This repository contains homework assignments for the Kadenze Academy course on Creative Applications of Deep Learning w/ Tensorflow.
| Session | Description | |
|---|---|---|
| Installation | Installation | Setting up Python/Notebook and necessary Libraries. |
| Preliminaries | Preliminaries with Python | Basics of working with Python and images. |
| 1 | Creating a Dataset/Computing with Tensorflow | Working with a small dataset of images. Dataset preprocessing. Tensorflow basics. Sorting/organizing a dataset. |
| 2 | TBA | TBA. |
| 3 | TBA | TBA. |
| 4 | TBA | TBA. |
| 5 | TBA | TBA. |
- What is Notebook?
- Docker Toolbox
- Jupyter Notebook
- Navigating to Notebook
- Installing Python Packages
- CUDA/GPU instructions
- Testing it
We will be using 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.
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.
Unforunately, at the time of this writing (July 2016), there are no binaries for Tensorflow available for Windows users. The easiest way to get up an running 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
With this installed, 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. Once the terminal is launched, 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 ipYou 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. Now run the following command, which will download about ~530 MB containing 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)/tensorflow:/notebooks --name tf pkmital/tf.0.9.0-py.3.4For OSX users, if you are installing Docker because you had installation problems using Anaconda and pip, you would instead write the following command:
$ docker run -it -p 8888:8888 -p 6006:6006 -v $(pwd)/Desktop/tensorflow:/notebooks --name tf pkmital/tf.0.9.0-py.3.4This command will download everything you need to run Tensorflow on your virtual machine.
When you want to start this machine, you will launch the Docker Quickstart Terminal and then write:
$ cd
$ docker start -i tfYou should have a new folder "tensorflow" inside your Home directory. 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!
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:
https://www.continuum.io/downloads
This package will install both 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 notebookIf 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 installFor Windows users making use of Docker, or for OSX users that had trouble w/ the pip/Anaconda install, once inside your Docker container as outlined above, you can launch notebook like so:
$ cd /notebooks
$ jupyter 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:
or for Windows users:
where ADDRESS is the ip address you should have noted down before. For instance, on my machine, I would visit the website:
This will launch the Jupyter Notebook where you will be able to interact with the homework assignments!
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, you'll write:
$ pip3 install some_packageTo 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 much the same way by pointing pip to the github repo corresponding to your OS like so.
$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.9.0-py3-none-any.whlYou can pip install Tensorflow for most OSX/Linux setups including those that are making use of NVIDIA GPUs and CUDA using one the packages listed on this link: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md#pip-installation
If you are having trouble with pip installation, try looking here first: Common Installation Problems. Failing that, reach out to us on the forums, or else you may want to instead run a Docker instance as outlined in the Windows instructions above: Setting up a Docker Container.
Note that I have not provided instructions on getting setup w/ CUDA as it is beyond the scope of this course! If you are interested in using GPU acceleration, I highly recommend using Ubuntu Linux and setting up a machine on Nimbix or Amazon EC2 using the instructions here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md#optional-install-cuda-gpus-on-linux. If you're using Nimbix, you can skip the install process as there is already a machine pre-installed w/ Tensorflow. Similarly, for Amazon EC2, there are many existing "images" of machines that have Tensorflow already installed.
To confirm it worked, try running:
$ python3 -c 'import tensorflow as tf; print(tf.__version__)'You should see 0.9.0 be printed.