.. _ch_install:

Installation
============


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.

.. _img_colab:

.. figure:: ../img/colab.png
   :width: 400px

   Click RUN ANYWAY to run a section on Colab.



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.

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 ``wget`` and
``unzip`` installed, you can do it through:

.. code:: bash

    wget http://tvm.d2l.ai/d2l-tvm.zip
    unzip d2l-tvm.zip -d d2l-tvm

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 ``pip``. The
required packages are

-  ``d2ltvm`` for all dependencies such as Jupyter and saved code blocks
-  ``tvm`` :cite:`Chen.Moreau.Jiang.ea.2018` for the deep learning
   compiler we are using
-  ``mxnet`` as the baseline in some chapters

First install ``d2ltvm``:

.. code:: bash

    pip install git+https://github.com/d2l-ai/d2l-tvm

Then compile ``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 <https://docs.tvm.ai/install/from_source.html>`__ to install
``tvm``. The configuration in ``config.cmake`` this book requires at
least

.. code:: bash

    set(USE_LLVM ON)

If you plan to run on Nvidia GPUs as well, you will also need to

.. code:: bash

    set(USE_CUDA ON)

Also don't forget to enable ``cython``, which accelerates the
performance. You just need to run ``make cython`` in the TVM source
folder.

If luckily you are using Ubuntu with ``python-3.7``, ``llvm-6.0`` and
``cuda-10.1`` installed, you may use the pre-built library that is for
evaluating this book:

.. code:: bash

    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
:cite:`Chen.Li.Li.ea.2015`. Assume you are have CUDA 10.1 installed,
then

.. code:: bash

    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

.. code:: bash

    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.

.. _ch_code:

Code
----


Throughout the book, we save reusable code blocks in the ``d2ltvm``
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 ``d2ltvm``.

.. code:: python

    # 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

`Discussions <https://discuss.tvm.ai/t/getting-started-installation/4706>`__
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