Getting Started with Code Interpreter Azure for Efficient Development

Code Interpreter Azure is a cloud-based service that allows you to run and manage code in a serverless environment. It's a game-changer for developers who want to speed up their development process.

Azure provides a wide range of languages and frameworks to choose from, including Python, Java, and Node.js. This means you can use the language and tools you're already familiar with.

To get started with Code Interpreter Azure, you'll need to create an Azure account and set up a resource group. This will give you a dedicated space to manage your code and resources.

With Code Interpreter Azure, you can write code in a text editor or IDE and run it directly in the cloud. This eliminates the need for local setup and allows you to focus on writing code.

To get started with Azure's code interpreter, you'll need to have a few things in place. First and foremost, you'll need an Azure account with an active subscription.

You'll also need to install the Azure CLI, which will give you access to a range of tools and features for managing your Azure resources.

In addition to the Azure CLI, you'll need to have Git installed on your computer. This is a version control system that will help you manage your code and collaborate with others.

Finally, make sure you have Python 3.10 or later installed on your system. This will be the language used to run your code in Azure.

Here's a quick rundown of the prerequisites:

To set up and configure the code interpreter Azure, you'll need to create a Python virtual environment and activate it. This is done by running `python3.11 -m venv .venv` and then `source .venv/bin/activate` (or `.venv\Scripts\activate` on Windows).

You'll also need to install the required Python packages using `python -m pip install -r requirements.txt`. This will ensure that the necessary tools are available for the code interpreter to function correctly.

To run the app, you'll need to configure environment variables, specifically giving yourself the Cognitive Services OpenAI User role on the Azure OpenAI account and the Azure ContainerApps Session Executor role on the session pool.

Here are the steps to create a Python virtual environment:

To configure the app, you'll need to create a Python virtual environment and activate it. This is done by running the command `python3.11 -m venv .venv` followed by `source .venv/bin/activate` (or `.venv\Scripts\activate` on Windows). It's recommended to use Python 3.10 or later for optimal performance.

You'll also need to install the required Python packages by running `python -m pip install -r requirements.txt`. This will ensure that all necessary dependencies are installed and ready to use.

To run the app, you'll need to configure environment variables. This includes setting up Azure services authentication using DefaultAzureCredential. On your local machine, this will use your current Azure CLI login credentials.

To authenticate with Azure services, you'll need to give yourself the Cognitive Services OpenAI User role on the Azure OpenAI account and the Azure ContainerApps Session Executor role on the session pool.

Here's a step-by-step guide to configuring environment variables:

Tools can be a bit overwhelming, but don't worry, I've got you covered. You can force the use of a specific tool by adding the tool_choice parameter, which can be used to choose from tools like file_search, code_interpreter, or even a custom function.

You can access up to 128 tools, including the code interpreter and file search, but you can also define your own custom tools via functions.

Here are some tools you can use:

By using the tool_choice parameter, you can ensure that a specific tool is used in a particular run, making it easier to manage your workflow.

To create an assistant, you'll need to enable it to access the code interpreter tool by adding "tools=[{"type": "code_interpreter"}]" to the model's configuration.

This gives the model a sandboxed Python environment to run and execute code, helping it formulate responses to user questions.

In the instructions, remind the model that it can execute code, especially if you know you want to use a particular library to generate a certain response.

For example, you can say "Use Matplotlib to do x" to guide the model towards the right tool.

Note that the value you enter for model= must match the deployment name in Azure OpenAI.

Here are the key steps to create an assistant:

To deploy your code interpreter to Azure, you'll need to create a container image and push it to a container registry. This can be done using the `az containerapp up` command, which combines the steps of building and deploying the app into a single command.

To use this command, you'll need to set the variables for the Container Apps environment and app name, such as `ENVIRONMENT_NAME=aca-sessions-tutorial-env` and `CONTAINER_APP_NAME=chat-api`. You'll also need to specify the Azure OpenAI account endpoint and session pool management endpoint as environment variables.

Here are the steps to deploy your app to Azure Container Apps:

Connecting to a remote compute instance and using it as a remote Jupyter server is seamless thanks to Azure Machine Learning's strong Jupyter notebooks support in VS Code.

This means you can access your remote Jupyter server directly from VS Code, without the need for additional setup or configuration.

For more information, see Configure a compute instance as a remote notebook server.

The code interpreter tool is used to execute Python commands when you need to perform calculations or computations in a Session. It takes a valid Python command as input and returns the result, stdout, and stderr.

The tool is available in Azure Code Interpreter tool spec, which leverages Azure Dynamic Sessions to execute Python code. You can use the code_interpreter function to execute Python code, and it will return a dictionary containing the result, stdout, and stderr.

The code_interpreter function also has a sanitize_input parameter, which defaults to True. If set to True, the input Python code is sanitized before execution. Additionally, the function has a local_save_path parameter, which allows you to specify a local path to save files generated by the Python interpreter.

Here's a table summarizing the parameters of the code_interpreter function:

The Code Interpreter is a powerful tool that allows you to execute Python commands directly in a session. It's designed to help with calculations and computations.

This tool can be used to run a wide range of Python code, from simple math operations to complex data analysis. The Code Interpreter takes in a string of Python code as input and returns the result, stdout, and stderr.

You can use the Code Interpreter by passing in a valid Python command as a string. The tool will then execute the code and return the output. The Code Interpreter is a great way to quickly test out code snippets or perform calculations without having to set up a full Python environment.

One of the key features of the Code Interpreter is its ability to sanitize input. This means that any malicious code will be caught and prevented from executing. The tool also has a built-in access token provider that ensures secure authentication with the session pool.

The Code Interpreter can be used in conjunction with other tools, such as the File Search tool, to create a powerful workflow for data analysis and manipulation.

Here are some key parameters for the Code Interpreter:

These parameters are used to configure the Code Interpreter and ensure that it runs securely and efficiently.

The Code Interpreter is available in all regions supported by Azure OpenAI Assistants.

You can find the most up-to-date information on regions and models where Assistants are currently supported by checking the models page.

Code Interpreter support is not limited to a specific set of regions or models, making it a versatile tool for developers.

The models page is the best resource for determining the current support status of regions and models for Azure OpenAI Assistants.