Abhimanyu Aryan | March 11, 2019 · 2 min read
Edit on GithubHowdy guys, I’m writing this blog post for people who own not so good enough laptops equiped with GPUs, over that a poor internet connection & still want to learn ML from fast.ai
Now sadly some of us feel that setting up development environment is complete waste of time and energy. If you are one of those people, this blog post will make a lot of sense for you.
Kaggle is an amazing platform if you want to start really quick. You don’t have to download datasets. Downloading datasets on any kind of platform is complete waste of resources and bandwidth. Datasets size range from some GBs-TBs, I believe you definitely won’t like spending time on that
!pip install fastai==0.7.0
The above command will install fast.ai library for you
A sample Kaggle Jupyter Notebook for predicting NYC Taxi Fair prices: https://www.kaggle.com/abhimanyuaryan/new-york-city-taxi-fare-prediction/
By default you datasets gets added to input directory
PATH="../input/"
df_raw = pd.read_csv(f'{PATH}train.csv', nrows = 50_000_000)
~Happy Hacking~
curl link_to_dataset
as shown in video by Jeremy: https://youtu.be/CzdWqFTmn0Y?t=969
First you need to download Kaggle API key. It’s a json file
Once you have the API key. Go to
from google.colab import files
files.upload()
# Install the Kaggle API Client
!pip install -q kaggle
Open complete notebook in colab here. Click on this button:
Jeremy has himself made an AWS starter video: https://course.fast.ai/lessons/aws.html. But AWS will cost you around $0.9/hr for p2 instance. See for yourself if you can afford it or not