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Toulouse Data Science #38 - Google Colab & AI Notebook Demo
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Google Colab & AI Notebook
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Google Colaboratory

& AI Notebook

Data Science in the cloud, the easy way

Toulouse Data Science #38 - June 18th 2019



  • ML Engineer @ Airbus Defence and Space (Space Systems)

  • Training Neural Networks on remote sensing imagery since 2016

    • Delair, Magellium & Airbus Intelligence (w/ Jeff, spoilers), Airbus DS...

    • torch, tf, keras, pytorch, ...

    • a lot of time spent installing instances

  • Contact: @foxchouteau or on Slack


Who started learning data science recently ?


Who works in data science ?


Who teaches data science classes ?



  • easy access to configured development environment for ML

  • from Google but not limited to their tech

  • jupyter-based products

  • one free, one paid: different use cases, similar principles



This talk is not sponsored by Google ;)

There may be better alternatives: Feel free to comment after :)




WTF is... Google Colab ?

  • Jupyter Notebook + Google Drive

  • Full python data science environment

  • 12h max session lifetime


Is it for YOU ?

  • Students, people learning ML/DS

  • Teachers, share courses, get assignments

  • Quick experiments / sharing


Nice features

  • Can use your data: gdrive, gsheet, local filesystem

  • Jupyter-based: All the power of interactive & visualisations

  • You can apt-get and pip install what you need


Nicer features

  • GPU ! (Nvidia Tesla T4, 16 GB GPU RAM = 3000$)

  • Collaboration ! (share and co-edit notebooks)

  • Open notebook from github to colab !


Demo Time



  • Long calculations w/ guarantees (you can checkpoint your models on colab though)

  • Code syncing / huge codebase & huge datasets

  • Full control over installation and data

GCP Deep Learning VM / AI Platform Notebook


Google Cloud Platform

  • Cloud Provider, very nice VM instances options

  • 300$ free, paid for GPU and unlocked bandwidth

  • Rather easy to use for ML / DS


WTF is... AI Platform Notebook ?

  • Pre configured paid Cloud Virtual Machines (Google Compute Engine)

  • With jupyter lab auto launched & ready

  • Papermill pre installed for scheduling


Available configurations


2 different workflows

  1. Jupyter only ("AI Notebook")

  2. Pre-configured instance for Data Science ("Deep Learning VM")


Demo 1: "AI Platform Notebook"

  • Creating an instance

  • Connecting to jupyter lab (with or without ssh !)


Demo 2: "Deep Learning VM"


execute_notebook -i "${INPUT_NOTEBOOK}" \
                 -o "${GCP_BUCKET}" \
                 -f "${IMAGE_FAMILY_NAME}" \
                 -t "${INSTANCE_TYPE}" \
                 -z "${ZONE}" \
                 -g "${GPU_TYPE}" \
                 -c "${GPU_COUNT}"


Advanced Usage (not covered here)

  • Extensive tutorial

  • Use "preemptible" (spot in AWS terminology)*

  • CLI creation for more customization

*5x less expensive, run only 24h



TL;DR (bis)

Google Colab Google AI Notebook
Learn, experiment Can scale compute
Single notebook / Clone from github Upload own code
Simple jupyter env. Full jupyter lab or SSH access
Data from anywhere / google drive Fully owned cloud environment
Short runtimes Cheap 1d runtimes or arbitrary runtimes
**free** **[paid](** (by minute of computing + storage)



  • Kaggle Kernels: for kaggle, colab, free, 9h, P100

  • Amazon Sagemaker: can someone tell me about it ?

  • A lot of smaller entities... floydhub...

  • Build your own machine ? opinion: last step for individual use (be sure of what you need !)


Thank you !

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