How to get AI/ML development, training & inference using Python & Jupyter Kit on AZURE
This section describes how to launch and connect to AI/ML development, training & inference using Python & Jupyter Kit in Azure.
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 instance type NC4asT4v3 from the Size section available on configuration page as shown in below screenshot.
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 Azure Cloud Platform
Select a Region where you want to launch the VM(such as East US)
Optionally change the number of cores and amount of memory. (This defaults to 2 vCPUs and 8 GB ram.)
Select the Authentication type as Password and enter Username as ubuntu and Password of your choice for the VM
Optionally change the OS disk type.
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) are exposed.
Optionally go to the Management, Advanced and Tags tabs for any advance settings you want for the VM.
Click on Review + create and then click on Create when you are done. MUJEFA will begin deploying.
A summary page displays when the virtual machine is successfully created. Click on Go to resource link to go to the resource page.
In the left navigation pane, select Run command, select RunShellScript and enter following command to change the password of the vm.
sudo echo ubuntu:yourpassword | chpasswd
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 local linux machine using Remmina.
To connect using RDP via Windows Machine, first note the public IP address 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 public IP address and click connect
This will connect you to the VM’s desktop environment. Provide the username (e.g “ubuntu”) and the password set in the above “Reset password” step to authenticate. Click OK
Now you are connected to the out of box MUJEFA environment.
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 above “Reset Password” step 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 ubuntu user password.
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.
