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@@ -23,13 +23,11 @@ This section will focus on user environments, which includes the following:
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To make packages available to users, you will typically install packages system-wide or in a shared environment.
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To make packages available to users, you will typically install packages system-wide or in a shared environment.
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This installation location should always be in the same environment where
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This installation location should always be in the same environment where
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`jupyterhub-singleuser` itself is installed in, and must be _readable and
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`jupyterhub-singleuser` itself is installed in, and must be _readable and
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executable_ by your users. If you want your users to be able to install additional
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executable_ by your users. If you want your users to be able to install additional
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packages, the installation location must also be _writable_ by your users.
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packages, the installation location must also be _writable_ by your users.
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If you are using a standard Python installation on your system, use the following command:
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If you are using a standard Python installation on your system, use the following command:
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```bash
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```bash
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@@ -78,6 +76,7 @@ with the following substitutions:
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:::
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:::
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To enable Jupyter notebook's internal idle-shutdown behavior (requires notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py` file:
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To enable Jupyter notebook's internal idle-shutdown behavior (requires notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py` file:
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```python
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```python
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# shutdown the server after no activity for an hour
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# shutdown the server after no activity for an hour
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c.ServerApp.shutdown_no_activity_timeout = 60 * 60
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c.ServerApp.shutdown_no_activity_timeout = 60 * 60
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@@ -92,12 +91,10 @@ c.MappingKernelManager.cull_interval = 2 * 60
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You may have multiple Jupyter kernels installed and want to make sure that they are available to all of your users. This means installing kernelspecs either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
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You may have multiple Jupyter kernels installed and want to make sure that they are available to all of your users. This means installing kernelspecs either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
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itself.
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itself.
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Jupyter kernelspec installation is system-wide by default, but some kernels
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Jupyter kernelspec installation is system-wide by default, but some kernels
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may default to installing kernelspecs in your home directory. These will need
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may default to installing kernelspecs in your home directory. These will need
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to be moved system-wide to ensure that they are accessible.
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to be moved system-wide to ensure that they are accessible.
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To see where your kernelspecs are, you can use the following command:
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To see where your kernelspecs are, you can use the following command:
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```bash
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```bash
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@@ -133,16 +130,13 @@ or a custom prefix such as `/opt/conda`.
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When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
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When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
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DockerSpawner), the 'system-wide' environment is really the container image used for users.
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DockerSpawner), the 'system-wide' environment is really the container image used for users.
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In both cases, you want to _avoid putting configuration in user home
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In both cases, you want to _avoid putting configuration in user home
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directories_ because users can change those configuration settings. Also, home directories typically persist once they are created, thereby making it difficult for admins to update later.
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directories_ because users can change those configuration settings. Also, home directories typically persist once they are created, thereby making it difficult for admins to update later.
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## Named servers
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## Named servers
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By default, in a JupyterHub deployment, each user has one server only.
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By default, in a JupyterHub deployment, each user has one server only.
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JupyterHub can, however, have multiple servers per user.
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JupyterHub can, however, have multiple servers per user.
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This is mostly useful in deployments where users can configure the environment in which their server will start (e.g. resource requests on an HPC cluster), so that a given user can have multiple configurations running at the same time, without having to stop and restart their own server.
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This is mostly useful in deployments where users can configure the environment in which their server will start (e.g. resource requests on an HPC cluster), so that a given user can have multiple configurations running at the same time, without having to stop and restart their own server.
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@@ -161,11 +155,9 @@ as well as the admin page:
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Named servers can be accessed, created, started, stopped, and deleted
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Named servers can be accessed, created, started, stopped, and deleted
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from these pages. Activity tracking is now per server as well.
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from these pages. Activity tracking is now per server as well.
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To limit the number of **named server** per user by setting a constant value, use this:
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To limit the number of **named server** per user by setting a constant value, use this:
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```python
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```python
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@@ -192,7 +184,6 @@ If `named_server_limit_per_user` is set to `0`, no limit is enforced.
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## Switching back to the classic notebook
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## Switching back to the classic notebook
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By default, the single-user server launches JupyterLab,
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By default, the single-user server launches JupyterLab,
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which is based on [Jupyter Server][].
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which is based on [Jupyter Server][].
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@@ -215,7 +206,6 @@ no longer the legacy [Jupyter Notebook][] server.
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JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
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JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
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and the Jupyter server could be selected by specifying the following:
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and the Jupyter server could be selected by specifying the following:
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```python
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```python
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# jupyterhub_config.py
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# jupyterhub_config.py
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c.Spawner.cmd = ["jupyter-labhub"]
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c.Spawner.cmd = ["jupyter-labhub"]
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