HX Lin
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67a35f96e0
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@ -53,7 +53,7 @@ Deep learning frameworks rely on pip for their own installation.
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> [!NOTE]
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>
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> 1. On Windows, it is preferred to install the Python launcher for yourself only.
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> 2. Administrative permission may be required to install Python packages via pip, depending on the target installation directory. E.g. "/usr/local/lib/python3.x/dist-packages".
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> 2. If your Python distribution is installed in the system directory (e.g. the one shipped with Visual Studio 2017), administrative permission is required to install Python packages with pip.
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@ -129,7 +129,10 @@ Then, select e.g. **Python 3.5 (64 bit)** and click ***Make this the default env
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Python is fully supported in Visual Studio Code through extensions.
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Please visit [here](https://code.visualstudio.com/docs/languages/python) for more details.
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## NumPy and SciPy
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## Essential packages
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### NumPy and SciPy
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- **NumPy** is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.
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@ -145,7 +148,37 @@ pip3 install -U numpy scipy
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>
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> The above command will upgrade existing old or unofficial (e.g. third party packages from http://www.lfd.uci.edu/~gohlke/pythonlibs/ for Windows) NumPy and SciPy to the latest official ones.
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## Microsoft Cognitive Toolkit (CNTK)
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### Jupyter Notebook
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[Jupyter Notebook](http://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.
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To install Jupyter Notebook, run the following command in a terminal:
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```bash
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pip3 install jupyter nbconvert
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```
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### Pandas
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[Pandas](https://pandas.pydata.org/) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
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To install Pandas, run the following command in a terminal:
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```bash
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pip3 install pandas
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```
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### Matplotlib
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[Matplotlib](https://matplotlib.org/) is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
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To install Matplotlib, run the following command in a terminal:
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```bash
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pip3 install matplotlib
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```
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## Deep learning and machine learning frameworks
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### Microsoft Cognitive Toolkit (CNTK)
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The [Microsoft Cognitive Toolkit](https://cntk.ai) is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. CNTK supports both Python and BrainScript programming languages.
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@ -193,7 +226,7 @@ To install CNTK BrainScript package, run the following command in a terminal:
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sudo apt-get install libopenmpi-dev
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```
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## TensorFlow
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### TensorFlow
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[TensorFlow](https://www.tensorflow.org/) is an open source software library for numerical computation using data flow graphs.
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Please refer to [here](https://www.tensorflow.org/install/) for detailed installation.
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@ -208,51 +241,7 @@ To install TensorFlow, run the following command in a terminal:
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pip3 install tensorflow==1.4.0
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```
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## Caffe2
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[Caffe2](https://caffe2.ai/) is a lightweight, modular, and scalable deep learning framework.
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Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
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Currently, there's no official prebuilt Caffe2 python wheel package available.
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Please visit [here](https://caffe2.ai/docs/getting-started.html) to build from source code.
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> [!NOTE]
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> [here](https://github.com/Microsoft/samples-for-ai/tree/master/installer) has a third-party Caffe2 0.8.1 Windows wheel package (supports both GPU and CPU).
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## MXNet
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[Apache MXNet (incubating)](https://mxnet.incubator.apache.org/) is a deep learning framework designed for both efficiency and flexibility.
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It allows you to **mix** [symbolic and imperative programming](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts) to maximize efficiency and productivity.
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To install MXNet, run the following command in a terminal:
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- With GPU
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```bash
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pip3 install mxnet-cu80==1.0.0
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```
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- Without GPU
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```bash
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pip3 install mxnet==1.0.0
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```
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## Keras
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[Keras](https://keras.io/) is a high-level neural networks API, written in Python and capable of running on top of CNTK, TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
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To install Keras, please run the following command in a terminal:
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```bash
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pip3 install Keras==2.1.2
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```
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## Theano
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[Theano](http://deeplearning.net/software/theano/) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
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To install Theano, please run the following command in a terminal:
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```bash
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pip3 install Theano==1.0.1
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```
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## PyTorch
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### PyTorch
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[PyTorch](http://pytorch.org/) is a python package that provides two high-level features:
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- Tensor computation (like numpy) with strong GPU acceleration
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@ -293,7 +282,51 @@ Finally, install torchvision on non-Windows:
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pip3 install torchvision
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```
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## Chainer
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### Caffe2
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[Caffe2](https://caffe2.ai/) is a lightweight, modular, and scalable deep learning framework.
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Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
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Currently, there's no official prebuilt Caffe2 python wheel package available.
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Please visit [here](https://caffe2.ai/docs/getting-started.html) to build from source code.
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> [!NOTE]
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> [here](https://github.com/Microsoft/samples-for-ai/tree/master/installer) has a third-party Caffe2 0.8.1 Windows wheel package (supports both GPU and CPU).
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### MXNet
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[Apache MXNet (incubating)](https://mxnet.incubator.apache.org/) is a deep learning framework designed for both efficiency and flexibility.
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It allows you to **mix** [symbolic and imperative programming](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts) to maximize efficiency and productivity.
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To install MXNet, run the following command in a terminal:
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- With GPU
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```bash
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pip3 install mxnet-cu80==1.0.0
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```
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- Without GPU
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```bash
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pip3 install mxnet==1.0.0
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```
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### Keras
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[Keras](https://keras.io/) is a high-level neural networks API, written in Python and capable of running on top of CNTK, TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
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To install Keras, please run the following command in a terminal:
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```bash
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pip3 install Keras==2.1.2
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```
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### Theano
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[Theano](http://deeplearning.net/software/theano/) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
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To install Theano, please run the following command in a terminal:
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```bash
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pip3 install Theano==1.0.1
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```
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### Chainer
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[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility.
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It provides automatic differentiation APIs based on the **define-by-run approach** (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks.
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@ -316,9 +349,19 @@ pip3 install chainer==3.2.0
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```
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To enable multi-node distributed deep learning, please install
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[ChainerMN]{https://github.com/chainer/chainermn} in a terminal:
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[ChainerMN](https://github.com/chainer/chainermn) in a terminal:
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```bash
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pip3 install chainermn
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```
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### scikit-learn
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[scikit-learn](scikit-learn.org) is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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To install scikit-learn, please run the following command in a terminal:
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```bash
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pip3 install scikit-learn
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```
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## [Using a one-click installer to setup deep learning frameworks](https://github.com/Microsoft/samples-for-ai/#using-a-one-click-installer-to-setup-deep-learning-frameworks)
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