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Add instructions to install using the zip file rather than git, if necessary

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Aurélien Geron 2018-05-08 12:52:47 +02:00
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@ -22,6 +22,8 @@ Next, clone this repository by opening a terminal and typing the following comma
$ git clone https://github.com/ageron/handson-ml.git $ git clone https://github.com/ageron/handson-ml.git
$ cd handson-ml $ cd handson-ml
If you do not want to install git, you can instead download [master.zip](https://github.com/ageron/handson-ml/archive/master.zip), unzip it, rename the resulting directory to `handson-ml` 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](https://gym.openai.com/docs) and its dependencies for Atari simulations. If you want to go through chapter 16 on Reinforcement Learning, you will need to [install OpenAI gym](https://gym.openai.com/docs) and its dependencies for Atari simulations.
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](#starting-jupyter) section. If you need detailed instructions, please read on. 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](#starting-jupyter) section. If you need detailed instructions, please read on.
@ -53,9 +55,9 @@ When using Anaconda, you can optionally create an isolated Python environment de
This creates a fresh Python 3.5 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: This creates a fresh Python 3.5 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=1.4.0 $ conda install -n mlbook -c conda-forge tensorflow
This installs TensorFlow 1.4.0 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. 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. Next, you can optionally install Jupyter extensions. These are useful to have nice tables of contents in the notebooks, but they are not required.
@ -64,7 +66,7 @@ Next, you can optionally install Jupyter extensions. These are useful to have ni
You are all set! Next, jump to the [Starting Jupyter](#starting-jupyter) section. You are all set! Next, jump to the [Starting Jupyter](#starting-jupyter) section.
## Using pip ## Using pip
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. 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`. 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`.