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
In the Google Cloud console, go to the Instances page.
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.