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.
The Lambda Deep Learning Blog
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Introducing the Lambda EchelonLambda Echelon [https://lambdalabs.com/gpu-cluster/echelon] is a GPU cluster designed for AI. It comes with the compute, storage, network, power, and support you need to tackle large scale deep learning tasks. Echelon offers a turn-key solution to faster training, faster hyperparameter search, and faster inference.
In this post we'll walk through using our Total Cost of Ownership (TCO) calculator to examine the cost of a variety of Lambda Hyperplane-16 clusters. We have the option to include 100 Gb/s EDR InfiniBand networking, storage servers, and complete rack-stack-label-cable service. The purpose of this post is to
Tracking system resource (GPU, CPU, etc.) utilization during training with the Weights & Biases Dashboard
One of the most asked questions we get at Lambda Labs is, “how do I track resource utilization for deep learning jobs?” Resource utilization tracking can help machine learning engineers improve both their software pipeline and model performance. I recently came across a tool called "Weights and Biases [https://www.
This presentation is a high-level overview of the different types of training regimes that you'll encounter as you move from single GPU to multi GPU to multi node distributed training. It briefly describes where the computation happens, how the gradients are communicated, and how the models are updated and communicated.
Titan V vs. RTX 2080 Ti vs. RTX 2080 vs. Titan RTX vs. Tesla V100 vs. GTX 1080 Ti vs. Titan Xp - TensorFlow benchmarks for neural net training.
RTX 2080 Ti vs. RTX 2080 vs. Titan RTX vs. Tesla V100 vs. Titan V vs. GTX 1080 Ti vs. Titan Xp benchmarks neural net training.
CPU, GPU, and I/O utilization monitoring using tmux, htop, iotop, and nvidia-smi. This stress test is running on a Lambda GPU Cloud [https://lambdalabs.com/service/gpu-cloud] 4x GPU instance.Often times you'll want to put a system through the paces after it's been set up. To stress test
Titan RTX vs. 2080 Ti vs. 1080 Ti vs. Titan Xp vs. Titan V vs. Tesla V100. For this post, Lambda engineers benchmarked the Titan RTX's deep learning performance vs. other common GPUs. We measured the Titan RTX's single-GPU training performance on ResNet50, ResNet152, Inception3, Inception4, VGG16, AlexNet, and SSD.
We open sourced the benchmarking code [https://github.com/lambdal/lambda-tensorflow-benchmark] we use at Lambda Labs so that anybody can reproduce the benchmarks that we publish or run their own. We encourage people to email us with their results and will continue to publish those results here. You can run
What's the best GPU for Deep Learning? The 2080 Ti. We benchmark the 2080 Ti vs the Titan V, V100, and 1080 Ti.