Interactive Python Notebook Setup for Data Science on Cloud9

Question

Pulumi · Accepted Answer

To set up an interactive Python notebook environment for data science purposes on AWS Cloud9, we'll create a Cloud9 environment using the EC2 instance option. AWS Cloud9 environments are cloud-based integrated development environments (IDEs) that let you write, run, and debug code with just a browser.

### AWS Cloud9 Environment Setup

With Pulumi, you can define infrastructure using general-purpose programming languages. To create a Cloud9 environment with an associated EC2 instance, we can use the `aws.cloud9.EnvironmentEC2` resource from the Pulumi AWS package.

Here's how you can create an AWS Cloud9 environment with Pulumi:

1. **Define the Cloud9 EC2 Environment**: We'll specify the desired name for our environment, the type of instance, and any other configuration that we might need.

2. **Configure the Environment**: Once the Cloud9 environment is created, one can start adding the desired Python libraries and packages that are common for data science tasks. This is typically done within the Cloud9 IDE terminal itself rather than through Pulumi.

3. **Access the Environment**: After creation, you'll be able to access your fresh Cloud9 environment through the AWS Management Console, where a browser-based IDE will be provided to you.

Below is a Pulumi Python program that accomplishes the creation of a Cloud9 EC2 environment:

```python
import pulumi
import pulumi_aws as aws

# Create a new AWS Cloud9 EC2 environment for data science purposes.
cloud9_environment = aws.cloud9.EnvironmentEC2("dataScienceEnvironment",
    instance_type="t2.medium",  # The type of instance to connect to the environment.
    auto_stop_time_minutes=30,  # Set automatic stop time to save resources.
    description="A Cloud9 environment for data science tasks.")

# Export the Cloud9 environment ID and URL for access.
pulumi.export('environment_id', cloud9_environment.id)
pulumi.export('environment_url', cloud9_environment.url)
```

In this program:

- We import the required Pulumi AWS package.
- Using `aws.cloud9.EnvironmentEC2`, we define a new Cloud9 environment. We've named it `dataScienceEnvironment` and selected `t2.medium` as the instance type, which should provide sufficient compute power for basic data science tasks. The `auto_stop_time_minutes` is set to 30 to automatically stop the instance when it's not being used, helping you save on AWS costs.
- After the Cloud9 environment is created, we export the environment's ID and URL, which can be used to access the Cloud9 IDE via the AWS Management Console.

### Post-setup Configuration

Once your Cloud9 environment has been provisioned, you will want to set up your Python environment:

1. Access the Cloud9 environment.
2. Open a terminal within the Cloud9 browser IDE.
3. Use package management tools like `pip` to install Python libraries for data science, such as `numpy`, `pandas`, `matplotlib`, `scikit-learn`, and others.
4. Optionally, install Jupyter or JupyterLab if you prefer working with Jupyter notebooks.

### Running the Program

After writing the above program to a file (let's say `cloud9_ds_env.py`), you can create your cloud infrastructure with Pulumi:

```sh
pulumi up
```

This command will prompt you to confirm the details before the infrastructure is provisioned.

Remember that this is a basic setup. Depending on your data science needs, you might need more powerful instance types, additional storage, or further configuration.