How to configure specific Python environment with Jupyter notebook?

/ / Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Working with Jupyter

Configuring a specific Python environment for use with Jupyter Notebook is a fundamental practice in data science, machine learning, and artificial intelligence workflows, particularly when leveraging Google Cloud Machine Learning (AI Platform) resources. This process ensures reproducibility, dependency management, and isolation of project environments. The following comprehensive guide addresses the configuration steps, rationale, and best practices for creating and integrating a Python environment with Jupyter Notebook, with a focus on practical application within the context of machine learning on cloud-based infrastructure.

1. Understanding Python Environments

A Python environment is an isolated workspace that allows users to install and manage packages independently of the system-wide Python installation. This isolation is vital for managing dependencies for different projects, avoiding version conflicts, and ensuring that development and production environments remain consistent.

Common tools for creating isolated Python environments include:
– virtualenv: Creates lightweight Python environments.
– venv: The standard library module for creating virtual environments (Python 3.3+).
– conda: A package, dependency, and environment manager that supports multiple languages.

For machine learning projects on Google Cloud, `virtualenv` and `conda` are widely used, with `conda` often preferred for its ease in managing both Python and non-Python dependencies.

2. Creating a Python Environment

Using virtualenv

1. Install virtualenv (if not present):

bash
   pip install virtualenv

2. Create a new environment:

bash
   virtualenv my_ml_env

3. Activate the environment:
– On Linux/macOS:

bash
     source my_ml_env/bin/activate

– On Windows:

bash
     my_ml_env\Scripts\activate

4. Verify activation:
The shell prompt changes to indicate the active environment (e.g., `(my_ml_env)`).

Using conda

1. Create a new environment with a specific Python version:

bash
   conda create -n my_ml_env python=3.10

2. Activate the environment:

bash
   conda activate my_ml_env

3. List environments (optional):

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3. Installing Required Packages


After activating the desired environment, install necessary packages, such as `jupyter`, machine learning libraries (`scikit-learn`, `tensorflow`, `pandas`, etc.), and any other dependencies.

Example with pip:

bash
pip install jupyter numpy pandas scikit-learn matplotlib

Example with conda:
bash
conda install jupyter numpy pandas scikit-learn matplotlib

For GPU support (e.g., TensorFlow-GPU), specify the appropriate versions:
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4. Integrating the Environment with Jupyter Notebook


To use the newly created environment as a Jupyter kernel, the `ipykernel` package must be installed within the environment. This enables Jupyter Notebook to recognize and launch kernels with the exact dependencies and Python version specified.

1. Install ipykernel in the active environment:
   
bash
   pip install ipykernel
   
 or
bash
   conda install ipykernel
   
2. Create a new Jupyter kernel for the environment:
bash
   python -m ipykernel install --user --name my_ml_env --display-name "Python (my_ml_env)"
   
 - `--user`: Installs the kernel for the current user.
 - `--name`: Internal identifier for the kernel.
 - `--display-name`: The name shown in Jupyter Notebook's kernel selection menu.
3. Verify kernel installation:
 Launch Jupyter Notebook:
bash
   jupyter notebook
   
 Under "Kernel" > "Change kernel", "Python (my_ml_env)" should appear as an option.
5. Example Workflow
Step 1: Create and activate environment
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Step 2: Install packages


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Step 3: Add environment as Jupyter kernel


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Step 4: Start Jupyter Notebook and select kernel


bash
jupyter notebook

- In the notebook interface, select "Kernel" > "Change Kernel" > "Python (GCP ML Env)".
Step 5: Run code in the isolated environment
python
import sys
import tensorflow as tf
print(sys.executable)
print(tf.__version__)

- This verifies that your notebook is running in the intended environment and with the appropriate package versions.
6. Managing Multiple Environments
When working on multiple machine learning projects with differing dependencies or Python versions, repeat the process above for each project. Each environment should be independently created, activated, and registered as a Jupyter kernel.
To remove a Jupyter kernel:
bash
jupyter kernelspec uninstall <kernel_name>

To remove a conda environment:
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7. Using Environments with Google Cloud Machine Learning


Google Cloud’s AI Platform Notebooks allow users to launch JupyterLab or Jupyter Notebook servers on customizable virtual machine instances. These instances can be further configured via SSH or terminal access to create and manage custom Python environments as described above.

Best Practice:
- Use startup scripts or Docker containers to automate environment setup on Google Cloud instances for consistent reproducibility.
- For advanced isolation, consider using Docker containers with Jupyter Notebook and the required environment pre-installed.

8. Exporting and Sharing Environments


To share environments or ensure reproducibility:

With pip (virtualenv or venv)


Export:

bash
pip freeze > requirements.txt

Import:
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With conda


Export:

bash
conda env export > environment.yml

Import:
bash
conda env create -f environment.yml

Sharing the `requirements.txt` or `environment.yml` file alongside your Jupyter notebooks allows collaborators to recreate the exact environment.
9. Troubleshooting Common Issues
- Kernel Not Appearing: Ensure `ipykernel` is installed in the active environment and that `python -m ipykernel install` has been executed.
 - Dependency Conflicts: Use virtual environments or conda environments to avoid version mismatches.
 - GPU Support: Install the correct versions of libraries (e.g., TensorFlow-GPU) and verify CUDA/cuDNN installation.
 - Cloud Permissions: When working on Google Cloud, ensure sufficient permissions to install packages and create environments.
10. Best Practices and Recommendations
- Isolate environments per project to prevent dependency conflicts and improve reproducibility.
 - Pin package versions in your environment files for exact replication.
 - Regularly update environments and test with your code to ensure compatibility.
 - Document environment setup steps in project documentation.
11. Example: Full Configuration on Google Cloud AI Platform Notebook
Suppose a user is working on a machine learning project that requires TensorFlow 2.9, scikit-learn 1.1, and pandas 1.4, and the project is hosted on a Google Cloud AI Platform Notebook. The steps might be:
1. Open a Terminal in the JupyterLab interface.
 2. Create a new conda environment:
bash
   conda create -n mygcpml python=3.8
   conda activate mygcpml
   
3. Install required packages:
bash
   conda install tensorflow=2.9 scikit-learn=1.1 pandas=1.4 ipykernel
   
4. Add the environment as a Jupyter kernel:
bash
   python -m ipykernel install --user --name mygcpml --display-name "Python (My GCP ML)"
   
5. Restart JupyterLab/Notebook and select the new kernel.
 6. Verify environment:
python
   import tensorflow as tf
   import sklearn
   import pandas as pd
   print(tf.__version__, sklearn.__version__, pd.__version__)
   
This approach ensures that experiments, models, and data analysis are run in a controlled, reproducible, and isolated environment, minimizing the likelihood of "works on my machine" issues and facilitating cloud-based collaboration.
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