Select the resource group you wish to deploy this to, Resource groups are simply a grouping for resources so typically we suggest creating a resource group per your course. So all services related to that course are within a single resource group.
You now need to complete information about the VM you wish to deploy
Compete the fields
The next option is simply selecting the VM instance you require so A – N instances are available typically for students you can pool students to VMs, again this depends on what services your running.
Once you have completed all the required field the Azure service request is validated
Once complete simply click ok and the service will start to deploy. Azure IaaS services typically take a max of 15 mins to deploy VMs. The deployment status is clearly shown.
You can then add new user accounts for your students and permission.
You then simply issues these and instructions of how to SSH into the server and allow students to run tasks and workloads.
Here is an example of of how Oxford University are communicating this to their students for undertaking deep learning using Azure GPU N Series instances.
Connect to your server
By this point you should have received an IP and a password in your college email. Using the ip and password provided you can connect to your server using
ssh deepnlp2017@<your server ip>
allows access multiple separate terminal sessions inside a remote terminal session. Using
you can keep sessions alive in the background (e.g. training your network) even when disconnecting, and reconnect to them the next time you login. Basic shortcuts:
detach from session
ctrl+b - d
change name of session
ctrl+b - shift+4
reconnect to session
tmux attach -t <session_name>
You are ready to install python and your favourite framework:
Performance and Monitoring
So now you have your servers live and students are using the services, what you need to do know is understand the utilisation.
Azure offers metrics for VMs so you can log into the Azure Portal
and select resources groups, select the resource group for your class and then the VMs you wish to view metrics for.
This is a an excellent resource as it ensure your doing right sizing for the VMs and workloads which are being undertaken by students.
So know we have our class infrastructure students are successfully utilising shared or dedicated VM instances and you know that you have right sized your VM and loading and performance is within acceptable preferences ranges.
If you need help there lots of resources directly available within the Azure portal