pkmital/cadl-gpu repository overview

⁠Creative Applications of Deep Learning w/ Tensorflow⁠

This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy⁠ courses on Creative Applications of Deep Learning w/ Tensorflow⁠. It also contains a python package containing all the code developed during all three courses.

COURSE 1: Creative Applications of Deep Learning with TensorFlow I
Session 1: Introduction to TensorFlow
Session 2: Training A Network W/ TensorFlow
Session 3: Unsupervised And Supervised Learning
Session 4: Visualizing And Hallucinating Representations
Session 5: Generative Models

COURSE 2: Creative Applications of Deep Learning with TensorFlow II
Session 1: Cloud Computing, GPUs, Deploying
Session 2: Mixture Density Networks
Session 3: Modeling Attention with RNNs, DRAW
Session 4: Image-to-Image Translation with GANs

COURSE 3: Creative Applications of Deep Learning with TensorFlow III
Session 1: Modeling Music and Art: Google Brain’s Magenta Lab
Session 2: Modeling Language: Natural Language Processing
Session 3: Autoregressive Image Modeling w/ PixelCNN
Session 4: Modeling Audio w/ Wavenet and NSynth

⁠Github Contents Overview

⁠Installation Preliminaries

• Quickstart Guide⁠
  • Method 1: pip Install⁠
  • Method 2: Docker Installation⁠
• What is Notebook?⁠
• Docker Toolbox⁠
• Jupyter Notebook⁠
  • OSX/Linux⁠
  • Windows/Docker Containers⁠
• Navigating to Notebook⁠
• Installing Python Packages⁠
  • Ubuntu/Linux 64-bit for Python 3.4⁠
  • Ubuntu/Linux 64-bit for Python 3.5⁠
  • OSX for Python 3.4 or Python 3.5⁠
  • Other Linux/OSX varieties⁠
• CUDA/GPU instructions⁠
• Testing it⁠
• CUDA/GPU instructions for MacOS⁠
• Troubleshooting⁠
  • ImportError: No module named 'tensorflow'⁠
  • AttributeError: module 'tensorflow' has no attribute '__version__'⁠
  • GPU-related issues⁠
  • Protobuf library related issues⁠
  • Cannot import name 'descriptor'⁠
  • Can't find setup.py⁠
  • SSLError: SSL_VERIFY_FAILED⁠
  • Jupyter Notebook Kernel is always busy (Windows)⁠
  • Something Else!⁠

The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results.

⁠Quickstart Guide

Please skip this section and read the rest of this readme if you are unfamiliar w/ Jupyter Notebook or installing Python libraries. This section is only for advanced users who want to get started quickly.

There are two ways to get started. You can use a native pip installation or use Docker. There is a quickstart guide for both methods below. If you have trouble with these, then please skip to the more in depth guides below these sections.

⁠Method 1: pip Install

For those of you that are proficient w/ Python programming, you'll need Python 3.4+ and the latest TensorFlow which you can install via pip, e.g.:

$ pip install tensorflow

or w/ CUDA as:

$ pip install tensorflow-gpu

⁠Method 2: Docker Installation

If you want a controlled environment w/ all dependencies installed for you, and are proficient w/ Docker and Jupyter, you can get started w/ this repo like so:

$ cd
$ git clone --recursive https://github.com/pkmital/CADL.git
$ cd CADL
$ docker build -t cadl .
$ docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/session-1:/notebooks --name tf cadl /bin/bash

Note that you can skip the build step and download from docker hub instead like so:

$ docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/session-1:/notebooks --name tf pkmital/cadl /bin/bash

Be sure to replace "session-1" with whichever session you are working on, e.g. "session-2", "session-3"... This will give you a bash prompt with the files for each session:

root@39c4441bcde8:/notebooks# ls
README.md  lecture-1.ipynb  libs  session-1.ipynb  tests

Which you can use to launch jupyter like so:

root@39c4441bcde8:/notebooks# jupyter notebook --allow-root
[I 01:45:27.712 NotebookApp] [nb_conda_kernels] enabled, 2 kernels found
[I 01:45:27.715 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[W 01:45:27.729 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended.
[I 01:45:27.799 NotebookApp] [nb_anacondacloud] enabled
[I 01:45:27.802 NotebookApp] [nb_conda] enabled
[I 01:45:27.856 NotebookApp] ✓ nbpresent HTML export ENABLED
[W 01:45:27.856 NotebookApp] ✗ nbpresent PDF export DISABLED: No module named 'nbbrowserpdf'
[I 01:45:27.858 NotebookApp] Serving notebooks from local directory: /notebooks
[I 01:45:27.858 NotebookApp] 0 active kernels
[I 01:45:27.858 NotebookApp] The Jupyter Notebook is running at: http://[all ip addresses on your system]:8888/?token=dd68eeffd8f227dd789327c981d16b24631866e909bd6469
[I 01:45:27.858 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

Jupyter should then be running if you navigate Google Chrome (suggested!) to "http://localhost:8888⁠". If you navigate to the session-1.ipynb file, you will see the homework, or to "lecture-1.ipynb", to find the lecture transcripts. The same goes for every other session.

If you need to relaunch the docker image again, you can write:

$ cd
$ cd CADL
$ docker start -i tf

If you want to use a GPU version, and have a Linux machine, and have an NVIDIA GPU, you can use nvidia-docker⁠ (this only works for Linux machines! for non-Linux machines that want to use GPU, please follow the expanded directions below, or the quickstart pip installation above):

$ wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.0-rc.3/nvidia-docker_1.0.0.rc.3-1_amd64.deb
$ sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
$ nvidia-docker build -t cadl-gpu -f Dockerfile-gpu .
$ nvidia-docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/session-1:/notebooks --name tf cadl-gpu /bin/bash 
$ nvidia-docker start -i tf

If you had any trouble w/ this setup then please go through the rest of this document which provides much more in depth details.

⁠What is Notebook?

Jupyter Notebook, previously called "iPython Notebook" prior to version 4.0, is a way of interacting with Python code using a web browser. It is a very useful instructional tool that we will be using for all of our homework assignments. Notebooks have the file extensions "ipynb" which are abbreviations of "iPython Notebook". Some websites such as nbviewer.ipython.org⁠ or www.github.com⁠ can view .ipynb files directly as rendered HTML. However, these are not interactive versions of the notebook, meaning, they are not running the python kernel which evaluates/interacts with the code. So the notebook is just a static version of the code contained inside of it.

In order to interact with notebook and start coding, you will need to launch Terminal (for Mac and Linux users). For Windows users, or for anyone having any problems with the Linux/Mac instructions, please follow the next section on Docker Toolbox⁠ very closely! If you are not a Windows user, please first try skipping over the next section and use the installation instructions in Jupyter Notebook⁠ before trying Docker as this solution will be much faster than running Docker.

⁠Docker Toolbox

Currently, Windows users can only install Tensorflow via pip using a 64-bit Python 3.5 environment⁠ or using Docker, as outlined below.

The easiest way to get up an running on any type of system is to use Docker. Docker is a way of managing a "virtual" Linux machine on your computer which will aid the creation a machine capable of running Tensorflow. First, please download and install the Docker Toolbox:

https://www.docker.com/products/docker-toolbox⁠

Linux users can install docker using their favorite package manager.

For OSX and Windows users, you'll then need to run the "Docker Quickstart Terminal" which will launch a Terminal environment running on a virtual Linux machine on your computer. A virtual machine is basically an emulation of another machine. This is important because we'll use this machine to run Linux and install all of the necessary libraries for running Tensorflow.

Note for Windows users, if you have trouble launching the Docker Quickstart Terminal because you have "Hyper-V", please instead try using https://docs.docker.com/docker-for-windows/⁠. Then launch the newly installed "Docker CLI" program.

Once the Terminal is launched, either via Docker CLI or Docker Quickstart Terminal, run the following command (ignoring the $ sign at the beginning of each line, which just denote that each line is a terminal command that you should type out exactly and then hit ENTER afterwards):

$ cd
$ docker-machine ip

If you are using Docker Toolbox, you should see your virtual machine's IP address as a result of the last command. This is the location of your virtual machine. NOTE THIS IP ADDRESS, as we'll need it in a second. If you are using "Docker for Windows" instead, then you won't need this IP as we'll just use "localhost".

This next command will move to your "home" directory. We'll then "clone" the github repo. This will download everything for the course using "git". If you have trouble w/ this step, make sure you have installed git⁠.

$ cd
$ git clone --recursive https://github.com/pkmital/CADL.git

We'll now print out what the full path to that directory is. PLEASE NOTE DOWN THIS DIRECTORY. This is where everything will happen, and I'll explain that in a minute.

$ echo /$(pwd)/CADL

Now run the following command, which will download everything we need to run tensorflow, python, and jupyter notebook (again, ignore the "$" at the beginning of the line only)!

$ docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/CADL:/notebooks --name tf pkmital/cadl

What this is doing is: * Running the docker image pkmital/cadl⁠ * --name is giving it a shorthand name of "tf" * -v is mirroring the directory "/$(pwd)/CADL" to the virtual machine's directory of "/notebooks" * -p is forwarding ports from the virtual machine to your local machine so that you can access the virtual machine's port * -it is running it as an interactive process

You will want to put files inside the "/notebooks" directory only. If you place files on the virtual machine outside of the "/notebooks" directory, which is the SAME as the "CADL" directory on your local machine, they will not be saved. We are using Docker to mirror the "CADL" directory on a virtual machine which has everything necessary for us to code in Python and Tensorflow. Whatever is in that directory will be mirrored on the virtual machine's directory under /notebooks.

You can also try running the docker run command with any other directory. For instance:

$ docker run -it -p 8888:8888 -p 6006:6006 -v /Users/YOURUSERNAME/Desktop:/notebooks --name tf pkmital/cadl

Which would mean that your Desktop is where you can move files around so that on the virtual machine, you can interact with them under the /notebooksdirectory.

For OSX users, if you are installing Docker because you had installation problems using Anaconda and pip, you would instead write the following command (note the missing slash):

$ docker run -it -p 8888:8888 -p 6006:6006 -v $(pwd)/CADL:/notebooks --name tf pkmital/cadl

When you want to start this machine, you will launch the Docker Quickstart Terminal and then write:

$ cd
$ docker start -i tf

Notice that the command prompt will now be # instead of $. You should have a new folder "tensorflow" inside your Home directory. This directory will be empty to begin with. Please make sure you do everything inside this directory only or else any files you make on your virtual machine WILL BE ERASED once it is shutdown! When you clone the CADL repository, or expand the zip file downloads contents inside this directory via your Windows machine (it will be in your Home directory under a folder "cadl"), then you will be able to access it via your Docker instance.

For instance, after running the docker start -i tf command, try going into the directory /notebooks:

# cd /notebooks

⁠Jupyter Notebook

⁠OSX/Linux

Note: Windows/Docker users should scroll past this section to "Windows/Docker"⁠. For OSX/Linux users, the easiest way to ensure you have Python 3.4 or higher and Jupter Notebook is to install Anaconda for Python 3.5 located here:

OSX⁠ or Linux⁠

Make sure you restart your Terminal after you install Anaconda as there are some PATH variables that have to be set.

Then run the following:

$ curl https://bootstrap.pypa.io/ez_setup.py -o - | python

If you already have conda, but only have Python 2, you can very easily add a new environment w/ Python 3⁠ and switch back and forth as needed. Or if you do not have Anaconda, but have a system based install, I'd really recommend either using Anaconda or pyenv⁠ to help you manage both python installations.

With Anaconda installed, you will have python and the package "ipython[notebook]", along with a ton of other very useful packages such as numpy, matplotlib, scikit-learn, scikit-image, and many others.

With everything installed, restart your Terminal application (on OSX, you can use Spotlight to find the Terminal application), and then navigate to the directory containing the "ipynb", or "iPython Notebook" file, by "cd'ing" (pronounced, see-dee-ing), into that directory. This involves typing the command: "cd some_directory". Once inside the directory of the notebook file, you will then type: "jupyter notebook". If this command does not work, it means you do not have notebook installed! Try installed anaconda as above, restart your Terminal application, or manually install notebook like so (ignore the "$" signs which just denote that this is a Terminal command that you should type out exactly and then hit ENTER!):

$ pip3 install ipython[notebook]
$ jupyter notebook

If you run into issues that say something such as:

[W 20:37:40.543 NotebookApp] Kernel not found: None

Then please try first running:

$ ipython3 kernel install

For users running firewalls, you must make sure you have an exception as per Jupyter Notebooks Firewall Instructions⁠ otherwise you may not be able to interact with the notebook. Namely, you will need to allow connections from 127.0.0.1 (localhost) on ports from 49152 to 65535. Once inside your Docker container as outlined above, you can now launch notebook like so:

$ cd /notebooks
$ jupyter notebook &

Note on Virtual versus Windows Directories:

This is tricky to grasp, mostly because I didn't explain it. Docker is "virtual" computer running inside your computer. It has its own filesystem and its own directories. So you can't reference your Windows machine's directories inside this machine. When you first ran docker (e.g. $ docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/tensorflow:/notebooks --name tf pkmital/cadl) it included as part of its command: -v /$(pwd)/tensorflow:/notebooks. What that was doing is "mirroring" a directory on your Windows machine inside your Virtual machine. So whatever was in your Windows machine under the directory /$(pwd)/tensorflow would appear in the Virtual machine under /notebooks. That Windows directory is likely /Users/<YOURUSERNAME>/tensorflow. So ONLY inside that directory, create it if it doesn't exist, should you put files in order to access it on the Virtual machine.

So let's say your Username was "pkmital". Then your home directory would be /Users/pkmital, and you would have mirrored /Users/pkmital/tensorflow on your Windows Machine to the Virtual machine under /notebook. Now let's say I create a directory /Users/pkmital/tensorflow/images on my Windows Machine, and then put a bunch of png files in there. I will then see them in my Virtual machine under /notebook/images. If I put the CADL repository inside /Users/pkmital/tensorflow, then I should have /Users/pkmital/tensorflow/CADL/session-1/session-1.ipynb and on the Virtual machine, it will be in /notebooks/CADL/session-1/session-1.ipynb - From this notebook, running on the virtual machine, accessed with Jupyter Notebook, I would access my images like so:

import os
os.listdir('../../images')

⁠Navigating to Notebook

After running "jupyter notebook &", you should see a message similar to:

root@182bd64f27d2:~# jupyter notebook &
[I 21:15:33.647 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[W 21:15:33.712 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended.
[W 21:15:33.713 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using authentication. This is highly insecure and not recommended.
[I 21:15:33.720 NotebookApp] Serving notebooks from local directory: /root
[I 21:15:33.721 NotebookApp] 0 active kernels
[I 21:15:33.721 NotebookApp] The IPython Notebook is running at: http://[all ip addresses on your system]:8888/
[I 21:15:33.721 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

Don't worry if the IP address or command prompt look different. Note where it says: The IPython Notebook is running at. If you are running Docker (Windows users), this is where we need that IP address. For OSX/Linux users, we'll use "localhost" so don't worry about this. Now open up Chrome/Safari/Firefox whatever browser you like, and then navigate to:

http://localhost:8888⁠

or for Windows users:

http://ADDRESS:8888⁠

where ADDRESS is the ip address you should have noted down before. For instance, on my machine, I would visit the website:

http://192.168.99.100:8888⁠

This will launch the Jupyter Notebook where you will be able to interact with the homework assignments!

⁠Installing Python Packages

Packages are libraries or useful extensions to the standard python libraries. In this course, we'll be using a few including Tensorflow, NumPy, MatPlotLib, SciPy, SciKit-Image, and SciKit-Learn. Windows users will already have these libraries since the Docker container includes these. However, if you needed to, you can install these using "pip", which is the python package manager. OSX/Linux users should follow these steps just to be sure they have the latest versions of these packages. In Python 3.4 and higher, pip comes with any standard python installation. In order to use pip, first make sure you are using the correct version. One way to do this is check which pip you are running:

$ which pip
$ which pip3

Use which pip points to the install path that makes the most sense (e.g. Anaconda for OSX users for some reason does not symlink pip3 to the python3 pip, and instead points to the system version of python3).

Then you'll write:

$ pip3 install -U pip setuptools

To make sure you have an up to date pip, then:

$ pip3 install some_package

To get the necessary libraries:

$ pip3 install "scikit-image>=0.11.3" "numpy>=1.11.0" "matplotlib>=1.5.1" "scikit-learn>=0.17"

This should get you all of the libraries we need for the course, EXCEPT for tensorflow. Tensorflow is a special case, but can be pip installed in mu