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Deep Learning with PaddlePaddle

Build Status Documentation Status Documentation Status

  1. Fit a Line
  2. Recognize Digits
  3. Image Classification
  4. Word to Vector
  5. Recommender System
  6. Understand Sentiment
  7. Label Semantic Roles
  8. Machine Translation

Running the Book

This book you are reading is interactive -- each chapter can run as a Jupyter Notebook.

We packed this book, Jupyter, PaddlePaddle, and all dependencies into a Docker image. So you don't need to install anything except Docker. If you are using Windows, please follow this installation guide. If you are running Mac, please follow this. For various Linux distros, please refer to https://www.docker.com. If you are using Windows or Mac, you might want to give Docker more memory and CPUs/cores.

Just type

docker run -d -p 8888:8888 paddlepaddle/book

This command will download the pre-built Docker image from DockerHub.com and run it in a container. Please direct your Web browser to http://localhost:8888 to read the book.

If you are living in somewhere slow to access DockerHub.com, you might try our mirror server hub.baidubce.com:

docker run -d -p 8888:8888 hub.baidubce.com/paddlepaddle/book

Training with GPU

By default we are using CPU for training, if you want to train with GPU, the steps are a little different.

To make sure GPU can be successfully used from inside container, please install nvidia-docker. Then run:

nvidia-docker run -d -p 8888:8888 paddlepaddle/book:latest-gpu

Or you can use the image registry mirror in China:

nvidia-docker run -d -p 8888:8888 hub.baidubce.com/paddlepaddle/book:latest-gpu

Change the code in the chapter that you are reading from

use_cuda = False

to:

use_cuda = True

Contribute

Your contribution is welcome! Please feel free to file Pull Requests to add your chapter as a directory under /pending. Once it is going stable, the community would like to move it to /.

To write, run, and debug your chapters, you will need Python 2.x, Go >1.5. You can build the Docker image using this script. This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.