Examples that have online deployments, either as runnable read-only notebooks or web applications, link these deployments at the top of their page.
All of the examples on this website have a link to download the project as a ZIP file.
To run an example locally, first install anaconda-project.
conda install anaconda-project
Once you unpack the project locally and visit that directory, you can see that each project directory has a text file
anaconda-project.yml that defines an environment along with predefined commands that can be run in that environment. To run the default command defined in that project, do:
Running this command will install the dependencies for the particular project, then execute whatever the first command is. E.g. for a Panel dashboard, the default command could start a server (e.g. it will end with a statement like:
Bokeh app running at: http://localhost:5006/attractors_panel ). You can then open the given link to see the running dashboard.
If the default command is a dashboard or app but you want to see or edit the individual steps involved, most projects also provide a predefined “notebook” command:
anaconda-project run notebook
Other commands might be defined in the
.yml file as well, e.g. multiple notebooks, multiple dashboards, or other tasks. You can also run any command you like in the provided environment, even if it’s not defined in the
.yml already. E.g. to launch a Jupyter notebook server for the entire directory, you can ask
anaconda-project to run
jupyter lab, or any other program:
anaconda-project run jupyter notebook
If you don’t want to use
anaconda-project at all, you can create a regular
conda environment using:
conda env create --file anaconda-project.yml
Activate the environment (be sure to replace env-name with the real name of the environment you created):
conda activate <env-name>
Then start a jupyter notebook as usual:
NOTE: If the notebook depends on data files, you will need to
download them explicitly if you don’t use
extracting the URLs defined in
anaconda-project.yml and saving the
file(s) to this directory.