Manage your conda environment

This page describes how to manage a conda environment in your Vertex AI Workbench instance.

Overview

If you've added a conda environment to your Vertex AI Workbench instance, it appears as a kernel in your instance's JupyterLab interface.

You might have added a conda environment to your instance to use a kernel that isn't available in a default Vertex AI Workbench instance. This page describes how to modify and delete that kernel.

Open JupyterLab

1. In the Google Cloud console, go to the Instancespage.

   Go to Instances

2. Next to your Vertex AI Workbench instance's name, click Open JupyterLab.

   Your Vertex AI Workbench instance opens JupyterLab.

Modify a conda kernel

Vertex AI Workbench instances come with pre-installed frameworks such as PyTorch and TensorFlow. If you need a different version, you can modify the libraries by using pip in the relevant conda environment.

For example, if you want to upgrade PyTorch:

 # Check the name of the conda environment for PyTorch 
 conda 
 env 
 list 
 # Activate the environment for PyTorch 
 conda 
 activate 
 pytorch 
 # Display the PyTorch version 
 python 
 - 
 c 
 "import torch; print(torch.__version__)" 
 # Make sure to use pip from the conda environment for PyTorch 
 # This should be `/opt/conda/envs/pytorch/bin/pip` 
 which 
 pip 
 # Upgrade PyTorch 
 pip 
 install 
 -- 
 upgrade 
 torch 

Delete a conda kernel

Some conda packages add default kernels to your environment when the packages are installed. For example, when you install R, conda might also add a python3 kernel. This can cause a duplication of kernels in your environment. To avoid duplicated kernels, delete the default kernel before you create a new kernel with the same name.

rm -rf /opt/conda/envs/ CONDA_ENVIRONMENT_NAME 
/share/jupyter/kernels/python3

Troubleshoot

To diagnose and resolve issues related to managing a conda environment in your Vertex AI Workbench instance, see Troubleshooting Vertex AI Workbench .

What's next

• Learn more about conda .