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edgeml-pytorch-0.3.0
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PyTorch code for ML algorithms for edge devices developed at Microsoft Research India.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | edgeml-pytorch-0.3.0 |
| نام | edgeml-pytorch |
| نسخه کتابخانه | 0.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | edgeml@microsoft.com |
| آدرس صفحه اصلی | https://github.com/Microsoft/EdgeML |
| آدرس اینترنتی | https://pypi.org/project/edgeml-pytorch/ |
| مجوز | MIT License |
## Edge Machine Learning: Pytorch Library
This package includes PyTorch implementations of following algorithms and training
techniques developed as part of EdgeML. The PyTorch graphs for the forward/backward
pass of these algorithms are packaged as `edgeml_pytorch.graph` and the trainers
for these algorithms are in `edgeml_pytorch.trainer`.
1. [Bonsai](https://github.com/microsoft/EdgeML/docs/publications/Bonsai.pdf): `edgeml_pytorch.graph.bonsai` implements
the Bonsai prediction graph. The three-phase training routine for Bonsai is decoupled
from the forward graph to facilitate a plug and play behaviour wherein Bonsai can be
combined with or used as a final layer classifier for other architectures (RNNs, CNNs).
See `edgeml_pytorch.trainer.bonsaiTrainer` for 3-phase training.
2. [ProtoNN](https://github.com/microsoft/EdgeML/docs/publications/ProtoNN.pdf): `edgeml_pytorch.graph.protoNN` implements the
ProtoNN prediction functions. The training routine for ProtoNN is decoupled from the forward
graph to facilitate a plug and play behaviour wherein ProtoNN can be combined with or used
as a final layer classifier for other architectures (RNNs, CNNs). The training routine is
implemented in `edgeml_pytorch.trainer.protoNNTrainer`.
3. [FastRNN & FastGRNN](https://github.com/microsoft/EdgeML/docs/publications/FastGRNN.pdf): `edgeml_pytorch.graph.rnn` provides
various RNN cells --- including new cells `FastRNNCell` and `FastGRNNCell` as well as
`UGRNNCell`, `GRUCell`, and `LSTMCell` --- with features like low-rank parameterisation
of weight matrices and custom non-linearities. Akin to Bonsai and ProtoNN, the three-phase
training routine for FastRNN and FastGRNN is decoupled from the custom cells to enable plug and
play behaviour of the custom RNN cells in other architectures (NMT, Encoder-Decoder etc.).
Additionally, numerically equivalent CUDA-based implementations `FastRNNCUDACell` and
`FastGRNNCUDACell` are provided for faster training. `edgeml_pytorch.graph.rnn`.
`edgeml_pytorch.graph.rnn.Fast(G)RNN(CUDA)` provides unrolled RNNs equivalent to `nn.LSTM` and `nn.GRU`.
`edgeml_pytorch.trainer.fastmodel` presents a sample multi-layer RNN + multi-class classifier model.
4. [S-RNN](https://github.com/microsoft/EdgeML/docs/publications/SRNN.pdf): `edgeml_pytorch.graph.rnn.SRNN2` implements a
2 layer SRNN network which can be instantied with a choice of RNN cell. The training
routine for SRNN is in `edgeml_pytorch.trainer.srnnTrainer`.
Usage directions and examples notebooks for this package are provided [here](https://github.com/microsoft/EdgeML/examples/pytorch).
## Installation
It is highly recommended that EdgeML be installed in a virtual environment.
Please create a new virtual environment using your environment manager
([virtualenv](https://virtualenv.pypa.io/en/stable/userguide/#usage) or
[Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands)).
Make sure the new environment is active before running the below mentioned commands.
Use pip to install requirements before installing the `edgeml_pytorch` library.
Details for cpu based installation and gpu based installation provided below.
### CPU
```
pip install -r requirements-cpu.txt
pip install -e .
```
Tested on Python3.6 with >= PyTorch 1.1.0.
### GPU
Install appropriate CUDA and cuDNN [Tested with >= CUDA 8.1 and cuDNN >= 6.1]
```
pip install -r requirements-gpu.txt
pip install -e .
```
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT license.
نحوه نصب
نصب پکیج whl edgeml-pytorch-0.3.0:
pip install edgeml-pytorch-0.3.0.whl
نصب پکیج tar.gz edgeml-pytorch-0.3.0:
pip install edgeml-pytorch-0.3.0.tar.gz