Faster Machine Learning in a World with Limited Memory

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.

Disaggregated Or Hyperconverged, What Storage Will Win The Enterprise?

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.

The OSI model explained: How to understand (and remember) the 7 layer network model

When most non-technical people hear the term “seven layers”, they either think of the popular Super Bowl bean dip or they mistakenly think about the seven layers of Hell, courtesy of Dante’s Inferno (there are nine). For IT professionals, the seven layers refer to the Open Systems Interconnection (OSI) model, a conceptual framework that describes the functions of a networking or telecommunication system.The model uses layers to help give a visual description of what is going on with a particular networking system. This can help network managers narrow down problems (Is it a physical issue or something with the application?), as well as computer programmers (when developing an application, which other layers does it need to work with?). Tech vendors selling new products will often refer to the OSI model to help customers understand which layer their products work with or whether it works “across the stack”.To read this article in full, please click here

The OSI model explained: How to understand (and remember) the 7 layer network model

When most non-technical people hear the term “seven layers”, they either think of the popular Super Bowl bean dip or they mistakenly think about the seven layers of Hell, courtesy of Dante’s Inferno (there are nine). For IT professionals, the seven layers refer to the Open Systems Interconnection (OSI) model, a conceptual framework that describes the functions of a networking or telecommunication system.The model uses layers to help give a visual description of what is going on with a particular networking system. This can help network managers narrow down problems (Is it a physical issue or something with the application?), as well as computer programmers (when developing an application, which other layers does it need to work with?). Tech vendors selling new products will often refer to the OSI model to help customers understand which layer their products work with or whether it works “across the stack”.To read this article in full, please click here

The Journey to 150,000 Containers at PayPal

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

BrandPost: Enterprises and Carriers in Sync with NFV

Historically, there’s always been tension between enterprises and carriers over equipment and servicing issues. But network functions virtualization (NFV) is providing more visibility into the network, giving enterprises greater confidence in what they’re paying for.Back in 2009, a survey by consulting firm EY found deep skepticism among enterprise users regarding telecom service providers. More than half of those surveyed at that time would not consider telcos for IT help desk, business consulting or cloud services.To read this article in full, please click here

The Eternal Cost Savings of Netflix’s Internal Spot Market

 

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:

Postpone Inbox Procrastination

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!

But how?

  1. Getting to inbox zero each day is, first of all, an important goal. In other words, I really want to be at inbox zero each day. I don’t want to leave items hanging around for the next day. Therefore, among all my tasks, I have to prioritize inbox management.
  2. I filter messages heavily. I use Gmail, and have begun digging into the filtering system. At the moment, I have 27 rules that route messages to folders. Those rules are covering several dozen PR agencies, newsletters, and auto-notifiers. This helps me to focus when I’m working on my inbox, making it much easier to evaluate and react to messages depending on the folder they were routed to.
  3. I unsubscribe from uninteresting lists. Because I work in media, I receive pitches everyday from PR firms who don’t know me, but Continue reading

Postpone Inbox Procrastination

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!

But how?

  1. Getting to inbox zero each day is, first of all, an important goal. In other words, I really want to be at inbox zero each day. I don’t want to leave items hanging around for the next day. Therefore, among all my tasks, I have to prioritize inbox management.
  2. I filter messages heavily. I use Gmail, and have begun digging into the filtering system. At the moment, I have 27 rules that route messages to folders. Those rules are covering several dozen PR agencies, newsletters, and auto-notifiers. This helps me to focus when I’m working on my inbox, making it much easier to evaluate and react to messages depending on the folder they were routed to.
  3. I unsubscribe from uninteresting lists. Because I work in media, I receive pitches everyday from PR firms who don’t know me, but Continue reading

When POSIX I/O Meets Exascale, Do the Old Rules Apply?

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.

Cloudera Puffs Up Analytics Database For Clouds

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.