Deep Learning using TensorFlow at Datagiri
Code, Slides and other material for the talk on Deep Learning using TensorFlow at Datagiri meetup (Feb, 2017)
How to Set Up
- Install Anaconda
- Install necessary packages
Notes
- In the following steps, we install a cpu-specific version of
tensorflow
, which is good enough for the session, but not for most real-world tasks. - The instructions have been tested only on Ubuntu and OS X (we didn't have a windows system available for testing; please raise a issue if you hit any snags on your windows system).
Install Anaconda
(Updated from Udacity's instructions)
Per the Anaconda docs:
Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.
Overview
Using Anaconda consists of the following:
- Install
miniconda
on your computer - Create a new
conda
environment - Each time you wish to work, activate your
conda
environment
Installation
Download the version of miniconda
that matches your system. Make sure you download the version for Python 3.x (3.6 is the latest at the time of writing).
Linux | Mac | Windows | |
---|---|---|---|
64-bit | 64-bit (bash installer) | 64-bit (bash installer) | 64-bit (exe installer) |
32-bit | 32-bit (bash installer) | 32-bit (exe installer) |
Install miniconda on your machine. Detailed instructions:
- Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
- Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
- Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install
Install necessary packages
Setup your the tensorflow
environment.
git clone https://github.com/soumendra/lecture_datagiri_deeplearning_tensorflow_Feb17.git
cd lecture_datagiri_deeplearning_tensorflow_Feb17
If you are on Windows, rename
meta_windows_patch.yml
to
meta.yml
Create tensorflow
. Running this command will create a new conda
environment that is provisioned with all libraries you need to run the notebooks.
conda env create -f environment.yml
Verify that the tensorflow
environment was created in your environments:
conda info --envs
Cleanup downloaded libraries (remove tarballs, zip files, etc):
conda clean -tp
Uninstalling
To uninstall the environment:
conda env remove -n tensorflow
Using the Anaconda environment
Now that you have created an environment, in order to use it, you will need to activate the environment. This must be done each time you begin a new working session, i.e., open a new terminal window.
Activate the tensorflow
environment:
OS X and Linux
$ source activate tensorflow
Windows
Depending on shell either:
$ source activate tensorflow
or
$ activate tensorflow
That's it. Now all of the tensorflow
libraries are available to you. You can start a Jupyter Notebook with:
jupyter notebook
To exit the environment when you have completed your work session, simply close the terminal window.