Joining phenomenal deep learning course fast.ai using free Google Colaboratory setup.

My colleague Ilia Lebedev @melevir at twitter recommended me a cool deep learning course at http://course.fast.ai.

Much appreciation to Jeremy and Rachel who gave us this opportunity to learn. They position themself as a course for software developers, not data scientists. This course is of January 2018, is the second version.

Listen that the course said for itself.

YOU WILL LEARN HOW TO:

• New Set up your own GPU server in the cloud
• Use the fastai and Pytorch libraries in python to train and run deep learning models
• Build, debug, and visualize a state of the art convolutional neural network (CNN) for recognizing images
• Build state of the art recommendation systems using neural-network based collaborative filtering
• Build state of the art time series and structured data models using categorical embeddings
• Get great results even from small datasets, by using transfer learning
• Understand the components of a neural network, including activation functions, dense and convolutional layers, and optimizers
• Build, debug, and visualize a recurrent neural network (RNN) for natural language processing (NLP), including developing a sentiment classifier which beat all previous academic benchmarks.
• Recognize and deal with over-fitting, by using data augmentation, dropout, batch normalization, and similar techniques

Back to a setup for fast.ai course using free Google Colaboratory.

In this post, I will show how to use organize setup of Google Colaboratory for the fast.ai deep learning course. You should repeat this simple steps every time you connect to a new GPU.

In order to train a neural network, we will most certainly need Graphics Processing Unit (GPU)  – and not everyone have a fast one. Without a decent GPU a singe step will last for hours instead of minutes. Sign-up to Google Colaboratory to get a hosted Jupyter notebook environment connected with a free Tesla K80 GPU.

You can use GPU as a backend free for 12 hours at a time. That is very good news for us!

The steps to repeat every time you connect to a new GPU.

Setup step 0: Select Free GPU

It is so simple to alter default hardware (CPU to GPU or vice versa); just follow Edit > Notebook settings or Runtime>Change runtime type and select GPU as Hardware accelerator.

Setup step 1: install libraries for fast.ai course.

Enter this code in a new code block on top of a notebook:

# Install torch compatible with fastai
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())
accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'
!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.1-{platform}-linux_x86_64.whl fastai torchvision

This will take a while.

Setup step 2: model weights download.

# Model weights for other network architectures (e.g. resnext50):
!wget -q http://files.fast.ai/models/weights.tgz && tar -xzf weights.tgz -C /usr/local/lib/python3.6/dist-packages/fastai

The step 2 will take a while too, need to download and unpack 1.1 Gb.

Setup step 3: dataset download.

For lesson 1, 2, 3 you need dogs $ cats dataset. This code does it. The dataset dogs & cats is available at http://files.fast.ai/data/dogscats.zip.

!mkdir -p data
!wget -q http://files.fast.ai/data/dogscats.zip
!unzip -q dogscats.zip -d data/

Some lessons as 2nd use Kaggle datasets, but it is a theme for another article.

Summary of all my setup steps you can copy from my Github Gist Fast.ai install script.py.

I will try to keep it updated as long as I have interest in fast.ai and Google Colaboratory.