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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
1. First, log in to the IDAS research instance with Python. If you are a student in a class that uses Python in IDAS, follow the instructions here to access to access your class instance.
22a. In JupyterLab, click the "Terminal" tile under "Other" to start a Terminal session.
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2b. If this is your first time using conda in the Terminal in IDAS, initialize your shell by running the following command in the Terminal.
This command only needs to be run once. It will create a .bashrc file in your IDAS home directory if a .bashrc doesn’t already exist.
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conda init bash |
2c. Close the Terminal by clicking the x in the upper right corner of the Terminal window.
2d. Click the Terminal tile to open the Terminal again.
2e. Run the following command in the Terminal. This will source the .bashrc file that was just created.
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source ~/.bashrc |
The command prompt in your Terminal will change to indicate the active environment, which is the base environment by default.
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(base) HawkID@idas-research-HawkID:~$ |
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Every time you open a new Terminal session, you’ll have to run |
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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.
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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.
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Additional 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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:
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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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:
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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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:
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conda env create -f environment.yml |
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For more examples of creating conda environments, please see the section "Managing environments" in the Conda User Guide. |
4. Once this conda environment has been created, we can activate it.
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conda activate py38 |
5. The command prompt in your Terminal will change to indicate the active environment.
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(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:
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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:
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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
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Notebook
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, 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.
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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:
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conda install --name py38 pandas |
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For more information on searching for and installing packages, see the section “Managing packages” in the Conda User Guide. |
Example: Create a conda environment with an R kernel
1. First, log in to the IDAS research instance with Python. If you are a student in a class that uses Python in IDAS, follow the instructions here to access to access your class instance.
22a. In JupyterLab, click the "Terminal" tile under "Other" to start a Terminal session.
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.
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2b. If this is your first time using conda in the Terminal in IDAS, initialize your shell by running the following command in the Terminal.
This command only needs to be run once. It will create a .bashrc file in your IDAS home directory if a .bashrc doesn’t already exist.
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conda init bash |
2c. Close the Terminal by clicking the x in the upper right corner of the Terminal window.
2d. Click the Terminal tile to open the Terminal again.
2e. Run the following command in the Terminal. This will source the .bashrc file that was just created.
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source ~/.bashrc |
The command prompt in your Terminal will change to indicate the active environment, which is the base environment by default.
| Code Block |
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(base) HawkID@idas-research-HawkID:~$ |
| Info |
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Every time you open a new Terminal session, you’ll have to run |
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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.
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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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Additional notes:
You can create an R conda environment with just the
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r-base
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package, like so:
conda create -n r-env r-base
However, the
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r-essentials
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package includes approximately 80 of the most popular packages for R, so it is convenient to install the
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r-essentials
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bundle rather than installing each individual package later.
Many R packages are available to install with conda, but not all. The latest index of R packages built by Anaconda, Inc. can be found on Anaconda Cloud or at http://repo.anaconda.com/pkgs/r/
For more information about using R with conda, please see Using R language with Anaconda and R language packages for Anaconda in the Anaconda documentation.
For more examples of creating conda environments, please see "Managing environments" in the Conda User Guide.
4. Once this conda environment is created, we can activate it.
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conda activate r-env |
5. The command prompt in your Terminal will change to indicate the active environment.
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(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:
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(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:
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# 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.
8. Go back to the JupyterLab Launcher page by pressing Ctrl+Shift+L (Windows) or Cmd+Shift+L (Mac). Under
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Notebook
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, a new option for your kernel will now be available, with the name "R 3.6.1"
Click on that new option to start a notebook. In that notebook, you can use the packages that you installed in the conda environment.
9. To install R packages in this conda environment, in Terminal:
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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:
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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 (*).
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conda info --envs |
or
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conda env list |
To activate a conda environment that was previously created, type (without the angle brackets):
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conda activate <environment-name> |
Deactivate a conda environment once you are finished working with it:
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conda deactivate |
Or, to return to the
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base
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environment, type (with no environment specified):
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conda activate |
View a list of packages installed in an environment:
If the environment is not activated, type (without the angle brackets):
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conda list -n <environment-name> |
If the environment was already activated with
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conda activate
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earlier:
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conda list |
To see if a specific package is installed in an environment, type (without the angle brackets):
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conda list -n <environment-name> <package-name> |
Or, if the environment was already activated with
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conda activate
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earlier, type (without the angle brackets):
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conda list <package-name> |
To install a package in an environment:
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conda install --name <environment-name> <package-name> |
Remove an environment:
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conda remove --name <environment-name> --all |
or
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conda env remove --name <environment-name> |
Using pip in a conda environment
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If you have any questions or comments, please contact research-computing@uiowa.edu.