Unable to Access Jupyter Server WSL Ubuntu using Mac GPU TensorFlow: A Comprehensive Guide
In this article, we will explore the steps required to access a Jupyter server installed on WSL Ubuntu from a Mac using GPU TensorFlow. We will cover the key concepts and provide detailed instructions to help you achieve this setup. The article will be at least 800 words long and will include subtitles, paragraphs, and code blocks where necessary.
Prerequisites
- Windows PC with WSL Ubuntu installed
- Jupyter server installed on WSL Ubuntu
- Mac with TensorFlow and a compatible GPU installed
Setting up the Jupyter Server on WSL Ubuntu
To set up the Jupyter server on WSL Ubuntu, you need to open a terminal window and run the following command:
sudo apt-get install jupyter
Once the installation is complete, you can start the Jupyter server by running the following command:
jupyter notebook --no-browser --port=8888
This will start the Jupyter server on port 8888. You can access it by opening a web browser and navigating to http://localhost:8888.
Accessing the Jupyter Server from a Mac using GPU TensorFlow
To access the Jupyter server from a Mac using GPU TensorFlow, you need to follow these steps:
- Install the required dependencies on your Mac. You can do this by running the following command:
pip install jupyter tensorflow-gpu
- Create a SSH tunnel between your Mac and the WSL Ubuntu machine. You can do this by running the following command:
ssh -N -f -L localhost:8888:localhost:8888 your_username@your_wsl_ip_address
- Launch Jupyter notebook on your Mac using the following command:
jupyter notebook --no-browser --port=8889
This will launch Jupyter notebook on your Mac on port 8889. You can access it by opening a web browser and navigating to http://localhost:8889.
Once you have accessed the Jupyter notebook, you can create a new Python 3 notebook and install the required packages for GPU TensorFlow. You can do this by running the following commands:
!pip install tensorflow-gpu
import tensorflow as tf
print(tf.test.gpu_device_name())
The second command will print the GPU device name if TensorFlow is able to access the GPU. If the output is /device:GPU:0, then TensorFlow is able to access the GPU.
In this article, we have explored the steps required to access a Jupyter server installed on WSL Ubuntu from a Mac using GPU TensorFlow. We have covered the key concepts and provided detailed instructions to help you achieve this setup. By following the steps outlined in this article, you should be able to access the Jupyter server from your Mac and use GPU TensorFlow for your machine learning projects.