The specific demands of artificial intelligence, machine learning, and deep learning applications test data center performance, dependability, and scalability. It is even more challenging as engineers imitate the design of public clouds to simplify the shift to hybrid cloud and on-premise deployments. Data centers need to evolve to cater to workloads associated with AI.
GPU servers are now commonly used. The ecosystem around GPU computing is swiftly evolving to increase the efficiency and scalability of GPU workloads. Yet there are tricks to maximizing the more costly GPU utilization while avoiding potential choke points in storage and networking.
Let's take a look at some of the best practices that will prepare a data center for heavy workloads from AI, ML, and DL:
Map Out Scalability Plans
Ensure the scale-out potential of the infrastructure you build in the present. If your infrastructure is easily scalable, you end up avoiding disruptive moves with every growth phase. Therefore, what you need is close communication between system administrators and data scientists to know about the performance requirements and the path of infrastructure evolution.
Multiple GPUs in one server is an efficient way for data sharing within the system. Moreover, it is cost-effective! Eventually, a single server will no longer be enough to work through the growth training database in a reasonable time. Thus, building a shared storage infrastructure into the design will make it easier to add GPU servers as AI/ML/DL use expands.
Resource scheduling and sharing is a critical concept to develop for maintaining cost-effective data centers. This approach enables co-working on data, so while one group of data scientists get new data that needs to be ingested, others will train on their available data, while elsewhere, previously generated models will be used in production. Kubernetes has become a widespread solution to this problem, making cloud technology readily available on-premise and making hybrid deployments attainable.
Parallel file systems
Parallel file systems can help in handling the metadata of a large number of small files efficiently. They enable 3 to 4 times faster analysis of ML data sets by delivering tens of thousands of small files per second across the network. Given the read-only nature of training data, it's also possible to avoid the need for a parallel file system altogether. Especially when making the data volumes directly available to the GPU servers and sharing them in a coordinated way through a framework like Kubernetes.
Reliable Data Center For Your Cloud Needs
When it comes down to storing and accessing large amounts of data securely through your business or organization, cloud services may be the solution that you're looking for. VEXXHOST has two data center regions within Quebec for high-density power exactly where you want it - in Canada. We can also give you high-speed direct access to Silicon Valley's Tier-1 carriers and blazing connectivity through our Santa Clara public cloud region.
If you are interested in knowing more about our data center specs or are interested in a public or hosted private cloud environment, get in touch!