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RL Loop Deployment

This guide provides step-by-step instructions to deploy the RL Loop in Azure.

Prerequisites

Before you begin, ensure you have the following:

Setup

  1. Login to Azure:

    az login
  2. Install Bicep CLI (if not already installed - Bicep CLI Install Documentation)

    az bicep install
  3. Clone the repository:

    git clone https://github.com/VowpalWabbit/rl_loop_deployment.git
    cd rl_loop_deployment/deploy
  4. Load the rl_loop image (located here) to a container registry

    Load rl_loop_image.tar to a container registry such as Docker Hub or an Azure Container Registry. The procedure for both is similar.

    Docker Hub

    docker load -i rl_loop_image.tar
    docker tag personalizerstandaloneintegrationtest.azurecr.io/personalizerstandalone:<version-tag> <myname-or-organization>/rl_loop:latest
    docker login
    docker push <myname-or-organization>/rl_loop:latest

    Azure Container Registry

    docker load -i rl_loop_image.tar
    docker tag personalizerstandaloneintegrationtest.azurecr.io/personalizerstandalone:<version-tag> <my-acr>.azurecr.io/rl_loop:latest
    az login
    az acr login --name <my-acr>
    az acr show --name <my-acr> --query loginServer --output table
    docker push <my-acr>.azurecr.io/rl_loop:latest

Deployment Steps

  1. Create a Resource Group:

    Create a resource group if needed (exmaple below).

    az group create --name myResourceGroup --location eastus
  2. Image Repository Access

    Depending on the image source, there are three options for authenticating with the image repository:

    Credentials via Managed Identity

    Using managed identity by setting the registry object in the container's configuration as follows (see sample.bicepparam):

    // set the mainConfig.registry object in the bicepparam file
    registry: {
        host: 'acrhost.io', // e.g., docker.io, myacr.azurecr.io, etc.
        credentials: {
            type: 'managedIdentity',
            username: 'identity',
            password: null
        }
    }
    

    Credentials via a Key Vault (how to create a Key Vault?)

    Use a Key Vault by setting the registry object for the container's configuration as follows (see sample.biceparam):

    
    // get the secrets from the key vault in the bicepparam file
    param kvImageRegistryUsername = getSecret('mysubscriptionid', 'myresourcegroup', 'keyvaultname', 'imageRegistryUsername')
    param kvImageRegistryPassword = getSecret('mysubscriptionid', 'myresourcegroup', 'keyvaultname', 'imageRegistryPassword')
    
    ...
    
    // set the mainConfig.registry object in the bicepparam file
    registry: {
        host: 'acrhost.io', // e.g., docker.io, myacr.azurecr.io, etc.
        credentials: {
            type: 'keyVault'
        }
    }
    

    Credentials via Username/Password

    Using explicity username and password by setting the registry object in the container's configuration as follows (see sample.biceparam):

    Note: this method is not recommended

    // set the mainConfig.registry object in the bicepparam file
    registry: {
        host: 'acrhost.io', // e.g., docker.io, myacr.azurecr.io, etc.
        credentials: {
            type: 'usernamePassword',
            username: 'myusername',
            password: 'mypassword'
        }
    }
    
  3. Deploy the RL Loop using Bicep:

    See the deployment readme for more information on how to customize your deployment.

    az deployment group create --resource-group myResourceGroup --name sample_loop  --rollback-on-error --parameters sample.bicepparam
  4. Verify Deployment:

    Check the Azure portal to ensure all resources are deployed correctly.

Post-Deployment

  1. Access the Application:

    Navigate to the deployed container application in the resource group. The container for the sample deployment is called sample_loopcg.

  2. Monitor and Manage:

    Use Azure Portal to monitor the performance and manage the resources.

  3. Use rl_sim to simulate training:

Running rl_sim against your loop

The details building rl_sim are located in project reinforcement_learning.

  1. Build reinforcement_learning

  2. Setup your rl_sim settings

    ./generate-rl-sim-config.ps1 -appId sample_loop -resourceGroupName myResourceGroup -configFilename rl-sim-config.json
  3. Run rl_sim After building reinforcement_learning execute the r_rim_cpp simulator. This file is located in the following path.

    reinforcement_learning/build/binaries/Debug
    or
    reinforcement_learning/build/binaries/Release
    
    rl_sim_cpp.out.exe -j ./rl-sim-config.json

Cleanup

To clean up resources resulting from the provided Bicep scripts, use the az command to remove all resources with the specified deployment tag as follows.

Note: The deployment tag is specified during deployment as an optional parameter (see sample.bicepparam).

az resource list --resource-group rg_test_rl_loop --query "[?tags.deploymentGroupName=='sample_loop'].id" -o tsv | % { az resource delete --ids $_ }

Troubleshooting

  • Common Issues:

    • Ensure all prerequisites are met.
    • Verify Azure CLI and Bicep CLI are up to date.
    • Check for any error messages in the deployment output.
  • Useful Commands:

    • To view deployment logs:
      az deployment group show --resource-group myResourceGroup --name sample_loop

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