After a period of closed beta, persistent storage for Lambda GPU Cloud is now available for all A6000 and V100 instances in an extended open beta period.
The Lambda Deep Learning Blog
Voltron Data Case Study: Why ML teams are using Lambda Reserved Cloud Clusters
November 01, 2022
How to fine tune stable diffusion: how we made the text-to-pokemon model at Lambda
September 28, 2022
Lambda Cloud Storage is now in open beta: a high speed filesystem for our GPU instances
April 18, 2022
New laptop offers the industry’s most powerful mobile workstation for deep learning, enabling ML engineers to immediately focus on achieving breakthroughs in AI/ML anytime, anywhere.
This is the second consecutive year the deep learning infrastructure provider has been chosen for this top honor SAN FRANCISCO—April 5, 2022 – Lambda today announced it has been selected as an NVIDIA Partner Network (NPN) [https://www.nvidia.com/en-us/about-nvidia/partners/] Solutions Integration Partner of the Year for
The best tools for monitoring your GPU usage and performance statistics compared.
While waiting for NVIDIA's next-generation consumer and professional GPUs, we decided to write a blog about the best GPU for Deep Learning currently available, as of March 2022.
If you're trying to figure out how to build and scale your team's deep learning infrastructure, this presentation is for you. We walk you through the decisions associated with building cloud, on-prem, and hybrid infrastructure for your team. We've distilled best practices learned from helping thousands of teams build their
Deep learning is the most important technology to impact gaming since the advent of 3D graphics. This short video presentation walks you through a few of the technologies that will deliver unbelievable gaming experiences in the near future. Research covered in this presentation: 1. Photorealistic neural rendering 2. Deepfakes for
Today, we will show how to install Anaconda and how to use YAML files for versioning environments. Anaconda is a distribution of the Python for machine learning and data science that simplifies package management and deployment. It is an invaluable tool for controlling the versioning of packages in your code
NVIDIA® A40 GPUs are now available on Lambda Scalar servers [https://lambdalabs.com/products/scalar]. In this post, we benchmark the A40 with 48 GB of GDDR6 VRAM to assess its training performance using PyTorch and TensorFlow. We then compare it against the NVIDIA V100, RTX 8000, RTX 6000, and RTX 5000.
Researchers from NVIDIA and Aalto University have released StyleGAN3, removing a major flaw of current generative models and opening up new possibilities for their use in video and animation.
This curriculum provides an overview of free online resources for learning about deep learning. It includes courses, books, and even important people to follow. If you only want to do one thing, do this: Train an MNIST network with PyTorch. https://github.com/pytorch/examples/tree/master/mnist Introductory CS231n:
by Lambda and Scale Visualizing your training data is the foundation of every successful deep learning project. It’s important to both identify potential flaws in the ground truth labels, and to look for insights that will guide your choice of model architecture. Deeply understanding your data can help ensure
This post discusses the Total Cost of Ownership (TCO) for a variety of Lambda A100 servers and clusters. We first calculate the TCO for individual Hyperplane-A100 servers, and compare the cost with renting a AWS p4d.24xlarge instance which has the similar hardware and software set up. We then walk you through the cost of building and operating A100 clusters.