Each section of this book is a Jupyter notebook. The easiest way to run them is clicking the COLAB button on the upper right of the HTML page, which will directly you to Google Colab with the corresponding notebook opened. Running the first code cell will connect to a host runtime and show the following warning message. You can click RUN ANYWAY to continue.
The reset of this section will go through how to set up a Python environment, Jupyter’s interactive notebooks, the relevant libraries, and the code needed to run the book you can run them on your machines.
1.1.1. Obtaining Source Codes¶
The source code package containing all notebooks is available at
http://tvm.d2l.ai/d2l-tvm.zip. Please download it and extract it into a
folder. For example, on Linux/macOS, if you have both
unzip installed, you can do it through:
wget http://tvm.d2l.ai/d2l-tvm.zip unzip d2l-tvm.zip -d d2l-tvm
1.1.2. Installing Running Environment¶
If you have both
Python 3.5 or later and
pip installed, the
easiest way to install the running environment is through
required packages are
d2ltvmfor all dependencies such as Jupyter and saved code blocks
tvm[Chen et al., 2018] for the deep learning compiler we are using
mxnetas the baseline in some chapters
pip install git+https://github.com/d2l-ai/d2l-tvm
tvm from source codes. TVM doesn’t have a pip package
because it highly depends on the libraries available on your system.
Please follow the instructions on
tvm.ai to install
tvm. The configuration in
config.cmake this book requires at
If you plan to run on Nvidia GPUs as well, you will also need to
Also don’t forget to enable
cython, which accelerates the
performance. You just need to run
make cython in the TVM source
If luckily you are using Ubuntu with
cuda-10.1 installed, you may use the pre-built library that is for
evaluating this book:
pip install https://tvm-repo.s3-us-west-2.amazonaws.com/tvm-0.7.dev1-cp37-cp37m-linux_x86_64.whl
Our code runs on tvm-0.7-dev1 for now.
Finally, install MXNet’s CUDA version if GPUs are available [Chen et al., 2015]. Assume you are have CUDA 10.1 installed, then
pip install mxnet-cu101
You can change the
101 to match your CUDA version.
Once all packages are installed, you can open the Jupyter notebook by
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.
Throughout the book, we save reusable code blocks in the
package by adding the comment: “
# Save to the d2ltvm package.”
before the code block. For example, the following code snippet shows the
libraries imported by
# Save to the d2ltvm package. import tvm from tvm import te import time import timeit import numpy as np from matplotlib import pyplot as plt from IPython import display try: import mxnet as mx except: pass