Setup and installation of 'AI Agents using CrewAI Studio & Jupyter with GPU support' on Azure

This section describes how to launch and connect to AI Agents using CrewAI Studio & Jupyter with GPU support’ VM solution on Azure Platform.

1. Open AI Agents using CrewAI Studio & Jupyter with GPU support VM listing on Azure Marketplace.

2. Click on Get It Now

• Login with your credentials, provide the details here. Once done click on Continue.

• It will take you to the Product details page. Click on Create.

• Select a Resource group for your virtual machine

• Select a Region where you want to launch the VM(such as East US)

• Note: If you see “This image is not compatible with selected security type. To keep trusted launch virtual machines, select a compatible image. Otherwise change your security type back to Standard” error message below the Image name as shown in the screenshot below then please change the Security type to Standard.

• Optionally change the number of cores and amount of memory.

Minimum VM Specs : 8GB vRAM / 2vCPU

Please note that the VM can also be deployed using NVIDIA GPU instance to execute crewai agents faster. Please check Publisher recommendations instance type for GPU (Standard_NC4as_T4_v3 - 4 vcpus, 28 GiB memory) or check the available NVIDIA GPU instances on Azure documentation page.

Select the Authentication type as Password and enter Username as ubuntu and Password of your choice.

• Optionally change the OS disk size and its type. By default the VM comes with 50GB of disk.

• Optionally change the network and subnetwork names. Be sure that whichever network you specify has ports 22 (for ssh), 3389 (for RDP) and 443 (for HTTPS) exposed.

The VM comes with the preconfigured NSG rules. You can check them by clicking on Create New option available under the security group option.

• 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.
  Virtual Machine will begin deploying.

3. A summary page displays when the virtual machine is successfully created. Click on Go to resource link to go to the resource page. It will open an overview page of virtual machine.

4. If you want to update your password then open up 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 SSH to the VM. To do so, first note the public IP address of the VM from VM details page as highlighted below

Open putty, paste the IP address and click on Open.

login as ubuntu and provide the password for ‘ubuntu’ user.

5. You can also connect to the VM’s desktop environment from any local windows machine using RDP protocol or local linux machine using Remmina.

6. To connect using RDP via Windows Machine, first note the public IP address of the VM from VM details page as highlighted below

7. Then From your 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

8. This will connect you to the VM’s desktop environment. Provide the username (e.g “ubuntu”) and the password set in the step4 to authenticate. Click OK

9. Now you are connected to the out of box “AI Agents using CrewAI Studio & Jupyter with GPU support” VM’s desktop environment via Windows Machine.

10. 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.

11. In the “Remmina Remote Desktop Client” wizard, select the RDP option from dropdown and paste the external ip and click enter.

12. 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

13. Now you are connected to out of box “AI Agents using CrewAI Studio & Jupyter with GPU support” VM’s desktop environment via Linux machine.

14. To access the JupyterHub Web Interface, copy the public IP address of the VM and paste it in the browser as https://public_ip_of_vm.

Browser will display a SSL certificate warning message. Accept the certificate warning and Continue.

15. Provide the ‘ubuntu’ user and its password set during VM creation. ubuntu is configured as an admin user here.

16. Now you are loggedin to jupyterhub. Here you can see we have setup folder configured with jupyter-venv, jupyterhub_config.py files along with other jupyterhub configuration files. You can use jupyter notebook to run and test your CrewAI projects.

17. To access CrewAI Studio, copy the public IP address of the VM and paste it in the browser as https://public_ip_of_vm/crewai-studio . Here you can create new tools, agents, crews and kickoff the crews to get the results.

18. If you deployed the VM using GPU instance then you can monitor GPU consumption by running below command in terminal. The command will automatically update nvidia-smi output every 1 seconds.

watch -n 1 nvidia-smi

To get started with CrewAI Platform please visit CrewAI Quickstart Guide

To run your Crews using GUI please visit CrewAI Studio Quickstart

To Learn more about JupyterHub, multiuser environment and installing new python packages on this VM visit JupterHub Guide

For more details, please visit Official Documentation page