This blog describes how to set up a Run:AI cluster on Lambda Cloud with one or multiple cloud instances.
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.
This curriculum provides an overview of free online resources for learning about deep learning. It includes courses, books, and even important people to follow.
This tutorial shows you how to install Docker with GPU support on Ubuntu Linux. To get GPU passthrough to work, you'll need docker, nvidia-container-toolkit, Lambda Stack, and a docker image with a GPU accelerated library.
This guide will walk you through how to load data from various sources onto your Lambda Cloud GPU instance. If you're looking for how to get started and SSH into your instance for the first time, check out our Getting Started Guide.
This guide will walk you through the process of launching a Lambda Cloud GPU instance and using SSH to log in. For this guide we'll assume that you're running either Mac OSX or Linux.
This tutorial walks through the steps required to set up a Mellanox SB7800 36-port switch. The subnet manager discovers and configures the devices running on the InfiniBand fabric. This guide shows you how to set it up via the command line or via the web browser.
This tutorial explains the basics of TensorFlow 2.0 with image classification as the example. 1) Data pipeline with dataset API. 2) Train, evaluate, save and restore models with Keras. 3) Multiple-GPU with distributed strategy. 4) Customized training with callbacks.
This tutorial will walk you through how to setup a working environment for multi-GPU training with Horovod and Keras.
Resource utilization tracking can help machine learning engineers improve their software pipeline and model performance. This blog discusses how to use Weights & Biases to inspect the efficiency of TensorFlow training jobs.
Distributed training allows scaling up deep learning tasks so bigger models can be learned from more extensive data. In this tutorial, we will explain how to do distributed training across multiple nodes.
This tutorial explains how early stopping is implemented in TensorFlow. The key lesson is to use tf.keras.EarlyStopping callback. Early stopping is triggered by monitoring if a certain quantity has improved over the latest period of time.
This tutorial explains how to use checkpoints to save and restore TensorFlow models during the training. The key is to use tf.kears.ModelCheckpoint callbacks to save the model. Set initial_epoch in the model.fit call to restore the model from a pre-saved checkpoint.
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