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Conda is a tool commonly used to manage dependencies and projects. You can create a conda environment for each project and install the packages that you need in that conda environment. If you make changes to one of your conda environments, your other environments are not affected. For more information, please refer to the Conda User Guide.

The following examples illustrate common conda tasks using JupyterLab in IDAS. 

Example: Create a conda environment with a Python kernel 

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2. In JupyterLab, click the "Terminal" tile under "Other" to start a Terminal session.Image Removed

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3. In Terminal, the conda create command can be used to create a new conda environment.

In this example, we will create a conda environment with Python 3.8 and name the environment

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py38

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.

conda create -n py38 python=3.8

When prompted 

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Proceed ([y]/n)?

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, press

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y

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to proceed.

Side notes:

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The conda create command can be modified to fit your needs. Below are a few additional examples:

Additional example 1: Install packages when creating an environment

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: 

conda create -n another-env python=3.8 numpy requests

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Additional example 2: Specify the versions of the packages

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: 

conda create -n another-env python=3.7 numpy=1.16.1 requests=2.19.1

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Additional example 3: Create a conda environment from an environment.yml file

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:

conda env create -f environment.yml

4. Once this conda environment has been created, we can activate it.

conda activate py38

5. The command prompt in your Terminal will change to indicate the active environment.

(py38) hawkid@idas-research-hawkid:~$

6. Next, we create a kernel in order to use Jupyter Notebook with this conda environment. Install the IPython kernel:

conda install ipykernel

When

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prompted Proceed ([y]/n)?

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, press

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y

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to proceed.

7. Now install a kernel in this environment:

python -m ipykernel install --user --name py38 --display-name "Python Project Vis"

The value for

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--name

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is used by Jupyter internally. Any existing kernel with the same

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--name

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value will be overwritten. The

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--display-name

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will be displayed in the Notebook menu in the JupyterLab Launcher page.

8. Go back to the JupyterLab Launcher page by pressing Ctrl+Shift+L (Windows) or Cmd+Shift+L (Mac). Under "Notebook", a new option for your kernel will now be available. In this example screenshot, the "Python Project Vis" kernel was just installed and now became available to use.

Click on that new option to start a notebook. In that notebook, you can use the packages that you installed in the conda environment.Image Removed

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9. To install Python packages to this conda environment using a Jupyter notebook:

a. Start a new Jupyter notebook by selecting the Python kernel you just installed. In this example, that is the "Python Project Vis" kernel in the previous step.

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10. We can also install Python packages to this conda environment using the Terminal:

conda install --name py38 pandas

Example: Create a conda environment with an R kernel 

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2. In JupyterLab, click the "Terminal" tile under "Other" to start a Terminal session.Image Removed

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3. In Terminal, the conda create command can be used to create a new conda environment.

In this example, we will create a conda environment with R and name the environment

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r-env

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.

conda create -n r-env r-essentials r-base

When prompted 

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Proceed ([y]/n)?

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, press

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y

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to proceed.

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4. Once this conda environment is created, we can activate it.

conda activate r-env

5. The command prompt in your Terminal will change to indicate the active environment.

(r-env) hawkid@idas-research-hawkid:~$

6. Next, we create a kernel in order to use Jupyter Notebook with this conda environment. We will install a kernel through IRkernel. The r-irkernel package was already installed with the r-essentials bundle earlier. You can check to make sure you have r-irkernel in your conda environment:

(r-env) hawkid@idas-research-hawkid:~$ conda list irkernel
# packages in environment at /home/hawkid/.conda/envs/r-env:
#
# Name                    Version                   Build  Channel
r-irkernel                0.8.15                    r36_0    defaults

7. Now install a kernel in R:

# launch R in Terminal
R
> library(IRkernel)
> IRkernel::installspec(name = "ir361", displayname = "R 3.6.1")

The value for

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name

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is used by Jupyter internally. Any existing kernel with the same

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name

...

value will be overwritten. The

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displayname

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will be displayed in the Notebook menu in JupyterLab.

Note that we specified the R version (3.6.1) in the "name" and "displayname". This helps remind us what R version we are using, especially if we have multiple conda environments and kernels.

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8. Go back to the JupyterLab Launcher page by pressing Ctrl+Shift+L (Windows) or Cmd+Shift+L (Mac). Under "Notebook", a new option for your kernel will now be available, with the name "R 3.6.1"

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10. Then you can load and use the package in Jupyter Notebook as usual. If you have an open notebook, you can use the package in the notebook right after you install the package in Terminal. You don't need to refresh the notebook.

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11. In general, any packages that are installed in the conda environment will be available to use in the notebook. To view a list of installed packages in the conda environment, type in Terminal:

conda list -n r-env

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12. To view a list of installed packages in Jupyter notebook, use the installed.packages() function:

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Other useful conda commands 

The following commands may be useful to manage conda environments. Type the following commands in Terminal. For more information, please refer to the "Managing environments" and "Managing packages" sections in the Conda User Guide.

• List all of your environments. In the output, your current environment will be marked with an asterisk (*).

  Code Block
  conda info --envs

  or

  Code Block
  conda env list

• To activate a conda environment that was previously created, type (without the angle brackets):

  Code Block
  conda activate <environment-name>

• Deactivate a conda environment once you are finished working with it:

  Code Block
  conda deactivate

  • Or, to return to the "base" environment, type (with no environment specified):

    Code Block
    conda activate

• View a list of packages installed in an environment:

  • If the environment is not activated, type (without the angle brackets):

    Code Block
    conda list -n <environment-name>

  • If the environment was already activated with

    "

    conda activate

    "

    earlier:

    Code Block
    conda list

• To see if a specific package is installed in an environment, type (without the angle brackets):

  Code Block
  conda list -n <environment-name> <package-name>

  • Or, if the environment was already activated with

    "

    conda activate

    "

    earlier, type (without the angle brackets):

    Code Block
    conda list <package-name>

• To install a package in an environment:

conda install --name <environment-name> <package-name>

• Remove an environment:

  Code Block
  conda remove --name <environment-name> --all

  or

  Code Block
  conda env remove --name <environment-name>

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If you have any questions or comments, please contact research-computing@uiowa.edu.