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📝 DOC: Some wording fixes

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Tania Allard
2022-02-07 10:49:59 +00:00
parent 53ffd9c1e9
commit c377d70218
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
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.
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.
The server logs appear in the terminal and include a URL to the notebook server.
@@ -49,27 +51,34 @@ docker rm 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.
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
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.
Files written to `~/work` in the container remain touched.
Any other changes made in the container are lost.
New files and changes in `~/work` in the container will be preserved.
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.
The `-d` flag mean to run the container in detached mode.
```bash
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
# 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
```
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
# 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
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
@@ -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) |
[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:
@@ -195,7 +195,7 @@ diagram](../images/inherit.svg)](http://interactive.blockdiag.com/?compression=d
### 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
@@ -253,10 +253,10 @@ See the [contributing guide](../contributing/stacks.md) for information about ho
### GPU enabled notebooks
| 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 |
| [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>. |
| 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 |
| [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
[prp_gpu]: https://gitlab.nautilus.optiputer.net/prp/jupyter-stack/-/tree/prp