Add a conda environment

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

Overview

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

You might add a conda environment to your Vertex AI Workbench instance to use kernels that aren't available in Vertex AI Workbench instances. For example, you can add conda environments for R and Apache Beam. Or you can add conda environments for specific older versions of the available frameworks, such as TensorFlow, PyTorch, or Python.

Before you begin

If you haven't already, create a Vertex AI Workbench instance .

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.

Add a conda environment

You can add a conda environment by entering commands in your instance's JupyterLab terminal.

1. In JupyterLab, select File  > New  > Terminal.

2. In the Terminalwindow, enter the following commands:

   #  
   Creates  
   a  
   conda  
   environment.
   conda  
   create  
   -n  
    CONDA_ENVIRONMENT_NAME 
     
   -y
   conda  
   activate  
    CONDA_ENVIRONMENT_NAME 
   #  
   Install  
   packages  
   using  
   a  
   pip  
   local  
   to  
   the  
   conda  
   environment.
   conda  
   install  
   pip
   pip  
   install  
    PACKAGE 
   #  
   Adds  
   the  
   conda  
   kernel.
   DL_ANACONDA_ENV_HOME=" ${ 
    DL_ANACONDA_HOME 
    } 
   /envs/ CONDA_ENVIRONMENT_NAME 
   "
   python  
   -m  
   ipykernel  
   install  
   --prefix  
   " ${ 
    DL_ANACONDA_ENV_HOME 
    } 
   "  
   --name  
    CONDA_ENVIRONMENT_NAME 
     
   --display-name  
    KERNEL_DISPLAY_NAME 
   

   Replace the following:

   • CONDA_ENVIRONMENT_NAME : your choice of name for the environment
   • PACKAGE : the package that you want to install
   • KERNEL_DISPLAY_NAME : the display name for the tile of the kernel in the JupyterLab interface

3. A default kernel can be created when installing to a given conda environment. You can remove the default kernel with the following command:

   rm -rf "/opt/micromamba/envs/ CONDA_ENVIRONMENT_NAME 
   /share/jupyter/kernels/python3

4. To see your new kernel, do the following:

   1. Refresh the page.

   2. Select File  > New Launcher.

   The kernel is listed among the others in the Launcherwindow.

By default, conda might use pip packages in the system pip folder (for example, /usr/bin/pip ). Running conda install pip ensures that the setup uses a pip local to the environment.

Example installation: R Essentials

The following example installs R Essentials in a conda environment named r .

conda create -n r
conda activate r
conda install -c r r-essentials

Example installation: pip package

The following example installs pip packages from a requirements.txt file.

conda  
create  
-n  
myenv
conda  
activate  
myenv
conda  
install  
pip
pip  
install  
-r  
requirements.txt
DL_ANACONDA_ENV_HOME=" ${ 
 DL_ANACONDA_HOME 
 } 
/envs/myenv"
python  
-m  
ipykernel  
install  
--prefix  
" ${ 
 DL_ANACONDA_ENV_HOME 
 } 
"  
--name  
myenv  
--display-name  
myenv

Troubleshoot

To diagnose and resolve issues related to adding a conda environment, see Troubleshooting Vertex AI Workbench .

What's next

• Learn more about conda .

• To modify your conda environment, see Manage your conda environment .