Modern Deep Learning Collaboration

Building and sharing experiments quickly is an important requirement when working with a global team consisting of different technical capabilities and expertise.

There are various tools for both running and sharing ML/DL research work. In this post I’ll review the process of creating and sharing a DL model using Corlab & Binder.

Corlab, a tool created by Google that allows spinning off a free Jupiter notebook which can run on a CPU or a GPU. No need for a strong local machine nor a local setup, it just works smoothly with a web browser.

a Jupiter notebook on Corlab running in the browser

Corlab is also linkable from Github so it’s easy to make small changes / iterations directly from the repository.

Once I built my model and ran a few training sessions, I was ready to share the model with a colleague for inference validation. For that, I had two requirements: First I don’t want to share my credentials and second I want my colleague to be able to run the inference on demand.

For that I chose to use Binder, a platform to create a Github repo into a executable Jupiter notebook. Basically it warps the repo code and dependencies into docker container and deploys it, all free and fast.

And a couple of minutes later the inference is ‘production’ ready:

Inference notebook running in Binder’s servers

👨🏻‍💻 Engineering Leader ⛰️ Software Developer ☁️ Cloud Solution Architect

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