Installing AI/ML Python Libraries & Packages in Jupyter Notebook: A Guide to Optimizing Performance

5 min readJul 3, 2023

Introduction

Artificial Intelligence (AI) and Machine Learning (ML) have become integral parts of many industries, revolutionizing the way we solve complex problems and extract valuable insights from data. Python, with its extensive ecosystem of AI/ML libraries and frameworks, has emerged as the language of choice for developing intelligent applications. Jupyter Notebook, with its interactive and collaborative environment, provides a convenient platform for exploring and experimenting with AI/ML algorithms.

In this blog, we will guide you through the process of installing and optimizing the performance of AI/ML-related Python libraries in Jupyter Notebook. We will focus on some popular libraries such as NumPy, TensorFlow, PyTorch, and Pandas, which are essential for various AI/ML tasks, including data preprocessing, model building, and evaluation.

Note

If you are looking to quickly set up and explore AI/ML & Python Jupyter Notebook Kit, Techlatest.net provides an out-of-the-box setup for AI/ML & Python Jupyter Notebook Kit on AWS, Azure, and GCP. Please follow the below links for the step-by-step guide to set up AI/ML & Python Jupyter Notebook Kit on your choice of cloud platform.

For AI/ML KIT: AWS, GCP & Azure.

Features of AI/ML Kit & Python Jupyter Notebook provided by Techlatest.net

In-browser editing of code
Ability to run and execute code in various programming languages
Supports rich media outputs like images, videos, charts, etc.
Supports connecting to external data sources
Supports collaborative editing by multiple users
Simple interface to create and manage notebooks
Ability to save and share notebooks

For AI/ML features in Jupyter Notebooks, some common ones include:

Integration with popular machine learning libraries like sci-kit-learn, TensorFlow, Keras, etc.
Ability to develop and test machine learning models
Ability to visualize data and results using plots, charts, etc.
Hyperparameter tuning
Model training and evaluation

Step-by-Step Guide to Installing AI/ML Python Libraries & Packages in Jupyter Notebook

During VM selection “We are selecting GPU instance by going to GPU tab and selecting the desired GPU instance type. GPU instance will give 10 to 15 times better performance compared to equivalent CPU instance, however, GPU instances will have a significantly higher cost, so choose the right instance type for your performance and budget requirement”.

I take all three examples of AWS, GCP & Azure for Your Reference.

After setup the VM, we can Install AI/ML Python Libraries & Packages in Jupyter Notebook, so below you can see step by step guide.

Step 1

This VM comes with the default Ubuntu as an admin user. So to access the Web UI and to install additional packages, log in with Ubuntu user and the password you set during the first login to the Jypyter Notebook.

Step 2

Open a Terminal in your Jupyter Notebook and enter the below command to install the there package using pip.

sudo -E pip install there

Note: Don’t forget to use sudo in the above command.

Step 3

Conda lets you install new languages (such as new versions of Python, node, R, etc) as well as packages in those languages. For lots of scientific software, installing with Conda is often simpler & easier than installing with pip — especially if it links to C / Fortran code.

Install a package using Conda with the below command.

sudo -E conda install -c conda-forge seaborn

Step 4

The packages are seaborn and there are now available to all users in JupyterHub. If a user already had a Python notebook running, they have to restart their notebook’s kernel to make the new libraries available.

Step 5

Alternatively, you can install the packages from jupyter notebook itself. Open a new jupyter notebook by clicking the new dropdown and selecting Python 3 (ipykernel)from the top right corner, Run below pip installation as

!sudo pip install matplotlib

Step 6

Install the conda package!

!sudo conda install -c conda-forge gdal

The user environment is a conda environment set up in /opt/tljh/user, with a python3 kernel as the default. It is readable by all users, but writeable only by users who have root access. This makes it possible for JupyterHub admins (who have root access to sudo) to install the software in the user environment easily.

Optimizing Performance

Installing the libraries is just the first step. To optimize performance in Jupyter Notebook, consider the following tips:

Efficient Data Loading: When working with large datasets, consider using libraries like Dask or Vaex, which can handle out-of-memory datasets efficiently. This allows you to work with datasets that don’t fit into memory.

GPU Acceleration: If you have a compatible GPU, configure TensorFlow or PyTorch to leverage GPU acceleration. This significantly speeds up training and inference for deep learning models.

Batch Processing: For computationally intensive tasks, process data in batches rather than one sample at a time. This minimizes overhead and improves efficiency.

Algorithmic Optimization: Explore optimized algorithms and techniques specific to your AI/ML tasks. This can involve implementing more efficient data structures or utilizing specialized libraries for specific operations.

Hardware Scaling: Consider utilizing cloud-based solutions or distributed computing frameworks like Apache Spark to scale your AI/ML workflows across multiple machines or clusters. This allows you to handle larger datasets and achieve faster training and inference times.

Conclusion

In summary, installing the necessary AI/ML libraries is just the beginning. There are several techniques to optimize the performance of AI/ML workflows in Jupyter Notebook — from efficient data loading and GPU acceleration to algorithmic and hardware optimizations. Following these tips can help speed up model training, inference, and overall AI/ML processes.