How to Run Jupyter Notebook on GPUs

Jupyter Notebook is a popular open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It is widely used by data scientists and machine learning engineers for data exploration, experimentation, and prototyping. However, as the size and complexity of data sets and models increase, the computational demands also increase, making it necessary to use more powerful hardware such as GPUs (Graphics Processing Units) to accelerate the computations. In this blog post, we will show you how to run Jupyter Notebook on GPUs using Anaconda, a popular distribution of Python and its libraries.

Looking to run your Jupyter Notebooks on GPUs but don’t know where to start? Saturn Cloud can provide the high-performance GPU compute power you need. Request a free demo to learn more. and supercharge your data science workflows.

Table of Contents

1. Step-by-Step Running Jupyter Notebook on GPUs
2. Pros and Cons of GPU Acceleration
3. Common Errors and Troubleshooting
4. Conclusion

Step-by-Step Running Jupyter Notebook on GPUs

Step 1: Install Anaconda

Anaconda is a free and open-source distribution of Python and its libraries that simplifies the installation and management of packages, dependencies, and environments. It also includes Jupyter Notebook and other useful tools for data science and machine learning. You can download and install Anaconda from its official website: https://www.anaconda.com/products/distribution.

Once you have installed Anaconda, you can launch Jupyter Notebook from the Anaconda Navigator or from the command line by typing [jupyter notebook](https://saturncloud.io/glossary/jupyter-notebook).

Step 2: Install CUDA Toolkit

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA that allows GPUs to be used for general-purpose computing. To use GPUs with Jupyter Notebook, you need to install the CUDA Toolkit, which includes the drivers, libraries, and tools needed to develop and run CUDA applications. You can download the CUDA Toolkit from NVIDIA’s website: https://developer.nvidia.com/cuda-downloads.

Make sure to select the version of CUDA Toolkit that matches your GPU and operating system. You can check your GPU’s specifications by running the nvidia-smi command in the terminal.

Step 3: Install cuDNN Library

cuDNN (CUDA Deep Neural Network) is a library developed by NVIDIA that provides optimized primitives for deep neural networks. It can significantly speed up the training and inference of deep learning models on GPUs. To use cuDNN with Jupyter Notebook, you need to download and install the cuDNN library from NVIDIA’s website: https://developer.nvidia.com/cudnn.

Make sure to select the version of cuDNN that matches your CUDA Toolkit version and operating system.

Step 4: Create a Conda Environment

A Conda environment is a self-contained directory that contains a specific version of Python and its libraries. It allows you to isolate and manage different versions of packages and dependencies for different projects. To create a Conda environment for running Jupyter Notebook on GPUs, you can use the following command:

conda create --name gpu_env [python](https://saturncloud.io/glossary/python)=3.8

This command creates a new Conda environment called gpu_env with Python 3.8 installed. You can replace gpu_env with any name you prefer.

Step 5: Install Required Packages

To use GPUs with Jupyter Notebook, you need to install some additional packages that provide the interface between Python and CUDA. You can install these packages using the following command:

conda install -c anaconda tensorflow-gpu keras-gpu

This command installs the latest version of TensorFlow and Keras that support GPU acceleration. You can also install other packages that you need for your project, such as NumPy, Pandas, Matplotlib, etc.

Step 6: Configure Jupyter Notebook

To configure Jupyter Notebook to use GPUs, you need to create a new kernel that uses the Conda environment you created earlier and specifies the GPU device. You can do this by running the following command:

python -m ipykernel install --user --name gpu_env --display-name "Python (GPU)"

This command installs a new kernel called “Python (GPU)” that uses the gpu_env Conda environment and specifies the GPU device.

Step 7: Launch Jupyter Notebook

Now that you have installed and configured all the necessary components, you can launch Jupyter Notebook and start using GPUs for your computations. To launch Jupyter Notebook, you can use the following command:

jupyter notebook

This command opens Jupyter Notebook in your default web browser. You can create a new notebook and select the “Python (GPU)” kernel from the dropdown menu. You can then write and run your code as usual, and it will be executed on the GPU device.

Looking to run your Jupyter Notebooks on GPUs but don’t know where to start? Saturn Cloud can provide the high-performance GPU compute power you need. Request a free demo to learn more. and supercharge your data science workflows.

Step 8: Verify that Jupyter Notebook is using GPU

import torch
torch.cuda.is_available()

Output:

True

The output indicates that torch is using GPU, meaning that the current Notebook is GPU-enabled.

Pros and Cons of GPU Acceleration

Pros:

• Faster Model Training: GPU acceleration significantly speeds up training times for deep learning models.
• Handling Larger Datasets: GPUs enable the processing of larger datasets that might not fit into memory.
• Parallel Processing Capabilities: Many deep learning operations can be parallelized, leading to efficient use of GPU resources.

Cons:

• Increased Power Consumption: Running computations on GPUs consumes more power compared to CPUs.
• Compatibility Issues: Some GPU models may not be compatible with certain deep learning frameworks.
• Limited GPU Memory: Extremely large models may encounter memory limitations on GPUs.

Common Errors and Troubleshooting

Error 1: CUDA Out-of-Memory Error

If you encounter a CUDA out-of-memory error, consider the following solutions:

• Reduce batch size.
• Optimize model architecture for lower memory usage.
• Utilize mixed-precision training.

Error 2: Incompatible CUDA Version

If you face compatibility issues, ensure that your CUDA version aligns with the PyTorch version. Upgrade CUDA and PyTorch accordingly:

conda install -y -c conda-forge cudatoolkit=<desired_version>
conda install -y pytorch torchvision torchaudio cudatoolkit=<desired_version> -c pytorch

Handling Data Movement Efficiently

Efficient data transfer between CPU and GPU is crucial for optimal performance. Use PyTorch’s to() and cuda() functions wisely:

# Moving data to GPU
x = torch.rand(1000, 1000)
x_gpu = x.to('cuda')
# or
x_gpu = x.cuda()

# Moving data back to CPU
x_cpu = x_gpu.to('cpu')

Conclusion

In this blog post, we have shown you how to run Jupyter Notebook on GPUs using Anaconda, CUDA Toolkit, and cuDNN library. By following these steps, you can take advantage of the power and speed of GPUs for your data science and machine learning projects. We hope that this tutorial has been helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.

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