Lambda, Razer launch laptop for deep-learning app development

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Not all IT administrators realize this, but when an enterprise is developing deep-learning applications for industrial, pharmaceuticals, pedagogy, and medical research, it is more efficient and saves time to develop them using a Linux workstation. Why? Because apps will eventually run on Linux production servers, and they will be speaking the same code language before they connect.

Lining up apps built on Linux laptops with production servers running on Red Hat, Ubuntu, Debian or similar OSes can avoid many potential problems when putting apps into action, said Stephen Balaban, CEO and co-founder of Lambda, VentureBeat.

According to W3Techs, about 42 percent of all production web servers run some kind of Linux, while Windows servers account for about 20 percent of the market. According to Statista, Linux or Unix servers account for 19% of the total global server market (most of which are in data centers), while Windows accounts for about 72% of the market.

It’s all about developing deep-learning apps

Balaben told VentureBeat that his company today unveiled its new Razor x Lambda Tenserbook, a device he described as “the world’s most powerful laptop designed for deep learning.” Nvidia GPUs, 64GB RAM, Ubuntu Linux, Lambda’s deep-learning software and laptops connected to the Lambda GPU cloud provide high computing performance to developers to create, train and test deep-learning models locally, Balab said.

“Most ML engineers do not have a dedicated GPU laptop, which forces them to use resources shared on a remote machine, slowing down their development cycle,” said Balaban. “When SSHing gets stuck on a remote server, you have no local data or code and it is difficult to show your model to colleagues. TenserBook solves this. It’s pre-installed with PyTorch and TensorFlow and lets you quickly train and demo your models: everything from the local GUI interface. No more SSH! ”

The new TenserBook is pre-configured with Lambda’s complete software environment, which includes Ubuntu Linux with a Lambda stack to train large workloads anytime, anywhere. Laptops have Razor’s high-performance hardware, powered by Nvidia RTX 3080, a well-known mobile GPU dedicated to dedicated, uninterrupted computing. This works in full compatibility with TensorFlow, PyTorch, cuDNN, CUDA and other ML frameworks and tools, Balab told VentureBeat.

Travis First, head of Lambda’s laptop division, said: “Razor’s experience in developing high-performance products for both game players and creators has been a crucial building block for the Lambda Tenserbook, a deep-learning system for engineers.

Specks Lambda Hardware

  • 15.6-inches. 2560 × 1440 165Hz display
  • Nvidia RTX 3080 Max-Q GPU with 16GB VRAM
  • Intel i7-11800 processor (8 cores, 2.3GHz to 4.6GHz)
  • 64GB DDR4 memory
  • 2TB SSD storage
  • Thunderbolt 4, USB 3.2, HDMI 2.1 ports
  • Slim 4.4-lb. Aluminum unibody chassis
  • 1080p webcam

Specs for Lamda software

  • Ubuntu Linux 20.04 LTS (Microsoft Windows Dual-Boot Optional)
  • Lambda stack with PyTorch, TensorFlow, CUDA, cuDNN and Nvidia drivers
  • One year of Lambda Engineering Support

Since its inception in 2012, San Francisco, California-based Lambda has become a de facto deep-learning infrastructure provider for many research and engineering teams around the world. The top five tech companies (Google, Facebook, Apple, Microsoft, Amazon), 97 percent of the top research universities in the U.S. (including MIT and Caltech) and thousands of businesses and organizations, including the Department of Defense, use Lambda. .

These teams use Lambda’s GPU clusters, servers, workstations and cloud instances to train neural networks for cancer detection, autonomous aircraft, drug discovery, self-driving cars and more.

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