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finet's Introduction

MegEngine

MegEngine is a fast, scalable, and user friendly deep learning framework with 3 key features.

  • Unified framework for both training and inference
    • Quantization, dynamic shape/image pre-processing, and even derivation with a single model.
    • After training, put everything into your model to inference on any platform with speed and precision. Check here for a quick guide.
  • The lowest hardware requirements
    • The memory usage of the GPU can be reduced to one-third of the original memory usage when DTR algorithm is enabled.
    • Inference models with the lowest memory usage by leveraging our Pushdown memory planner.
  • Inference efficiently on all platforms
    • Inference with speed and high-precision on x86, Arm, CUDA, and RoCM.
    • Supports Linux, Windows, iOS, Android, TEE, etc.
    • Optimize performance and memory usage by leveraging our advanced features.

Installation

NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.6 to 3.9. On Windows 10 you can either install the Linux distribution through Windows Subsystem for Linux (WSL) or install the Windows distribution directly. Many other platforms are supported for inference.

Binaries

To install the pre-built binaries via pip wheels:

python3 -m pip install --upgrade pip
python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html

Building from Source

How to Contribute

We strive to build an open and friendly community. We aim to power humanity with AI.

How to Contact Us

Resources

License

MegEngine is licensed under the Apache License, Version 2.0

Citation

If you use MegEngine in your publication,please cite it by using the following BibTeX entry.

@Misc{MegEngine,
  institution = {megvii},
  title =  {MegEngine:A fast, scalable and easy-to-use deep learning framework},
  howpublished = {\url{https://github.com/MegEngine/MegEngine}},
  year = {2020}
}

Copyright (c) 2014-2021 Megvii Inc. All rights reserved.

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finet's Issues

there is a conflict between cuda and megengine

We have met the problem when we train FINet, do you have any idea? Thanks.

Traceback (most recent call last):
File "/home/hongchang/.conda/envs/finet/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/home/hongchang/.conda/envs/finet/lib/python3.8/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/hongchang/.conda/envs/finet/lib/python3.8/site-packages/megengine/distributed/launcher.py", line 43, in _run_wrapped
_check_device_initialized(device_type, dev)
File "/home/hongchang/.conda/envs/finet/lib/python3.8/site-packages/megengine/distributed/helper.py", line 195, in _check_device_initialized
raise RuntimeError(errmsg)
RuntimeError: The cuda env is set before the forked thread starts. Please do not use any cuda function or variable before forking.
Process Process-2:

How to use the trained model?

Hi author, thank you very much for your excellent work. I would like to ask, if I now have two tensors of shape n1x3 and n2X3, how should I use the trained model?

I've met some trouble in using megengine

Traceback (most recent call last):
File "train.py", line 148, in
train_proc(params)
File "train.py", line 136, in main
train_and_evaluate(model, manager)
File "train.py", line 92, in train_and_evaluate
train(model, manager, gm)
File "train.py", line 56, in train
gm.backward(loss["total"])
File "/home/jzf/anaconda3/envs/meg/lib/python3.7/site-packages/megengine/autodiff/grad_manager.py", line 289, in backward
self._grad(ys, dys)
File "/home/jzf/anaconda3/envs/meg/lib/python3.7/site-packages/megengine/core/autodiff/grad.py", line 67, in call
self._impl.backward(ys, dys)
File "/home/jzf/anaconda3/envs/meg/lib/python3.7/site-packages/megengine/core/autodiff/grad.py", line 20, in backward
return core2.backward(self, ys, dys)
RuntimeError: assertion `val.shape().is_scalar()' failed at ../../../../../../src/opr/impl/tensor_manip.cpp:1011: bool mgb::opr::Split::infer_shape(size_t, megdnn::TensorShape&, const mgb::cg::static_infer::InpVal&)
extra message: shapes for Split must be scalars

missing files?

├── modelnet40_half1_rm_rotate.txt
├── modelnet40_half2_rm_rotate.txt

Where can I get these two files?thanks

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