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11 changes: 11 additions & 0 deletions
11
cli/foundation-models/system/inference/visual-chat-completion/deploy.yml
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| $schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json | ||
| name: phi-3-vision | ||
| instance_type: Standard_NC48ads_A100_v4 | ||
| instance_count: 1 | ||
| liveness_probe: | ||
| initial_delay: 180 | ||
| period: 180 | ||
| failure_threshold: 49 | ||
| timeout: 299 | ||
| request_settings: | ||
| request_timeout_ms: 180000 |
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79
cli/foundation-models/system/inference/visual-chat-completion/online-endpoint-deployment.sh
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| set -x | ||
|
|
||
| # script inputs | ||
| subscription_id="<SUBSCRIPTION_ID>" | ||
| resource_group_name="<RESOURCE_GROUP>" | ||
| workspace_name="<WORKSPACE_NAME>" | ||
|
|
||
| # This is the model from system registry that needs to be deployed | ||
| registry_name="azureml" | ||
| model_name="Phi-3-vision-128k-instruct" | ||
| deployment_sku="Standard_NC48ads_A100_v4" | ||
|
|
||
| # Validate the existence of the model in the registry and get the latest version | ||
| model_list=$(az ml model list --name ${model_name} --registry-name ${registry_name} 2>&1) | ||
| if [[ ${model_list} == *"[]"* ]]; then | ||
| echo "Model doesn't exist in registry. Check the model list and try again."; exit 1; | ||
| fi | ||
| version_temp=${model_list#*\"version\": \"} | ||
| model_version=${version_temp%%\"*} | ||
|
|
||
| version=$(date +%s) | ||
| endpoint_name="phi-3-mini-v-instruct-$version" | ||
|
|
||
| # scoring_file | ||
| scoring_file="./sample_chat_completions_score.json" | ||
|
|
||
| # 1. Setup pre-requisites | ||
| if [ "$subscription_id" = "<SUBSCRIPTION_ID>" ] || \ | ||
| ["$resource_group_name" = "<RESOURCE_GROUP>" ] || \ | ||
| [ "$workspace_name" = "<WORKSPACE_NAME>" ]; then | ||
| echo "Please update the script with the subscription_id, resource_group_name and workspace_name" | ||
| exit 1 | ||
| fi | ||
|
|
||
| az account set -s $subscription_id | ||
| workspace_info="--resource-group $resource_group_name --workspace-name $workspace_name" | ||
|
|
||
| # 2. Check if the model exists in the registry | ||
| # need to confirm model show command works for registries outside the tenant (aka system registry) | ||
| if ! az ml model show --name $model_name --version $model_version --registry-name $registry_name | ||
| then | ||
| echo "Model $model_name:$model_version does not exist in registry $registry_name" | ||
| exit 1 | ||
| fi | ||
|
|
||
| # 3. Deploy the model to an endpoint | ||
| # create online endpoint | ||
| az ml online-endpoint create --name $endpoint_name $workspace_info || { | ||
| echo "endpoint create failed"; exit 1; | ||
| } | ||
|
|
||
| # deploy model from registry to endpoint in workspace | ||
| az ml online-deployment create --file deploy.yml $workspace_info --all-traffic --set \ | ||
| endpoint_name=$endpoint_name model=azureml://registries/$registry_name/models/$model_name/versions/$model_version \ | ||
| instance_type=$deployment_sku || { | ||
| echo "deployment create failed"; exit 1; | ||
| } | ||
|
|
||
| # 4. Try a sample scoring request | ||
|
|
||
| # Check if scoring data file exists | ||
| if [ -f $scoring_file ]; then | ||
| echo "Invoking endpoint $endpoint_name with following input:\n\n" | ||
| cat $scoring_file | ||
| echo "\n\n" | ||
| else | ||
| echo "Scoring file $scoring_file does not exist" | ||
| exit 1 | ||
| fi | ||
|
|
||
| az ml online-endpoint invoke --name $endpoint_name --request-file $scoring_file $workspace_info || { | ||
| echo "endpoint invoke failed"; exit 1; | ||
| } | ||
|
|
||
| # 6. Delete the endpoint | ||
| az ml online-endpoint delete --name $endpoint_name $workspace_info --yes || { | ||
| echo "endpoint delete failed"; exit 1; | ||
| } | ||
|
|
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22
...ndation-models/system/inference/visual-chat-completion/sample_chat_completions_score.json
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| { | ||
| "input_data": { | ||
| "input_string": [ | ||
| { | ||
| "role": "user", | ||
| "content": [ | ||
| { | ||
| "type": "image_url", | ||
| "image_url": { | ||
| "url": "https://www.ilankelman.org/stopsigns/australia.jpg" | ||
| } | ||
| }, | ||
| { | ||
| "type": "text", | ||
| "text": "What is shown in this image? Be extremely detailed and specific." | ||
| } | ||
| ] | ||
| } | ||
| ], | ||
| "parameters": { "temperature": 0.7, "max_new_tokens": 2048 } | ||
| } | ||
| } |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Use litellm with JAIS in Azure AI and Azure ML\n", | ||
| "\n", | ||
| "Use `litellm` to consume JAIS deployments in Azure AI and Azure ML. Notice that JAIS in Azure only supports chat completions API.\n", | ||
| "\n", | ||
| "> Review the [documentation](https://aka.ms/jais-azure-ai-studio-docs) for the JAIS 30b Chat model in AI Studio and for ML Studio for details on how to provision inference endpoints, regional availability, pricing and inference schema reference." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Prerequisites\n", | ||
| "\n", | ||
| "Before we start, there are certain steps we need to take to deploy the models:\n", | ||
| "\n", | ||
| "* Register for a valid Azure account with subscription \n", | ||
| "* Make sure you have access to [Azure AI Studio](https://learn.microsoft.com/en-us/azure/ai-studio/what-is-ai-studio?tabs=home)\n", | ||
| "* Create a project and resource group\n", | ||
| "* Select `Jais-30b-Chat` in the model catalog.\n", | ||
| "\n", | ||
| " > Notice that some models may not be available in all the regions in Azure AI and Azure Machine Learning. On those cases, you can create a workspace or project in the region where the models are available and then consume it with a connection from a different one. To learn more about using connections see [Consume models with connections](https://learn.microsoft.com/en-us/azure/ai-studio//deployments-connections)\n", | ||
| "\n", | ||
| "* Deploy with \"Serverless APIs\"\n", | ||
| "\n", | ||
| "Once deployed successfully, you should be assigned for an API endpoint and a security key for inference.\n", | ||
| "\n", | ||
| "For more information, you should consult Azure's official documentation [here](https://aka.ms/jais-azure-ai-studio-docs) for model deployment and inference.\n", | ||
| "\n", | ||
| "To complete this tutorial, you will need to:\n", | ||
| "\n", | ||
| "* Install `litellm`:\n", | ||
| "\n", | ||
| " ```bash\n", | ||
| " pip install litellm\n", | ||
| " ```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Example\n", | ||
| "\n", | ||
| "The following is an example about how to use `litellm` with a JAIS model deployed in Azure AI and Azure ML:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "name": "imports" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import litellm" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "You will need to have a Endpoint url and Authentication Key associated with that endpoint. This can be acquired from previous steps. To work with `litellm`, configure the client as follows:\n", | ||
| "\n", | ||
| "- `base_url`: Use the endpoint URL from your deployment. Include the `/v1` in the URL.\n", | ||
| "- `api_key`: Use your API key." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "name": "chat_client" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "client = litellm.LiteLLM(\n", | ||
| " base_url=\"https://<endpoint-name>.<region>.inference.ai.azure.com/v1\",\n", | ||
| " api_key=\"<key>\",\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Use the client to create chat completions requests:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "name": "chat_invoke" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "response = client.chat.completions.create(\n", | ||
| " messages=[{\"content\": \"List the emirates of the UAE.\", \"role\": \"user\"}],\n", | ||
| " model=\"openai\",\n", | ||
| " custom_llm_provider=\"custom_openai\",\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The generated text can be accessed as follows:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "name": "chat_response" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "print(response.choices[0].message.content)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Aditional resources\n", | ||
| "\n", | ||
| "Here are some additional reference: \n", | ||
| "\n", | ||
| "* [Plan and manage costs (marketplace)](https://learn.microsoft.com/azure/ai-studio//costs-plan-manage#monitor-costs-for-models-offered-through-the-azure-marketplace)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3.10 - SDK v2", | ||
| "language": "python", | ||
| "name": "python310-sdkv2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.11" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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