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describe dynamic Dockerfile generation

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Christoph Schranz 2020-02-22 14:51:34 +01:00
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![Jupyterlab Overview](/extra/jupyterlab-overview.png)
First of all, thanks to [hub.docker.com/u/jupyter](https://hub.docker.com/u/jupyter) for creating and maintaining a robost Python, R and Julia toolstack. This project uses their toolstack and uses the NVIDIA CUDA drivers as a basis to enable GPU calculations in the Jupyter notebooks.
First of all, thanks to [hub.docker.com/u/jupyter](https://hub.docker.com/u/jupyter)
for creating and maintaining a robost Python, R and Julia toolstack for Data Analytics/Science
applications. This project uses the NVIDIA CUDA image as a basis image and installs their
toolstack on top of it to enable GPU calculations in the Jupyter notebooks.
## Contents
@ -16,10 +19,11 @@ First of all, thanks to [hub.docker.com/u/jupyter](https://hub.docker.com/u/jupy
## Requirements
1. Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
2. Install [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**
3. A NVIDIA GPU
3. Get access to use your GPU via the CUDA drivers, check out this [medium](https://medium.com/@christoph.schranz/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43) article.
4. Clone this repository
and [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**.
2. A NVIDIA GPU
3. Get access to use your GPU via the CUDA drivers, check out this
[medium article](https://medium.com/@christoph.schranz/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43).
4. Clone the Repository:
```bash
git clone https://github.com/iot-salzburg/gpu-jupyter.git
cd gpu-jupyter
@ -27,14 +31,30 @@ First of all, thanks to [hub.docker.com/u/jupyter](https://hub.docker.com/u/jupy
## Quickstart
As soon as you have access to your GPU locally (it can be tested via a Tensorflow or PyTorch), you can run these commands to start the jupyter notebook via docker-compose:
First of all, it is necessary to generate the `Dockerfile` based on the latest toolstack of
[hub.docker.com/u/jupyter](https://hub.docker.com/u/jupyter).
As soon as you have access to your GPU locally (it can be tested via a Tensorflow or PyTorch
directly on the host node), you can run these commands to start the jupyter notebook via
docker-compose (internally):
```bash
./generate_Dockerfile.sh
docker build -t gpu-jupyter .
docker run -d -p [port]:8888 gpu-jupyter
```
Alternatively, you can configure the environment in `docker-compose.yml` and run this to deploy
the `GPU-Jupyter` via docker-compose (under-the-hood):
```bash
./generate_Dockerfile.sh
./start-local.sh
```
This will run *GPU-Jupyter* by default on [localhost:8888](http://localhost:8888) with the default password `asdf`. The general usage is:
Both options will run *GPU-Jupyter* by default on [localhost:8888](http://localhost:8888) with the default
password `asdf`. The general usage of the `docker-compose` variant is:
```bash
./start-local.sh -p [port:8888] # port must be an integer with 4 or more digits.
./start-local.sh -p [port] # port must be an integer with 4 or more digits.
```
With these commands we can see if everything worked well:
@ -107,6 +127,7 @@ networks:
Finally, *GPU-Jupyter* can be deployed in the Docker Swarm with the shared network, using:
```bash
./generate_Dockerfile.sh
./add-to-swarm.sh -p [port] -n [docker-network] -r [registry-port]
# e.g. ./add-to-swarm.sh -p 8848 -n elk_datastack -r 5001
```