Update installation instructions and have just one environment.yml for all platforms
Aurélien Geron
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INSTALL.md
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INSTALL.md
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@ -24,25 +24,15 @@ Once Anaconda or miniconda is installed, then run the following command to updat
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## Install the GPU Driver and Libraries
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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](https://www.nvidia.com/Download/index.aspx?lang=en-us) 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](https://tensorflow.org/install/gpu) for more details.
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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.
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## Create the tf2 Environment
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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):
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$ conda env create -f environment.yml # or environment-windows.yml on Windows
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$ conda env create -f environment.yml
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Next, activate the new environment:
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$ conda activate tf2
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## Windows
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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.
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$ pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
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> **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.
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## Start Jupyter
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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):
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10
README.md
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README.md
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@ -38,25 +38,19 @@ Read the [Docker instructions](https://github.com/ageron/handson-ml2/tree/master
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### Want to install this project on your own machine?
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Start by installing [Anaconda](https://www.anaconda.com/distribution/) (or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)), [git](https://git-scm.com/downloads), and if you have a TensorFlow-compatible GPU, install the [GPU driver](https://www.nvidia.com/Download/index.aspx).
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Start by installing [Anaconda](https://www.anaconda.com/distribution/) (or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)), [git](https://git-scm.com/downloads), and if you have a TensorFlow-compatible GPU, install the [GPU driver](https://www.nvidia.com/Download/index.aspx), as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details).
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Next, clone this project by opening a terminal and typing the following commands (do not type the first `$` signs on each line, they just indicate that these are terminal commands):
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$ git clone https://github.com/ageron/handson-ml2.git
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$ cd handson-ml2
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If you want to use a GPU, then edit `environment.yml` (or `environment-windows.yml` on Windows) and replace `tensorflow=2.0.0` with `tensorflow-gpu=2.0.0`. Also replace `tensorflow-serving-api==2.0.0` with `tensorflow-serving-api-gpu==2.0.0`.
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Next, run the following commands:
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$ conda env create -f environment.yml # or environment-windows.yml on Windows
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$ conda activate tf2
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$ python -m ipykernel install --user --name=python3
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Then if you're on Windows, run the following command:
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$ pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
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Finally, start Jupyter:
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$ jupyter notebook
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@ -64,4 +58,4 @@ Finally, start Jupyter:
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If you need further instructions, read the [detailed installation instructions](INSTALL.md).
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## Contributors
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I would like to thank everyone who contributed to this project, either by providing useful feedback, filing issues or submitting Pull Requests. Special thanks go to Haesun Park who helped on some of the exercise solutions, and to Steven Bunkley and Ziembla who created the `docker` directory. Thanks as well to github user SuperYorio for helping out on the coding exercise solutions.
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I would like to thank everyone who contributed to this project, either by providing useful feedback, filing issues or submitting Pull Requests. Special thanks go to Haesun Park who helped on some of the exercise solutions, and to Steven Bunkley and Ziembla who created the `docker` directory. Thanks as well to github user SuperYorio for helping out on the coding exercise solutions.
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@ -1,46 +0,0 @@
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name: tf2
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channels:
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- conda-forge
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- defaults
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dependencies:
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- atari_py=0.2 # used only in chapter 18
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- ftfy=5.8 # used only in chapter 16 by the transformers library
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- graphviz # used only in chapter 6 for dot files
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- gym=0.18 # used only in chapter 18
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- ipython=7.20 # a powerful Python shell
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- ipywidgets=7.6 # optionally used only in chapter 12 for tqdm in Jupyter
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- joblib=0.14 # used only in chapter 2 to save/load Scikit-Learn models
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- jupyter=1.0 # to edit and run Jupyter notebooks
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- matplotlib=3.3 # beautiful plots. See tutorial tools_matplotlib.ipynb
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- nbdime=2.1 # optional tool to diff Jupyter notebooks
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- nltk=3.4 # optionally used in chapter 3, exercise 4
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- numexpr=2.7 # used only in the Pandas tutorial for numerical expressions
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- numpy=1.19 # Powerful n-dimensional arrays and numerical computing tools
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- opencv=4.5 # used only in chapter 18 by TF Agents for image preprocessing
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- pandas=1.2 # data analysis and manipulation tool
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- pillow=8.1 # image manipulation library, (used by matplotlib.image.imread)
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- pip # Python's package-management system
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- py-xgboost=0.90 # used only in chapter 7 for optimized Gradient Boosting
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- pyglet=1.5 # used only in chapter 18 to render environments
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- pyopengl=3.1 # used only in chapter 18 to render environments
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- python=3.7 # Python! Not using latest version as some libs lack support
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- python-graphviz # used only in chapter 6 for dot files
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- requests=2.25 # used only in chapter 19 for REST API queries
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- scikit-learn=0.24 # machine learning library
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- scipy=1.6 # scientific/technical computing library
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- tqdm=4.56 # a progress bar library
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- transformers=4.3 # Natural Language Processing lib for TF or PyTorch
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- wheel # built-package format for pip
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- widgetsnbextension=3.5 # interactive HTML widgets for Jupyter notebooks
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- pip:
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- tensorboard-plugin-profile==2.4.0 # profiling plugin for TensorBoard
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- tensorboard==2.4.1 # TensorFlow's visualization toolkit
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- tensorflow-addons==0.12.1 # used only in chapter 16 for a seq2seq impl.
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- tensorflow-datasets==3.0.0 # datasets repository, ready to use
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- tensorflow-hub==0.9.0 # trained ML models repository, ready to use
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- tensorflow-probability==0.12.1 # Optional. Probability/Stats lib.
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- tensorflow-serving-api==2.4.1 # or tensorflow-serving-api-gpu if gpu
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- tensorflow==2.4.1 # Deep Learning library
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- tf-agents==0.7.1 # Reinforcement Learning lib based on TensorFlow
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- tfx==0.27.0 # platform to deploy production ML pipelines
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- urlextract==1.2.0 # optionally used in chapter 3, exercise 4
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@ -20,7 +20,7 @@ dependencies:
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- pandas=1.2 # data analysis and manipulation tool
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- pillow=8.1 # image manipulation library, (used by matplotlib.image.imread)
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- pip # Python's package-management system
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- py-xgboost=1.3 # used only in chapter 7 for optimized Gradient Boosting
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- py-xgboost=0.90 # used only in chapter 7 for optimized Gradient Boosting
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- pyglet=1.5 # used only in chapter 18 to render environments
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- pyopengl=3.1 # used only in chapter 18 to render environments
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- python=3.7 # Python! Not using latest version as some libs lack support
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