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Deploy Llama2 on AWS with the Help of Walrus


Llama 2 is Meta's next-generation open-source large language model. It consists of a series of models that have undergone pre-training and fine-tuning, with parameters ranging from 7 billion to 70 billion. This article will guide you on how to deploy Llama 2 on AWS using the Walrus, theapplication management platform. 

Walrus has been officially open-sourced, and you can find detailed open-source information about the project by clicking here.


To follow this tutorial, you will need:

  1. An AWS account with associated credentials, and sufficient permissions to create EC2 instances.

  2. Walrus installed.

Note: While using CPU is cheaper than GPU, it still incurs costs corresponding to the EC2 instance.

The Simple Way

With Walrus, you can have a running llama-2 instance on AWS with a user-friendly web UI in about a minute. Just follow these steps:

Add the llama-2 Service Template

  1. Log in to Walrus, click on [Operations Center] in the left navigation, go to the [Templates] tab, and click the [New Template] button.

  2. Enter a template name, e.g., llama-2.

  3. In the source field, enter

  4. Click [Save].


Configure Environment and AWS Credentials

  1. In the left navigation, click on [Application Management], go to the default project view, and click the [Connectors] tab.

  2. Click the [New Connector] button and select the Cloud Provider type.

  3. Enter a connector name, e.g., aws.

  4. Choose AWS for the [Type] option.Select Tokyo (ap-northeast-1) for the Region option.

  5. Click [Save].

Note: The specified region is used here because the subsequent steps involve using an AMI from that region. If you want to use a different region, you can export the AMI to your region or refer to the following sections on how to build the llama-2 image from scratch.


  1. Click the [Environments] tab, click the [New Environment] button.

  2. Enter an environment name, e.g., dev.

  3. Click the [Add Connector] button and select the aws connector created in the previous step.

  4. Click [Save].


Create the llama-2 Service

  1. In the [Environments] tab, click on the name of the dev environment to enter its view.

  2. Click the [New Service] button.

  3. Enter a service name, e.g., my-llama-2.

  4. Choose llama-2 in the Template option.

  5. Click [Save].

Note: The default service configuration assumes your AWS account has a default VPC in the corresponding region. If you don't have a default VPC, create a new VPC, associate a subnet and a security group with it in the AWS VPC console. The security group needs to open port 7860 TCP (for accessing the llama-2 web UI). You can set your VPC name and security group name in the service configuration.

Accessing the llama-2 Web UI

You can see the deployment and running status of the llama-2 service on its details page. Once the llama-2 service deployment is complete, you can access its web UI by clicking the access link of the service in the Walrus UI.



Deep Dive: Building the llama-2 Image from Scratch

The above instructions utilized a pre-built llama-2 image. This approach saves time as you don't need to download the large language model (often with a significant file size) or build the inference service when creating a new llama-2 instance. This section explains how such a llama-2 image is built.

You can find the complete build process here.

Key steps include:

# get text-generation-webui
git clone && cd text-generation-webui
# configure text-generation-webui
ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
sed -i '/^CLI_ARGS=/s/.*/CLI_ARGS=--model llama-2-7b-chat.ggmlv3.q4_K_M.bin --wbits 4 --listen --auto-devices/' .env
sed -i '/^\s*deploy:/,$d' docker/docker-compose.yml
# get quantized llama-2
curl -L --output ./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin
# build and run
docker compose up --build

In essence, this process downloads the quantized llama-2-7b-chat model, then builds and utilizes text-generation-webui to launch the llama-2 service.

Congratulations! You have successfully deployed Llama-2 on AWS using Walrus with CPU. If you have any other questions about Walrus, feel free to join our community group and communicate directly with our development engineers.


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