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Not all IT admins realize this, but when a company develops deep learning applications for industrial, pharmaceutical, academic and medical research, it is much more efficient and faster to develop them using Linux workstations. Why? Because apps will end up running on production Linux servers and they’ll be speaking the same code language long before they connect.
Aligning applications built on Linux laptops running operating systems such as Red Hat, Ubuntu, Debian, or others with production servers running the same operating systems avoids many potential snafus when upgrading. application implementation, an expert in this field, Stephen Balaban, CEO and co-founder of Lambda, told VentureBeat.
About 42% of all production web servers run Linux, while Windows servers make up about 20% of the market, according to W3Techs. Of the total global server market (most of which is in data centers), Linux or Unix servers account for 19%, while Windows accounts for about 72% of the market, according to Statista.
It is about developing deep learning applications
Balaban told VentureBeat that his company today released its new Razer x Lambda Tensorbook, a device he described as “the world’s most powerful laptop designed for deep learning.” The laptops, featuring Nvidia GPUs, 64GB of RAM, Ubuntu Linux, Lambda’s deep learning software, and coupled with the Lambda GPU Cloud, provide developers with high-end computing performance to create, train, and test deep learning models locally, Balaban said.
“Most ML engineers don’t have a dedicated GPU laptop, which forces them to use shared resources on a remote machine, which slows down their development cycle,” Balaban said. “When you’re stuck in SSH to a remote server, you don’t have any of your local data or code, and you even have a hard time demonstrating your model to your colleagues. The Tensorbook solves that problem. It comes preinstalled with PyTorch and TensorFlow and lets you quickly train and demonstrate your models—all from a local GUI—no more SSH!
The new Tensorbook comes pre-configured with a full Lambda software environment, including Ubuntu Linux with the Lambda stack to train large workloads anytime, anywhere, Balaban said. The laptop features high-performance hardware from Razer, powered by Nvidia RTX 3080, a popular mobile GPU for dedicated, uninterrupted computing. It works with full compatibility with TensorFlow, PyTorch, cuDNN, CUDA and other ML frameworks and tools, Balaban told VentureBeat.
“Razer’s experience in developing high-performance products for gamers and creators has been a critical component for the Lambda Tensorbook, a deep learning system for engineers,” said Travis Furst, Division Manager. laptops from Lambda.
- 15.6 inches. Display 2560×1440 165Hz
- Nvidia RTX 3080 Max-Q GPU with 16GB of VRAM
- Intel i7-11800 processor (8 cores, 2.3 GHz to 4.6 GHz)
- 64 GB of DDR4 memory
- 2TB SSD storage
- Thunderbolt 4, USB 3.2, HDMI 2.1 ports
- Slim 4.4 lbs. aluminum monocoque chassis
- 1080p webcam
Lamda Software Specifications
- Ubuntu Linux 20.04 LTS (dual boot Microsoft Windows optional)
- Lambda Stack with PyTorch, TensorFlow, CUDA, cuDNN and Nvidia drivers
- One year of Lambda technical support
Since its launch in 2012, San Francisco, CA-based Lambda has become the de facto deep learning infrastructure provider for many research and engineering teams around the world. Thousands of companies and organizations use Lambda, Balaban said, including the top five tech companies (Google, Facebook, Apple, Microsoft, Amazon), 97% of top research universities in the United States (including the MIT and Caltech) and the Department of Defense. .
These teams use Lambda’s GPU clusters, servers, workstations, and cloud instances to train neural networks for cancer detection, autonomous planes, drug discovery, self-driving cars, and more.
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