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Run:AI brings virtualization to GPUs running Kubernetes workloads
In the early 2000s, VMware introduced the world to virtual servers that allowed IT to make more efficient use of idle server capacity. Today, Run:AI is introducing that same concept to GPUs running containerized machine learning projects on Kubernetes.
This should enable data science teams to have access to more resources than they would normally get were they simply allocated a certain number of available GPUs. Company CEO and co-founder Omri Geller says his company believes that part of the issue in getting AI projects to market is due to static resource allocation holding back data science teams.
“There are many times when those important and expensive computer sources are sitting idle, while at the same time, other users that might need more compute power since they need to run more experiments and don’t have access to available resources because they are part of a static assignment,” Geller explained.
To solve that issue of static resource allocation, Run:AI came up with a solution to virtualize those GPU resources, whether on prem or in the cloud, and let IT define by policy how those resources should be divided.
“There is a need for a specific virtualization approaches for AI and actively managed orchestration and scheduling of those GPU resources, while providing the visibility and control over those compute resources to IT organizations and AI administrators,” he said.
Run:AI creates a resource pool, which allocates based on need. Image Credits Run:AI
Run:AI built a solution to bridge this gap between the resources IT is providing to data science teams and what they require to run a given job, while still giving IT some control over defining how that works.
“We really help companies get much more out of their infrastructure, and we do it by really abstracting the hardware from the data science, meaning you can simply run your experiment without thinking about the underlying hardware, and at any moment in time you can consume as much compute power as you need,” he said.
While the company is still in its early stages, and the current economic situation is hitting everyone hard, Geller sees a place for a solution like Run:AI because it gives customers the capacity to make the most out of existing resources, while making data science teams run more efficiently.
He also is taking a realistic long view when it comes to customer acquisition during this time. “These are challenging times for everyone,” he says. “We have plans for longer time partnerships with our customers that are not optimized for short term revenues.”
Run:AI was founded in 2018. It has raised $13 million, according to Geller. The company is based in Israel with offices in the United States. It currently has 25 employees and a few dozen customers.
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This year’s Computex was a wild ride with dueling chip releases, new laptops and 467 startups
After a relatively quiet show last year, Computex picked up the pace this year, with dueling chip launches by rivals AMD and Intel and a slew of laptop releases from Asus, Qualcomm, Nvidia, Lenovo and other companies.
Founded in 1981, the trade show, which took place last week from May 28 to June 1, is one of the ICT industry’s largest gatherings of OEMs and ODMs. In recent years, the show’s purview has widened, thanks to efforts by its organizers, the Taiwan External Trade Development Council and Taipei Computer Association, to attract two groups: high-end computer customers, such as hardcore gamers, and startups looking for investors and business partners. This makes for a larger, more diverse and livelier show. Computex’s organizers said this year’s event attracted 42,000 international visitors, a new record.
Though the worldwide PC market continues to see slow growth, demand for high-performance computers is still being driven by gamers and the popularity of esports and live-streaming sites like Twitch. Computex, with its large, elaborate booths run by brands like Asus’ Republic of Gaming, is a popular destination for many gamers (the show is open to the public, with tickets costing NTD $200, or about $6.40), and began hosting esport competitions a few years ago.
The timing of the show, formally known as the Taipei International Information Technology Show, at the end of May or beginning of June each year, also gives companies a chance to debut products they teased at CES or preview releases for other shows later in the year, including E3 and IFA.
One difference between Computex now and ten (or maybe even just five) years ago is that the increasing accessibility of high-end PCs means many customers keep a close eye on major announcements by companies like AMD, Intel and Nvidia, not only to see when more powerful processors will be available but also because of potential pricing wars. For example, many gamers hope competition from new graphic processor units from AMD will force Nvidia to bring down prices on its popular but expensive GPUs.
The Battle of the Chips
The biggest news at this year’s Computex was the intense rivalry between AMD and Intel, whose keynote presentations came after a very different twelve months for the two competitors.
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Paperspace scores $13M investment for AI-fueled application development platform
Paperspace wants to help developers build artificial intelligence and machine learning applications with a software/hardware development platform powered by GPUs and other powerful chips. Today, the Winter 2015 Y Combinator grads announced a $13 million Series A.
Battery Ventures led the round with participation from SineWave Ventures, Intel Capital and Sorenson Ventures. Existing investor Initialized Capital also participated. Today’s investment brings the total amount to $19 million raised.
Dharmesh Thakker, a general partner with Battery Ventures sees Paperspace as being in the right place at the time. As AI and machine learning take off, developers need a set of tools and GPU-fueled hardware to process it all. “Major silicon, systems and Web-scale computing providers need a cloud-based solution and software ‘glue’ to make deep learning truly consumable by data-driven organizations, and Paperspace is helping to provide that,” Thakker said in a statement.
Paperspace provides its own GPU-powered servers to help in this regard, but co-founder and CEO Dillon Erb says they aren’t trying to compete with the big cloud vendors. They offer more than a hardware solution to customers. Last spring, the company released Gradient, a serverless tool to make it easier to deploy and manage AI and machine learning workloads.
By making Gradient a serverless management tool, customers don’t have to think about the underlying infrastructure. Instead, Paperspace handles all of that for them providing the resources as needed. “We do a lot of GPU compute, but the big focus right now and really where the investors are buying into with this fundraise, is the idea that we are in a really unique position to build out a software layer and abstract a lot of that infrastructure away [for our customers],” Erb told TechCrunch.
He says that building some of the infrastructure was an important early step, but they aren’t trying to compete with the cloud vendors. They are trying to provide a set of tools to help developers build complex AI and machine learning/deep learning applications, whether it’s on their own infrastructure or on the mainstream cloud providers like Amazon, Google and Microsoft.
What’s more, they have moved beyond GPUs to support a range of powerful chips being developed to support AI and machine learning workloads. It’s probably one of the reasons that Intel joined as an investor in this round.
He says the funding is definitely a validation of something they set out to work on when they first started this in 2014 and launched out of Y Combinator in 2015. Back then he had to explain what a GPU was in his pitch decks. He doesn’t have to do that anymore, but there is still plenty of room to grow in this space.
“It’s really a greenfield opportunity, and we want to be the go-to platform that you can start building out into intelligent applications without thinking about infrastructure.” With $13 million in hand, it’s safe to say that they are on their way.
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Nvidia launches colossal HGX-2 cloud server to power HPC and AI
Nvidia launched a monster box yesterday called the HGX-2, and it’s the stuff that geek dreams are made of. It’s a cloud server that is purported to be so powerful it combines high-performance computing with artificial intelligence requirements in one exceptionally compelling package.
You know you want to know the specs, so let’s get to it: It starts with 16x NVIDIA Tesla V100 GPUs. That’s good for 2 petaFLOPS for AI with low precision, 250 teraFLOPS for medium precision and 125 teraFLOPS for those times when you need the highest precision. It comes standard with a 1/2 a terabyte of memory and 12 Nvidia NVSwitches, which enable GPU to GPU communications at 300 GB per second. They have doubled the capacity from the HGX-1 released last year.
Chart: Nvidia
Paresh Kharya, group product marketing manager for Nvidia’s Tesla data center products, says this communication speed enables them to treat the GPUs essentially as a one giant, single GPU. “And what that allows [developers] to do is not just access that massive compute power, but also access that half a terabyte of GPU memory as a single memory block in their programs,” he explained.
Graphic: Nvidia
Unfortunately you won’t be able to buy one of these boxes. In fact, Nvidia is distributing them strictly to resellers, who will likely package these babies up and sell them to hyperscale data centers and cloud providers. The beauty of this approach for cloud resellers is that when they buy it, they have the entire range of precision in a single box, Kharya said.
“The benefit of the unified platform is as companies and cloud providers are building out their infrastructure, they can standardize on a single unified architecture that supports the entire range of high-performance workloads. So whether it’s AI, or whether it’s high-performance simulations, the entire range of workloads is now possible in just a single platform,”Kharya explained.
He points out this is particularly important in large-scale data centers. “In hyperscale companies or cloud providers, the main benefit that they’re providing is the economies of scale. If they can standardize on the fewest possible architectures, they can really maximize the operational efficiency. And what HGX allows them to do is to standardize on that single unified platform,” he added.
As for developers, they can write programs that take advantage of the underlying technologies and program in the exact level of precision they require from a single box.
The HGX-2 powered servers will be available later this year from partner resellers, including Lenovo, QCT, Supermicro and Wiwynn.
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Pure Storage teams with Nvidia on GPU-fueled Flash storage solution for AI
As companies gather increasing amounts of data, they face a choice over bottlenecks. They can have it in the storage component or the backend compute system. Some companies have attacked the problem by using GPUs to streamline the back end problem or Flash storage to speed up the storage problem. Pure Storage wants to give customers the best of both worlds.
Today it announced, Airi, a complete data storage solution for AI workloads in a box.
Under the hood Airi starts with a Pure Storage FlashBlade, a storage solution that Pure created specifically with AI and machine learning kind of processing in mind. NVidia contributes the pure power with four NVIDIA DGX-1 supercomputers, delivering four petaFLOPS of performance with NVIDIA ® Tesla ® V100 GPUs. Arista provides the networking hardware to make it all work together with Arista 100GbE switches. The software glue layer comes from the NVIDIA GPU Cloud deep learning stack and Pure Storage AIRI Scaling Toolkit.
Photo: Pure Storage
One interesting aspect of this deal is that the FlashBlade product operates as a separate product inside of the Pure Storage organization. They have put together a team of engineers with AI and data pipeline understanding with the focus inside the company on finding ways to move beyond the traditional storage market and find out where the market is going.
This approach certainly does that, but the question is do companies want to chase the on-prem hardware approach or take this kind of data to the cloud. Pure would argue that the data gravity of AI workloads would make this difficult to achieve with a cloud solution, but we are seeing increasingly large amounts of data moving to the cloud with the cloud vendors providing tools for data scientists to process that data.
If companies choose to go the hardware route over the cloud, each vendor in this equation — whether Nvidia, Pure Storage or Arista — should benefit from a multi-vendor sale. The idea ultimately is to provide customers with a one-stop solution they can install quickly inside a data center if that’s the approach they want to take.
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Google Cloud launches preemptible GPUs with a 50% discount
Google Cloud today announced the launch of preemptible GPUs. Like Google’s preemptible VMs (and AWS’s comparable spot instances), these GPUs come at a significant discount — in this case, 50 percent. But in return, Google may shut them down at any point if it needs these resources. All you get is a 30-second warning. You also can only use any given preemptible GPU for up to… Read More
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