To install this repository and run the Jupyter notebooks on your machine, you will first need git, which you may already have. Open a terminal and type git to check. If you do not have git, 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/UAPH4582/PH482_582
$ cd PH482_582
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.
Next, you will need Python 3.6 or 3.7 and a bunch of Python libraries. The simplest way to install these is to use Anaconda, which is a great cross-platform Python distribution for scientific computing. It comes bundled with many scientific libraries, including NumPy, Pandas, Matplotlib, Scikit-Learn and much more, so it's quite a large installation. If you choose to download and install Anaconda, just make sure to install the Python 3 version. If you prefer a lighter weight Anaconda distribution, you can install Miniconda, which contains the bare minimum to run the conda packaging tool.
Once Anaconda or miniconda is installed, then run the following command to update the conda packaging tool to the latest version:
$ conda update -n base -c defaults conda
Note: if you don't like Anaconda for some reason, then you can install Python 3 and use pip to install the required libraries manually (this is not recommended, unless you know what you are doing).
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 download the latest driver for your card from nvidia.com and install it. You will also need NVidia's CUDA and cuDNN libraries, but the good news is that they will be installed automatically when you install the tensorflow-gpu package from Anaconda. However, if you don't use Anaconda, you will have to install them manually. If you hit any roadblock, see TensorFlow's GPU installation instructions for more details.
If you want to use a GPU then you should also edit environment.yml (or environment-windows.yml if you're on Windows), located at the root of the handson-ml2 project, replace tensorflow=2.0.0 with tensorflow-gpu=2.0.0, and replace tensorflow-serving-api==2.0.0 with tensorflow-serving-api-gpu==2.0.0. This will not be needed anymore when TensorFlow 2.1 is released.
Next, make sure you're in the handson-ml2 directory and run the following command. It will create a new conda environment containing every library you will need to run all the notebooks (by default, the environment will be named tf2, but you can choose another name using the -n option):
$ conda env create -f environment.yml # or environment-windows.yml on Windows
Next, activate the new environment:
$ conda activate tf2
If you're on Windows, and you want to go through chapter 18 on Reinforcement Learning, then you will also need to run the following command. It installs a Windows-compatible fork of the atari-py library.
$ pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
Warning: TensorFlow Transform (used in chapter 13) and TensorFlow-AddOns (used in chapter 16) are not yet available on Windows, but the TensorFlow team is working on it.
You're almost there! You just need to register the tf2 conda environment to Jupyter. The notebooks in this project will default to the environment named python3, so it's best to register this environment using the name python3 (if you prefer to use another name, you will have to select it in the "Kernel > Change kernel..." menu in Jupyter every time you open a notebook):
$ python3 -m ipykernel install --user --name=python3
And that's it! You can now start Jupyter like this:
$ 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!
When you're done with Jupyter, you can close it by typing Ctrl-C in the Terminal window where you started it. Every time you want to work on this project, you will need to open a Terminal, and run:
$ cd $HOME # or whatever development directory you chose earlier
$ cd handson-ml2
$ conda activate tf2
$ jupyter notebook