnet2_pytorch
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Help me understand how to use this PointNet implementation (pytorch, point cloud classification)
here is a sample from one of these log files
I am trying to use this implementation of PointNet (GitHub repo)
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Are the New M1 Macbooks Any Good for Deep Learning? Let’s Find Out
Here you go: https://github.com/yanx27/Pointnet_Pointnet2_pytorch - no need for any custom cuda code.
Note the cuda kernels in the original repo were added in August 2017. It might have been the case at the time they needed them, but again, if you need to do something like that today, you're probably an outlier. Modern DL library have a pretty vast assortment of ops. There have been a few cases in the last couple of years when I thought I'd need write a custom op in cuda (e.g. np.unpackbits) but every time I found a way to implement it with native Pytorch ops.
If you're doing DL/CV research, can you give an example from your own work where you really need to run custom cuda code today?
What are some alternatives?
miniforge - A conda-forge distribution.
pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
Pix2Vox - The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)
flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
RAFT
Python-docker - Docker Official Image packaging for Python
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
SimpleView - Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"