Striking acceptable training times for GPU accelerated machine learning on very large datasets has long-since been a challenge, in part because there are limited options with constrained on-board GPU memory.
For those who are working on training against massive volumes (in the many millions to billions of examples) using cloud infrastructure, the impetus is greater than ever to pare down training time given the per-hour instance costs and for cloud-based GPU acceleration on hardware with more memory (the more expensive Nvidia P100 with 16 GB memory over a more standard 8 GB memory GPU instance). Since hardware limitations are not …
Faster Machine Learning in a World with Limited Memory was written by Nicole Hemsoth at The Next Platform.
The company says the new capabilities don't compete with its service provider customers.
It has been a long time coming, but hyperconverged storage pioneer Nutanix is finally letting go of hardware, shifting from being an a server-storage hybrid appliance maker to a company that sells software that provides hyperconverged functionality on whatever hardware large enterprises typically buy.
The move away from selling appliances was something that The Next Platform has been encouraging Nutanix to do to broaden its market appeal, but until the company reached a certain level of demand from customers, Nutanix had to restrict its hardware support matrix so it could affordably put a server-storage stack in the field and not …
Disaggregated Or Hyperconverged, What Storage Will Win The Enterprise? was written by Timothy Prickett Morgan at The Next Platform.
IHS Markit research sheds light on top vendors in hot software-defined WAN space.
The Silicon Valley firm is working with Telefonica to demo the technology.
PayPal is committed to democratizing financial services and empowering people and businesses to join and thrive in the global economy. Their open digital payments platform gives 218 million active account holders the confidence to connect and transact in new and powerful ways. To achieve this, PayPal has built a global presence that must be highly available to all its users: if PayPal is down, the effects ripple down to many of their small business customers, who rely on PayPal as their sole payment processing solution.
PayPal turned to Docker Enterprise Edition to help them achieve new operational efficiencies, including a 50% increase in the speed of their build-test-deploy cycles. At the same time, they increased application availability through Docker’s dynamic placement capabilities and infrastructure independence; and they improved security by using Docker to automate and granularly control access to resources. On top of the operational benefits, PayPal’s use of Docker empowered developers to innovate and try new tools and frameworks that previously were difficult to introduce due to PayPal’s application and operational complexity.
Meghdoot Bhattacharya, Cloud Engineer at PayPal, shared the journey his team has helped PayPal undertake over the course of the past two years to introduce Docker in Continue reading
The release builds on its contributions to past Kubernetes projects.
Aryaka also gains revenue prominence among SD-WAN vendors.
Netflix used their internal spot market to save 92% on video encoding costs. The story of how is told by Dave Hahn in his now annual A Day in the Life of a Netflix Engineer. Netflix first talked about their spot market in a pair of articles published in 2015: Creating Your Own EC2 Spot Market Part 1 and Part 2.
The idea is simple:
Netflix runs out of three AWS regions and uses hundreds of thousands of EC2 instances; many are underutilized at various parts in the day.
Video encoding is 70% of Netflix’s computing needs, running on 300,000 CPUs in over 1000 different autoscaling groups.
So why not create a spot market to process video encoding?
As background, Dave explained the video encoding process:
I’ve recently admitted to myself that my ineptitude with my inbox is due largely to procrastination. That is, I can’t face the task that a particular inbox message presents, and thus I ignore the message. With this admission comes a desire to reach inbox zero each and every day. I don’t like my productivity squashed by ineptitude. I must overcome!
I’ve recently admitted to myself that my ineptitude with my inbox is due largely to procrastination. That is, I can’t face the task that a particular inbox message presents, and thus I ignore the message. With this admission comes a desire to reach inbox zero each and every day. I don’t like my productivity squashed by ineptitude. I must overcome!
The GNU Public License version 2 (GPLv2) is arguably the most important open-source license for one reason: It’s the license Linux uses. On November 27, three Linux-using technology powers, Facebook, Google, and IBM, and the major Linux distributor Red Hat announced they would extend additional rights to help companies who’ve made GPLv2 open-source license compliance errors and mistakes. —Steven J. Vaughan-Nichols @ ZDNetThe GNU Public License version 2 (GPLv2) is arguably the most important open-source license for one reason: It’s the license Linux uses. On November 27, three Linux-using technology powers, Facebook, Google, and IBM, and the major Linux distributor Red Hat announced they would extend additional rights to help companies who’ve made GPLv2 open-source license compliance errors and mistakes. —Steven J. Vaughan-Nichols @ ZDNet
We’ve all grown up in a world of digital filing cabinets. POSIX I/O has enabled code portability and extraordinary advances in computation, but it is limited by its design and the way it mirrors the paper offices that it has replaced.
The POSIX API and its implementation assumes that we know roughly where our data is, that accessing it is reasonably quick and that all versions of the data are the same. As we move to exascale, we need to let go of this model and embrace a sea of data and a very different way of handling it.
In …
When POSIX I/O Meets Exascale, Do the Old Rules Apply? was written by Nicole Hemsoth at The Next Platform.
In many ways, public clouds like Amazon Web Services, Microsoft Azure, and Google Cloud Platform can be the great equalizers, giving enterprises access to computing and storage resources that they may not have the money to be able to bring into their on-premises environments. Given the new compute-intensive workloads like data analytics and machine learning, and the benefits they can bring to modern businesses, this access to cloud-based platforms is increasingly critical to large enterprises.
Cloudera for several years has been pushing its software offerings – such as Data Science Workbench, Analytic DB, Operational DB, and Enterprise Data Hub – …
Cloudera Puffs Up Analytics Database For Clouds was written by Jeffrey Burt at The Next Platform.