Setting up your own Cloud-GPU Server, Jupyter and Anaconda — Easy and complete walkthrough
Note: One of the important tips for lab environments is to set an auto-shutdown timer, below is one such setting in GCP

I have been working on a few hosted environments which include AWS Sagemaker Notebook instances, Google Cloud Colab, Gradient (Paperspace) etc and all of them are really good and needed monthly subscriptions, I decided to have my own GPU server instance which can be personalized and I get charged on a granular basis.
Installing it is not easy, first, you need to find a cloud-computing instance which has GPU support enabled, AWS and GCP are straightforward in this section as the selection is really easy.
Let’s break this into 3 stages
- Selecting a GPU server-based instance for ML practice.
- Installing Jupyter Server — Pain-Point Making it accessible from the internet.
- Installing Package managers like Anaconda — Pain-Point having Kernel of conda reflect in Jupyter lab.
Stage-1
For a change, I will be using GCP in this case from my usual choice of AWS here.

Choose GPU alongside the Instance
Generic Guidelines — https://cloud.google.com/deep-learning-vm/docs/cloud-marketplace
rakesh@instance-1:~$ sudo apt install jupyter-notebook # Step1: generate the file by typing this line in console jupyter notebook Continue reading



