Installation¶
In order to get you up and running for hands-on learning experience, we need to set you up with an environment for running Python, Jupyter notebooks, the relevant libraries, and the code needed to run the book itself.
Installing Miniconda¶
The simplest way to get going will be to install
Miniconda. The Python
3.x version is required. You can skip the following steps if conda has
already been installed. Download the corresponding Miniconda sh file
from the website and then execute the installation from the command line
using sh <FILENAME> -b. For macOS users:
# The file name is subject to changes
sh Miniconda3-latest-MacOSX-x86_64.sh -b
For Linux users:
# The file name is subject to changes
sh Miniconda3-latest-Linux-x86_64.sh -b
Next, initialize the shell so we can run conda directly.
~/miniconda3/bin/conda init
Now close and re-open your current shell. You should be able to create a new environment as following:
conda create --name d2l python=3.8 -y
Downloading the D2L Notebooks¶
Next, we need to download the code of this book. You can click the “All
Notebooks” tab on the top of any HTML page to download and unzip the
code. Alternatively, if you have unzip (otherwise run
sudo apt install unzip) available:
mkdir d2l-en && cd d2l-en
curl https://d2l.ai/d2l-en.zip -o d2l-en.zip
unzip d2l-en.zip && rm d2l-en.zip
Now we will want to activate the d2l environment.
conda activate d2l
Installing the Framework and the d2l Package¶
Before installing the deep learning framework, please first check whether or not you have proper GPUs on your machine (the GPUs that power the display on a standard laptop do not count for our purposes). If you are installing on a GPU server, proceed to GPU Support for instructions to install a GPU-supported version.
Otherwise, you can install the CPU version as follows. That will be more than enough horsepower to get you through the first few chapters but you will want to access GPUs before running larger models.
pip install mxnet==1.7.0.post1
pip install torch torchvision -f https://download.pytorch.org/whl/torch_stable.html
You can install TensorFlow with both CPU and GPU support via the following:
pip install tensorflow tensorflow-probability
We also install the d2l package that encapsulates frequently used
functions and classes in this book.
# -U: Upgrade all packages to the newest available version
pip install -U d2l
Once they are installed, we now open the Jupyter notebook by running:
jupyter notebook
At this point, you can open http://localhost:8888 (it usually opens
automatically) in your Web browser. Then we can run the code for each
section of the book. Please always execute conda activate d2l to
activate the runtime environment before running the code of the book or
updating the deep learning framework or the d2l package. To exit the
environment, run conda deactivate.
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, then you should install a GPU-enabled version. If you have installed the CPU-only version, you may need to remove it first by running:
pip uninstall mxnet
Then we need to find the CUDA version you installed. You may check it
through nvcc --version or cat /usr/local/cuda/version.txt.
Assume that you have installed CUDA 10.1, then you can install with the
following command:
# For Windows users
pip install mxnet-cu101==1.7.0 -f https://dist.mxnet.io/python
# For Linux and macOS users
pip install mxnet-cu101==1.7.0
You may change the last digits according to your CUDA version, e.g.,
cu100 for CUDA 10.0 and cu90 for CUDA 9.0.
By default, the deep learning framework is installed with GPU support. If your computer has NVIDIA GPUs and has installed CUDA, then you are all set.
By default, the deep learning framework is installed with GPU support. If your computer has NVIDIA GPUs and has installed CUDA, then you are all set.
Exercises¶
Download the code for the book and install the runtime environment.