How deeply does LLMs in Production cover Google Compute Engine?

Guides account, project, billing, image, instance, SSH, file-transfer, and deletion steps.

Google Compute Engine

Google Compute Engine is Google Cloud's infrastructure service for creating and running virtual machines.

Google Compute Engine provides a practical way to create a cloud virtual machine for multi-GPU training when local hardware is unavailable. The trade-off is speed and capacity versus hourly cost: shut down environments when they are no longer needed. section 5.1.1

A typical setup involves:

Creating a Google Cloud account and project, configuring billing, and installing the gcloud CLI. section 5.1.1
Choosing a GPU-capable machine image and instance configuration. section 5.1.1
Connecting through SSH, copying project files, and deleting the instance after use. section 5.1.1

$ INSTANCE_NAME="g2-llminprod-example"
    $ gcloud compute instances create ${INSTANCE_NAME} --zone=us-west1-a 
    --machine-type=g2-standard-24 --image-project=deeplearning-platform-release
    --image=common-gpu-v20230925-debian-11-py310 --boot-disk-size=200GB --scopes
    cloud-platform --metadata=install-unattended-upgrades=False,install-nvidia-
    driver=True --maintenance-policy TERMINATE --restart-on-failure

Create a GPU-enabled instance with a selected image, boot disk, cloud-platform scope, NVIDIA driver installation, and restart settings. section 5.1.1

How LLMs in Production covers it

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Related concepts

Migrate to Virtual Machinesused by · Migrate to Virtual Machines, formerly Migrate for Compute Engine, is a Google Cloud service for migrating virtual machines from on-premises or other cloud environments to Compute Engine.
virtual machineuses · A virtual machine is a software-based environment that emulates or implements an abstract computer system, providing an interface on which an operating system or programs can execute.
Migrate to Containersused by · Migrate to Containers, formerly Migrate for Anthos, is a Google Cloud tool that converts applications from virtual machines or traditional environments into containerized workloads for Kubernetes.

This explainer was generated by Manning's knowledge graph from the text of LLMs in Production. The section links point to the parts of the book it draws on.