Hello_User/gpu-jupyter
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ignore push_tag and fixed README explaination

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Christoph Schranz 2021-01-05 14:07:01 +01:00
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commit fb97025ac0
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@ -116,3 +116,4 @@ venv.bak/
src/jupyter_notebook_config.json
.idea
/Deployment-notes.md
/push_tag.sh

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README.md
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@ -11,17 +11,17 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
## Contents
1. [Requirements](#requirements)
2. [Quickstart](#quickstart)
1. [Quickstart](#quickstart)
2. [Build your own image](#build-your-own-image)
3. [Tracing](#tracing)
4. [Configuration](#configuration)
5. [Deployment](#deployment-in-the-docker-swarm)
6. [Issues and Contributing](#issues-and-contributing)
## Requirements
## Quickstart
1. A computer with a NVIDIA GPU
1. A computer with an NVIDIA GPU is required.
2. Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
and [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**.
3. Get access to your GPU via CUDA drivers within Docker containers.
@ -62,11 +62,13 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
environment will be downloaded:
```bash
cd your-working-directory
docker run --gpus all -d -it -p 8848:8888 -v data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root cschranz/gpu-jupyter:v1.1_cuda-10.1_ubuntu-18.04_python-only
docker run --gpus all -d -it -p 8848:8888 -v data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root cschranz/gpu-jupyter:v1.2_cuda-10.1_ubuntu-18.04_python-only
```
This starts a new instance of the GPU-Jupyter service on at [http://localhost:8848](http://localhost:8848) (port `8484`).
This starts an instance with of *GPU-Jupyter* the tag `v1.2_cuda-10.1_ubuntu-18.04_python-only` at [http://localhost:8848](http://localhost:8848) (port `8484`).
The default password is `asdf` which should be changed as described [below](#set-password).
Furthermore, data within the host's `data` directory is shared with the container.
Other versions of GPU-Jupyter are available and listed on Dockerhub under [Tags](https://hub.docker.com/r/cschranz/gpu-jupyter/tags?page=1&ordering=last_updated).
Within the Jupyterlab instance, you can check if you can access your GPU by opening a new terminal window and running
`nvidia-smi`. In terminal windows, you can also install new packages for your own projects.
@ -74,7 +76,7 @@ Some example code can be found in the repository under `extra/Getting_Started`.
If you want to learn more about Jupyterlab, check out this [tutorial](https://www.youtube.com/watch?v=7wfPqAyYADY).
## Build a modified version
## Build your own Image
First, it is necessary to generate the `Dockerfile` in `.build`, that is based on
the NIVIDA base image and the [docker-stacks](https://github.com/jupyter/docker-stacks).