Getting Setup with Fast.ai for Machine Learning (No GPU Required)

Howdy! This post is for people who own laptops without good GPU specs, have a poor internet connection, and still want to learn ML from fast.ai.

Setting up a dev environment can feel like a waste of time. If you’re one of those people, this post should help.

Free Options

Kaggle
Google Colab + GitHub

Paid Options

AWS

Kaggle

Kaggle is amazing if you want to start quickly — no downloading datasets. Datasets range from GBs to TBs, so not having to download them locally is a huge win.

To use the fast.ai library, run in a Kaggle notebook:

!pip install fastai==0.7.0

📓 Sample Kaggle Notebook - NYC Taxi Fare Prediction

By default your dataset gets added to the input directory:

PATH = "../input/"
df_raw = pd.read_csv(f'{PATH}train.csv', nrows=50_000_000)

Google Colab

Google Colab provides a free GPU. Here’s how to use it with GitHub:

Create a .ipynb notebook locally
Push it to GitHub
Go to colab.research.google.com and load your repo

Ways to download datasets in Colab:

Curl:

curl <link_to_dataset>

(as shown in Jeremy’s video)

Kaggle API:

# Step 1: Upload Kaggle API key
from google.colab import files
files.upload()

# Step 2: Install Kaggle API client
!pip install -q kaggle

AWS (Last Resort)

Jeremy has an AWS starter video. AWS p2 instances cost around $0.9/hr — decide for yourself!