How to get AI/ML development, training & inference using Python & Jupyter Kit on GCP (Google Cloud Platform)
This section describes how to launch and connect to AI/ML development, training & inference using Python & Jupyter Kit in a Google Compute environment using the available Cloud Launcher offering.
Note: Now the AI/ML development, training & inference using Python & Jupyter kit is available with Jupyter AI and GPU Acceleration support. To provision this VM with the GPU instance, please select the GPU tab available on configuration page (Please see below configuration screenshot in step2). It will show 1 Nvidia T4 GPU selected. Now choose the instance type. Default instance is n1-standard2(2vCPU, 7.5GB memory).
Note: GPU availability is limited to certail zones.
NOTE: While deploying the instance on GPU, if you encounter the quota exhaust error or you are unable to deploy the instance on GPU VM then please refer to our Request Quota on Google Cloud Platform
It will take you to the agreement page. On this page, you can change the project from the project selector on top navigator bar as shown in the below screenshot.
Accept the Terms and agreements by ticking the checkbox and clicking on the AGREE button.
It will show you the successfully agreed popup page. Click on Deploy.
On deployment page, give a name to your deployment.
In Deployment Service Account section, click on Existing radio button and Choose a service account from the Select a Service Account dropdown.
If you don't see any service account in dropdown, then change the radio button to New Account and create the new service account here.
If after selecting New Account option, you get below permission error message then please reach out to your GCP admin to create service account by following Step by step guide to create GCP Service Account and then refresh this deployment page once the service account is created, it should be available in the dropdown.
You are missing resourcemanager.projects.setIamPolicy permission, which is needed to set the required roles on the created Service Account
Select a zone where you want to launch the VM(such as us-east1-)
Optionally change the number of cores and amount of memory. (This defaults to 2 vCPUs and 7.5 GB ram.)
Optionally change the boot disk type and size. (This defaults to “Standard Persistent Disk” and 35 GB respectively)
Optionally change the network name and subnetwork names. Be sure that whichever network you specify has ports 22 (for ssh), 3389 (for RDP) and 80 (for HTTP) exposed.
Click Deploy when you are done. Python AI & Machine learning Suit will begin deploying.
A summary page displays when the compute engine is successfully deployed. Click on the Instance link to go to the instance page .
On the instance page, click on the “SSH” button, select “Open in browser window”.
This will open SSH window in a browser.
Run below command to set the password for “ubuntu” user
sudo passwd ubuntu
Now the password for ubuntu user is set, you can connect to the VM’s desktop environment from any local windows machine using RDP protocol or linux machine using Remmina.
To connect using RDP via Windows Machine, first note the external IP of the VM from VM details page as highlighted below
Then From you local windows machine, goto “start” menu, in the search box type and select “Remote desktop connection”
In the “Remote Desktop connection” wizard, copy the external ip and click connect
This will connect you to the VM’s desktop environment. Provide “ubuntu” as the userid and the password set in step 6 to authenticate. Click OK
Now you are connected to the out of box MUJEFA environment via Windows Machine..
To connect using RDP via Linux machine, first note the external IP of the VM from VM details page,then from your local Linux machine, goto menu, in the search box type and select “Remmina”.
Note: If you don’t have Remmina installed on your Linux machine, first Install Remmina as per your linux distribution.
In the “Remmina Remote Desktop Client” wizard, select the RDP option from dropdown and paste the external ip and click enter.
This will connect you to the VM’s desktop environment. Provide “ubuntu” as the userid and the password set in step 6 to authenticate. Click OK
Now you are connected to the out of box MUJEFA environment via Linux machine.
You can use the remote desktop you connected in above step for using the VM, however, more convenient and better method is to use the Jupyter/Ipython notebook which comes with the VM .
The Notebook is available on the same public IP you used for remote desktop and accessible via any browser. Just open the browser and type the public IP address and you will get below screen for login.
The Jupyter Notebook is configured with the ubuntu as an admin user. Login with ubuntu as username and use a ubuntu user password set in the above step 6.
Note: Make sure you use “http” and not “https” in the url
After Login , you will see below screen. This is JupyterLab. You will find various options here. You can Access Jupyter Notebook, Console, Files and Folders, etc.
This VM comes with the JuputerAI plugin installed out of the box which allow you to access Chat UI in the JupyterLab. This can be used for AI-conversation with lots of LLMs via APIs. It has support for chatgpt and lots of other LLMs.
To configure your Jupyternaut(ChatUI official name), You need to select a language model and an embedding model to chat. Once you have made your selections, the UI may display text boxes for one or more settings keys. So keep your keys ready with you. click on Start Here.
Select a Language Model and Embedding model from the dropdown. If your selected model needs API keys, provide the API Keys. You need to get the API keys from the selected model providers.
For example, for OpenAI provider you can get the API keys as explained here.
Select the Input Type.
Click on Save Changes. Once settings are saved successfully, click the “back” (left arrow) button in the upper-left corner of the Jupyter AI side panel. The chat interface now appears.
Now your personal assistance is ready. Seek code suggestions, debugging tips, or even have code snippets generated for you by interacting with the chat UI.
This VM also comes with the magics package jupyter_ai_magics installed out of the box. These magic commands %%ai and %ai turns your Jupyter into a generative AI playground anywhere the IPython kernel runs.
Before you send your first prompt to an AI model, load the IPython extension by running the following code in a notebook cell or IPython shell: -
%load_ext jupyter_ai_magics
To use these magic commands, open Jupyter Notebook. Run %ai help for help with syntax.
%ai help
The %%ai cell magic allows you to invoke a language model of your choice with a given prompt.The model is identified with a global model ID, which is a string with the syntax provider-id:local-model-id, where provider-id is the ID of the provider and local-model-id is the ID of the model scoped to that provider Set your model API keys using environment variable and your model of choice as shown below. Reload the IPython extension.
%env OPENAI_API_KEY=Your API Key
%%ai <provider-id>:<local-model-id>
%reload_ext jupyter_ai_magics
Now you can invoke a model as follows. The prompt begins on the second line of the cell.
%%ai model
Your prompt here
Jupyter AI also includes multiple subcommands, which may be invoked via the %ai line magic.
The %ai list subcommand prints a list of available providers and models.
