Customize environment for runtime

Customize environment with docker context for runtime

This section assumes you have knowledge of Docker and Azure Machine Learning environments.

Step-1: Prepare the docker context

Create image_build folder

In your local environment, create a folder contains following files, the folder structure should look like this:

|  |--requirements.txt
|  |--Dockerfile
|  |--environment.yaml

Define your required packages in requirements.txt

Optional: Add packages in private pypi repository.

Using the following command to download your packages to local: pip wheel <package_name> --index-url=<private pypi> --wheel-dir <local path to save packages>

Open the requirements.txt file and add your extra packages and specific version in it. For example:

###### Requirements with Version Specifiers ######
langchain == 0.0.149        # Version Matching. Must be version 0.6.1
keyring >= 4.1.1            # Minimum version 4.1.1
coverage != 3.5             # Version Exclusion. Anything except version 3.5
Mopidy-Dirble ~= 1.1        # Compatible release. Same as >= 1.1, == 1.*
<path_to_local_package>     # reference to local pip wheel package

You can obtain the path of local packages using ls > requirements.txt.

Define the Dockerfile

Create a Dockerfile and add the following content, then save the file:

FROM <Base_image>
COPY ./* ./
RUN pip install -r requirements.txt


This docker image should be built from prompt flow base image that is<newest_version>. If possible use the latest version of the base image.

Step 2: Create custom Azure Machine Learning environment

Define your environment in environment.yaml

In your local compute, you can use the CLI (v2) to create a customized environment based on your docker image.



Prompt flow is not supported in the workspace which has data isolation enabled. The enableDataIsolation flag can only be set at the workspace creation phase and can't be updated.

Prompt flow is not supported in the project workspace which was created with a workspace hub. The workspace hub is a private preview feature.

az login(optional)
az account set --subscription <subscription ID>
az configure --defaults workspace=<Azure Machine Learning workspace name> group=<resource group>

Open the environment.yaml file and add the following content. Replace the <environment_name> placeholder with your desired environment name.

name: <environment_name>
  path: .

Run CLI command to create an environment

cd image_build
az login(optional)
az ml environment create -f environment.yaml --subscription <sub-id> -g <resource-group> -w <workspace>


Building the image may take several minutes.

Go to your workspace UI page, then go to the environment page, and locate the custom environment you created. You can now use it to create a runtime in your prompt flow. To learn more, see Create compute instance runtime in UI.

To learn more about environment CLI, see Manage environments.

Create a custom application on compute instance that can be used as prompt flow runtime

A prompt flow runtime is a custom application that runs on a compute instance. You can create a custom application on a compute instance and then use it as a prompt flow runtime. To create a custom application for this purpose, you need to specify the following properties:

Docker image ImageSettings.reference Image used to build this custom application
Target port Port where you want to access the application, the port inside the container
published port EndpointsSettings.published Port where your application is running in the image, the publicly exposed port

Create custom application as prompt flow runtime via SDK v2

# import required libraries
import os
from import MLClient
from import WorkspaceConnection
# Import required libraries
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential

    credential = DefaultAzureCredential()
    # Check if given credential can get token successfully.
except Exception as ex:
    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work
    credential = InteractiveBrowserCredential()

from import ComputeInstance 
from import CustomApplications, ImageSettings, EndpointsSettings, VolumeSettings 

ml_client = MLClient.from_config(credential=credential)

image = ImageSettings(reference='<newest_version>') 

endpoints = [EndpointsSettings(published=8081, target=8080)]

app = CustomApplications(name='promptflow-runtime',endpoints=endpoints,bind_mounts=[],image=image,environment_variables={}) 

ci_basic_name = "<compute_instance_name>"

ci_basic = ComputeInstance(name=ci_basic_name, size="<instance_type>",custom_applications=[app]) 



Change newest_version, compute_instance_name and instance_type to your own value.

Create custom application as prompt flow runtime via Azure Resource Manager template

You can use this Azure Resource Manager template to create compute instance with custom application.

Deploy To Azure

To learn more, see Azure Resource Manager template for custom application as prompt flow runtime on compute instance.

Create custom application as prompt flow runtime via Compute instance UI

Follow this document to add custom application.

Screenshot of compute showing custom applications.

Leverage requirements.txt in flow folder to dynamic your environment - quick test only

In promptflow flow.dag.yaml, you can also specify define requirements.txt, which will be used when you deploy your flow as deployment.

Screenshot of flow dag yaml file showing requirements txt file.

Add packages in private pypi repository - optional

Use the following command to download your packages to local: pip wheel <package_name> --index-url=<private pypi> --wheel-dir <local path to save packages>

Create a python tool to install requirements.txt to runtime

Screenshot of python tool showing how to add custom packages.

from promptflow import tool

import subprocess
import sys

# Run the pip install command
def add_custom_packages():
    subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])

import os
# List the contents of the current directory
files = os.listdir()
# Print the list of files

# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Please update the function name/signature per need

# In Python tool you can do things like calling external services or
# pre/post processing of data, pretty much anything you want

def echo(input: str) -> str:
    return files

We would recommend to put the common packages (include private wheel) in the requirements.txt when building the image. Put the packages (include private wheel) in flow folder that are only used in flow or change more rapidly in the requirements.txt in the flow folder, the later approach is not recommended for production.

Next steps