To install this repository and run the Jupyter notebooks on your machine, you will first need git, which you probably have already. If not, you can download it from git-scm.com.
Next, clone this repository by opening a terminal and typing the following commands:
$ cd $HOME # or any other development directory you prefer
$ git clone https://github.com/ageron/handson-ml2.git
$ cd handson-ml2
If you do not want to install git, you can instead download master.zip, unzip it, rename the resulting directory to handson-ml2 and move it to your development directory.
If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.
If you have a TensorFlow-compatible GPU card (NVidia card with Compute Capability ≥ 3.5), and you want TensorFlow to use it, then you should follow TensorFlow's GPU installation instructions to install the driver and libraries such as CUDA and CuDNN. Note that the installation instructions are still for TF 1.12, not TF 2.0, so you need to install CUDA 10.0 (not 9.2) with the corresponding NVidia driver (see NVidia's website for details) and CuDNN SDK 7.4 (not 7.2). Also edit requirements.txt to replace tf-nightly-2.0-preview with tf-nightly-gpu-2.0-preview.
If you are familiar with Python and you know how to install Python libraries, go ahead and install the libraries listed in requirements.txt and jump to the Starting Jupyter section. If you need detailed instructions, please read on.
Of course, you obviously need Python. Python 2 is already preinstalled on most systems nowadays, and sometimes even Python 3. You can check which version(s) you have by typing the following commands:
$ python --version # for Python 2
$ python3 --version # for Python 3
Right now, only Python 3.6 is supported (TensorFlow support for Python 3.7 is coming soon). If you don't have Python 3, I strongly recommend installing it (Python ≥2.7 may work with minor adjustments, but it is deprecated so Python 3 is preferable). To do so, you have several options: on Windows or MacOSX, you can just download it from python.org. On MacOSX, you can alternatively use MacPorts or Homebrew. If you are using Python 3.6 on MacOSX, you need to run the following command to install the certifi package of certificates because Python 3.6 on MacOSX has no certificates to validate SSL connections (see this StackOverflow question):
$ /Applications/Python\ 3.6/Install\ Certificates.command
On Linux, unless you know what you are doing, you should use your system's packaging system. For example, on Debian or Ubuntu, type:
$ sudo apt-get update
$ sudo apt-get install python3
Another option is to download and install Anaconda. This is a package that includes both Python and many scientific libraries. You should prefer the Python 3 version.
If you choose to use Anaconda, read the next section, or else jump to the Using pip section.
Warning: this section does not work yet, since TensorFlow 2.0 is not yet available Anaconda repositories.
When using Anaconda, you can optionally create an isolated Python environment dedicated to this project. This is recommended as it makes it possible to have a different environment for each project (e.g. one for this project), with potentially different libraries and library versions:
$ conda create -n mlbook python=3.6 anaconda
$ conda activate mlbook
This creates a fresh Python 3.6 environment called mlbook (you can change the name if you want to), and it activates it. This environment contains all the scientific libraries that come with Anaconda. This includes all the libraries we will need (NumPy, Matplotlib, Pandas, Jupyter and a few others), except for TensorFlow, so let's install it:
$ conda install -n mlbook -c conda-forge tensorflow
This installs the latest version of TensorFlow available for Anaconda (which is usually not the latest TensorFlow version) in the mlbook environment (fetching it from the conda-forge repository). If you chose not to create an mlbook environment, then just remove the -n mlbook option.
Next, you can optionally install Jupyter extensions. These are useful to have nice tables of contents in the notebooks, but they are not required.
$ conda install -n mlbook -c conda-forge jupyter_contrib_nbextensions
You are all set! Next, jump to the Starting Jupyter section.
If you are not using Anaconda, you need to install several scientific Python libraries that are necessary for this project, in particular NumPy, Matplotlib, Pandas, Jupyter and TensorFlow (and a few others). For this, you can either use Python's integrated packaging system, pip, or you may prefer to use your system's own packaging system (if available, e.g. on Linux, or on MacOSX when using MacPorts or Homebrew). The advantage of using pip is that it is easy to create multiple isolated Python environments with different libraries and different library versions (e.g. one environment for each project). The advantage of using your system's packaging system is that there is less risk of having conflicts between your Python libraries and your system's other packages. Since I have many projects with different library requirements, I prefer to use pip with isolated environments. Moreover, the pip packages are usually the most recent ones available, while Anaconda and system packages often lag behind a bit.
These are the commands you need to type in a terminal if you want to use pip to install the required libraries. Note: in all the following commands, if you chose to use Python 2 rather than Python 3, you must replace pip3 with pip, and python3 with python.
First you need to make sure you have the latest version of pip installed:
$ python3 -m pip install --user --upgrade pip setuptools
The --user option will install the latest version of pip only for the current user. If you prefer to install it system wide (i.e. for all users), you must have administrator rights (e.g. use sudo python3 -m pip instead of python3 -m pip on Linux), and you should remove the --user option. The same is true of the command below that uses the --user option.
Next, you can optionally create an isolated environment. This is recommended as it makes it possible to have a different environment for each project (e.g. one for this project), with potentially very different libraries, and different versions:
$ python3 -m pip install --user --upgrade virtualenv
$ virtualenv -p `which python3` env
This creates a new directory called env in the current directory, containing an isolated Python environment based on Python 3. If you installed multiple versions of Python 3 on your system, you can replace `which python3` with the path to the Python executable you prefer to use.
Now you must activate this environment. You will need to run this command every time you want to use this environment.
$ source ./env/bin/activate
On Windows, the command is slightly different:
$ .\env\Scripts\activate
Next, use pip to install the required python packages. If you are not using virtualenv, you should add the --user option (alternatively you could install the libraries system-wide, but this will probably require administrator rights, e.g. using sudo pip3 instead of pip3 on Linux).
$ python3 -m pip install --upgrade -r requirements.txt
Great! You're all set, you just need to start Jupyter now.
Okay! You can now start Jupyter, simply type:
$ jupyter notebook
This should open up your browser, and you should see Jupyter's tree view, with the contents of the current directory. If your browser does not open automatically, visit localhost:8888. Click on index.ipynb to get started!
Congrats! You are ready to learn Machine Learning, hands on!