I saw that Cloud9 is popular and has good documentation. I prefer the Continuum Analytics Anaconda Python distribution for its Conda package manager. The notebook serves as a top-level script, the parts of which I eventually modularize as components of installable packages. IPython (Jupyter) Notebooks are an important part of my development process since they enable me to prototype quickly and to share my work in-progress. SSH within the browser is very convenient. 2īetween Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. Connecting other cloud services expands the possibilities. On a cloud platform, I can mount disks with large data sets and change the instance size to test how efficiently algorithms use CPUs and memory. I wanted to test the scalability of a pipeline.For more complex platform managment, there's the Google Cloud Shell.

Snapshots can serve as simple backups of instances. I spend $10 per month on a Cloud9 micro plan for SSH workspaces.I spend about $20 per month on Google Cloud services.I spent $170 on my ASUS C201 Chromebook with 4GB RAM.

With a cloud-based platform, I only need a laptop as a web browser.

I use the Cloud9 IDE and connect the instance as an SSH workspace.

$ ssh -f -N -L localhost:8888:0.0.0.0:8888 Chrome Secure Shell, omit -f to keep the tunnel open (see screenshot).

Create an SSH tunnel to forward a local port to the server's port on the instance:.

$ disown 1234 (where 1234 is the process ID)

Start a Jupyter Notebook server on the instance:.

Start a Google Compute Engine virtual machine instance.

Chrome ipynb viewer