Dive into Deep Learning
Table Of Contents
Dive into Deep Learning
Table Of Contents

Installation

To get you up and running with hands-on experiences, we’ll need you to set up with a Python environment, Jupyter’s interactive notebooks, the relevant libraries, and the code needed to run the book.

Obtaining Source Codes

The source code package containing all notebooks is available at https://d2l.ai/d2l-en.zip. Please download it and extract it into a folder. For example, on Linux/macos, if you have both wget and unzip installed, you can do it through:

wget https://d2l.ai/d2l-en.zip
unzip d2l-en.zip -d d2l-en

Installing Running Environment

If you have both Python 3.5 or older and pip installed, the easiest way to install the running environment through pip. Two packages are needed, d2l for all dependencies such as Jupyter and saved code blocks, and mxnet for deep learning framework we are using. First install d2l by

pip install d2l

If unfortunately something went wrong, please check

  1. You are using pip for Python 3 instead of Python 2 by checking pip --version. If it’s Python 2, then you may check if there is a pip3 available.
  2. You are using a recent pip, such as version 19. Otherwise you can upgrade it through pip install --upgrade pip
  3. If you don’t have permission to install package in system wide, you can install to your home directory by adding a --user flag. Such as pip install d2l --user

Before installing mxnet, please first check if you are able to access GPUs. If so, please go to GPU Support for instructions to install a GPU-supported mxnet. Otherwise, we can install the CPU version, which is still good enough for the first few chapters.

pip install mxnet

Once both packages are installed, we now open the Jupyter notebook by

jupyter notebook

At this point open http://localhost:8888 (which usually opens automatically) in the browser, then you can view and run the code in each section of the book.

Upgrade to a New Version

Both this book and MXNet are keeping improving. You may want to check a new version from time to time.

  1. This URL https://d2l.ai/d2l-en.zip always points to the contents.
  2. You can upgrade d2l by pip install d2l -U or even just install the latest version from Github by pip install git+https://github.com/d2l-ai/d2l-en.
  3. MXNet can be upgraded by pip install MXNet -U as well.

GPU Support

By default MXNet is installed without GPU support to ensure that it will run on any computer (including most laptops). Part of this book requires or recommends running with GPU. If your computer has NVIDIA graphics cards and has installed CUDA, you should install a GPU-enabled MXNet.

If you have installed the CPU-only version, then remove it first by

pip uninstall mxnet

Then you need to find the CUDA version you installed. You may check it through nvcc --version or cat /usr/local/cuda/version.txt. Assume you have installed CUDA 10.1, then you can install the according MXNet version by

pip install mxnet-cu101

You may change the last digits according to your CUDA version, e.g. cu100 for CUDA 10.0 and cu90 for CUDA 9.0. You can find all available MXNet versions by pip search mxnet.

Exercises

  1. Download the code for the book and install the runtime environment.

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