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📝 DOC: Some wording fixes

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Tania Allard
2022-02-07 10:49:59 +00:00
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2 changed files with 27 additions and 18 deletions
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31
docs/using/running.md
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@@ -9,11 +9,13 @@ This section provides details about the second.
## Using the Docker CLI ## Using the Docker CLI
You can launch a local Docker container from the Jupyter Docker Stacks using the [Docker command line interface](https://docs.docker.com/engine/reference/commandline/cli/). You can launch a local Docker container from the Jupyter Docker Stacks using the [Docker command-line interface](https://docs.docker.com/engine/reference/commandline/cli/).
There are numerous ways to configure containers using the CLI. There are numerous ways to configure containers using the CLI.
The following are some common patterns. The following are some common patterns.
**Example 1** This command pulls the `jupyter/scipy-notebook` image tagged `b418b67c225b` from Docker Hub if it is not already present on the local host. **Example 1:**
This command pulls the `jupyter/scipy-notebook` image tagged `b418b67c225b` from Docker Hub if it is not already present on the local host.
It then starts a container running a Jupyter Notebook server and exposes the server on host port 8888. It then starts a container running a Jupyter Notebook server and exposes the server on host port 8888.
The server logs appear in the terminal and include a URL to the notebook server. The server logs appear in the terminal and include a URL to the notebook server.
@@ -49,27 +51,34 @@ docker rm 221331c047c4
# 221331c047c4 # 221331c047c4
``` ```
**Example 2** This command pulls the `jupyter/r-notebook` image tagged `b418b67c225b` from Docker Hub if it is not already present on the local host. **Example 2:**
This command pulls the `jupyter/r-notebook` image tagged `b418b67c225b` from Docker Hub if it is not already present on the local host.
It then starts a container running a Jupyter Notebook server and exposes the server on host port 10000. It then starts a container running a Jupyter Notebook server and exposes the server on host port 10000.
The server logs appear in the terminal and include a URL to the notebook server, but with the internal container port (8888) instead of the the correct host port (10000). The server logs appear in the terminal and include a URL to the notebook server, but with the internal container port (8888) instead of the correct host port (10000).
```bash ```bash
docker run -it --rm -p 10000:8888 -v "${PWD}":/home/jovyan/work jupyter/r-notebook:b418b67c225b docker run -it --rm -p 10000:8888 -v "${PWD}":/home/jovyan/work jupyter/r-notebook:b418b67c225b
``` ```
Pressing `Ctrl-C` twice shuts down the notebook server and immediately destroys the Docker container. Pressing `Ctrl-C` twice shuts down the notebook server and immediately destroys the Docker container.
Files written to `~/work` in the container remain touched. New files and changes in `~/work` in the container will be preserved.
Any other changes made in the container are lost. Any other changes made in the container will be lost.
**Example 3** This command pulls the `jupyter/all-spark-notebook` image currently tagged `latest` from Docker Hub if an image tagged `latest` is not already present on the local host. **Example 3:**
This command pulls the `jupyter/all-spark-notebook` image currently tagged `latest` from Docker Hub if an image tagged `latest` is not already present on the local host.
It then starts a container named `notebook` running a JupyterLab server and exposes the server on a randomly selected port. It then starts a container named `notebook` running a JupyterLab server and exposes the server on a randomly selected port.
The `-d` flag mean to run the container in detached mode.
```bash ```bash
docker run -d -P --name notebook jupyter/all-spark-notebook docker run -d -P --name notebook jupyter/all-spark-notebook
``` ```
The assigned port and notebook server token are visible using other Docker commands. where:
- `-d`: will run the container in detached mode
You can also use the following docker commands to see the port and notebook server token:
```bash ```bash
# get the random host port assigned to the container port 8888 # get the random host port assigned to the container port 8888
@@ -84,9 +93,9 @@ docker logs --tail 3 notebook
# or http://127.0.0.1:8888/lab?token=d336fa63c03f064ff15ce7b269cab95b2095786cf9ab2ba3 # or http://127.0.0.1:8888/lab?token=d336fa63c03f064ff15ce7b269cab95b2095786cf9ab2ba3
``` ```
Together, the URL to visit on the host machine to access the server in this case is <http://127.0.0.1:49153/lab?token=d336fa63c03f064ff15ce7b269cab95b2095786cf9ab2ba3>. Together, the URL to visit on the host machine to access the server, in this case, is <http://127.0.0.1:49153/lab?token=d336fa63c03f064ff15ce7b269cab95b2095786cf9ab2ba3>.
The container runs in the background until stopped and/or removed by additional Docker commands. The container runs in the background until stopped and/or removed by additional Docker commands:
```bash ```bash
# stop the container # stop the container

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docs/using/selecting.md
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@@ -14,7 +14,7 @@ This section provides details about the first.
## Core Stacks ## Core Stacks
The Jupyter team maintains a set of Docker image definitions in the <https://github.com/jupyter/docker-stacks> GitHub repository. The Jupyter team maintains a set of Docker image definitions in the <https://github.com/jupyter/docker-stacks> GitHub repository.
The following sections describe these images including their contents, relationships, and versioning strategy. The following sections describe these images, including their contents, relationships, and versioning strategy.
### jupyter/base-notebook ### jupyter/base-notebook
@@ -44,7 +44,7 @@ It is the basis for all other stacks and contains:
[Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/minimal-notebook/Dockerfile) | [Dockerfile commit history](https://github.com/jupyter/docker-stacks/commits/master/minimal-notebook/Dockerfile) |
[Docker Hub image tags](https://hub.docker.com/r/jupyter/minimal-notebook/tags/) [Docker Hub image tags](https://hub.docker.com/r/jupyter/minimal-notebook/tags/)
`jupyter/minimal-notebook` adds command line tools useful when working in Jupyter applications. `jupyter/minimal-notebook` adds command-line tools useful when working in Jupyter applications.
It contains: It contains:
@@ -195,7 +195,7 @@ diagram](../images/inherit.svg)](http://interactive.blockdiag.com/?compression=d
### Builds ### Builds
Every Monday and whenever a pull requests is merged, images are rebuilt and pushed to [the public container registry](https://hub.docker.com/r/jupyter). Every Monday and whenever a pull request is merged, images are rebuilt and pushed to [the public container registry](https://hub.docker.com/r/jupyter).
### Versioning via image tags ### Versioning via image tags
@@ -253,10 +253,10 @@ See the [contributing guide](../contributing/stacks.md) for information about ho
### GPU enabled notebooks ### GPU enabled notebooks
| Flavor | Description | | Flavor | Description |
| ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [GPU-Jupyter][gpu] | Power of your NVIDIA GPU and GPU calculations using Tensorflow and Pytorch in collaborative notebooks. This is done by generating a Dockerfile, that consists of the **nvidia/cuda** base image, the well-maintained **docker-stacks** that is integrated as submodule and GPU-able libraries like **Tensorflow**, **Keras** and **PyTorch** on top of it | | [GPU-Jupyter][gpu] | Power of your NVIDIA GPU and GPU calculations using Tensorflow and Pytorch in collaborative notebooks. This is done by generating a Dockerfile that consists of the **nvidia/cuda** base image, the well-maintained **docker-stacks** that is integrated as submodule and GPU-able libraries like **Tensorflow**, **Keras** and **PyTorch** on top of it |
| [PRP-GPU][prp_gpu] | PRP (Pacific Research Platform) maintained [registry][prp_reg] for jupyter stack based on NVIDIA CUDA-enabled image. Added the PRP image with Pytorch and some other python packages, and GUI Desktop notebook based on <https://github.com/jupyterhub/jupyter-remote-desktop-proxy>. | | [PRP-GPU][prp_gpu] | PRP (Pacific Research Platform) maintained [registry][prp_reg] for jupyter stack based on NVIDIA CUDA-enabled image. Added the PRP image with Pytorch and some other python packages and GUI Desktop notebook based on <https://github.com/jupyterhub/jupyter-remote-desktop-proxy>. |
[gpu]: https://github.com/iot-salzburg/gpu-jupyter [gpu]: https://github.com/iot-salzburg/gpu-jupyter
[prp_gpu]: https://gitlab.nautilus.optiputer.net/prp/jupyter-stack/-/tree/prp [prp_gpu]: https://gitlab.nautilus.optiputer.net/prp/jupyter-stack/-/tree/prp