As an IT administrator, it can become very challenging to meet the SOAs - to meet the
end-user requirements from data science teams - whether that’s an individual data scientist,
a data engineer, a platform engineer - it can take a very long time for me to provision
the infrastructure required for them to get their job done.
GPUs in particular have become very difficult to obtain.
Getting the GPU card installed and getting the infrastructure ready.
Getting the drivers deployed in a fashion where they can be utilized by the overlying
application can take days, weeks, even months to provision.
This GPU as a Service offering, powered by HPE Bluedata software and HPE Apollo and ProLiant
infrastructure, allows IT administrators to have a cloud-like experience where they can
provision GPU infrastructure resources in a matter of minutes.
So if I log in here as an administrator, the first thing we’ll see here is all the utilization
of my underlying infrastructural resources.
GPUs, as you can see, I have up to 24 GPUs available, and two of them are currently being
used.
If I go into the Installation tab, I can see all the information of the underlying HPE
infrastructural resources, such as type of server - in this case, Apollo 6500 - which
rack it’s installed on, and how many resources it has as far as CPU, memory and in this case,
GPU.
This machine has 8 GPU cards installed.
If I want to add additional servers, I can easily add the IP address of the new server,
the credentials, and click Add Hosts.
BlueData will dynamically add the infrastructural resources to the environment.
Once I have my infrastructure configured, I can go now and create tenants.
Tenants are private groupings of logical resources.
These can be divided by projects, user groups, different applications, however you want to
divide up and isolate your individual environments.
You can set specific quotas for how much resources each individual tenant can consume.
In this case, this Team 2 tenant can only consume 8 out of the 24 GPUs I have installed
on this entire platform.
So if I go into this Team 2 tenant, what I’ll see is all of the resources that are currently
being utilized within this environment.
I have no visibility or access to any applications or environments outside of this tenant.
Within this Team 2 tenant, I can go and create individual flavors.
These flavors are going to be predefined configurations of virtual nodes of how much CPU, memory,
GPU and storage each individual configuration is going to consume.
You can create as many different flavors as you want.
Now I’ll show you how easy it is to create a virtual environment.
Let’s say, for example, you get a request from Steve.
Steve requires a single node CentOS environment with 2 GPU devices.
There are two ways you can create this environment for Steve.
You can either create a predefined Quick Launch Template which has all of the configuration
predefined for you so you can just click one button and the environment will come up for
you, or you can create it manually.
In this case, we’ll create it manually.
Go to Create Cluster.
We’ll set our Cluster Name, so in this case, this is going to be Steve’s GPU Environment.
Steve wants a CentOS-based environment, so we have chosen the right operating system.
Referring back to the flavors, Steve would like to have 2 GPU devices, so in this case,
we have a medium-sized flavor so we can choose 2 GPU devices, and Steve only wants one node
for this environment.
So now, I can go ahead and create my cluster.
As this cluster comes up, we’re going to switch over to another cluster that I’ve
already created for Joel.
I can give Joel access to this environment where he can SSH into his virtual node.
Joel’s user experience is that this environment is running on a set of physical machines.
Joel can see that this is a Red Hat operating system.
All of the Nvidia CLI drivers are pre-installed in this virtual environment, so I can run
the Nvidia CLI commands to see all the information of my GPU devices as far as information and
utilization.
Now if we look at our dashboard here, we see that we’re using 50% of the GPU devices
that I have available within this individual tenant.
So again, this tenant was only provided up to 8 GPU devices, and I’m currently using
4 of those GPU devices.
Another challenge that IT administrators face is the ability to revoke these environments.
What we can do here is, we can come in and stop the clusters, once they’re not in use
anymore, to reclaim all of the compute resources.
Once these environments are stopped, it gives me the ability to come back and restart the
environments when Joel or Steve should require these environments to run additional jobs,
or I can choose to terminate them if they’re not needed anymore.
HPE BlueData software combined with HPE GPU-enabled infrastructure allows IT administrators to
increase utilization, reduce costs, and maximize the ROI on their GPU-enabled infrastructure.