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clarify description and configurations. Closing issue #28

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Chris 2020-09-29 11:42:20 +02:00
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13
.build/Dockerfile
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@ -1,4 +1,9 @@
# This adaptive Dockerfile is generated by 'generate-Dockerfile.sh' from parts within src/ # This Dockerfile is generated by 'generate-Dockerfile.sh' from elements within 'src/'
# **Please do not change this file directly!**
# To adapt this Dockerfile, adapt 'generate-Dockerfile.sh' or 'src/Dockerfile.usefulpackages'.
# More information can be found in the documentation.
# Use NVIDIA CUDA as base image and run the same installation as in the other packages. # Use NVIDIA CUDA as base image and run the same installation as in the other packages.
# The version of cudatoolkit must match those of the base image, see Dockerfile.pytorch # The version of cudatoolkit must match those of the base image, see Dockerfile.pytorch
@ -412,12 +417,6 @@ LABEL authors="Christoph Schranz <christoph.schranz@salzburgresearch.at>, Mathem
USER root USER root
# Install elasticsearch libs
USER root
RUN apt-get update \
&& curl -sL https://repo1.maven.org/maven2/org/elasticsearch/elasticsearch-hadoop/6.8.1/elasticsearch-hadoop-6.8.1.jar
RUN pip install --no-cache-dir elasticsearch==7.1.0
RUN pip install --no-cache-dir ipyleaflet plotly==4.8.* "ipywidgets>=7.5" RUN pip install --no-cache-dir ipyleaflet plotly==4.8.* "ipywidgets>=7.5"
# Install important packages and Graphviz # Install important packages and Graphviz

228
README.md
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@ -1,7 +1,7 @@
# GPU-Jupyter # GPU-Jupyter
#### Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks. #### Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks.
![Jupyterlab Overview](/extra/jupyterlab-overview.png) ![Jupyterlab Overview](https://raw.githubusercontent.com/iot-salzburg/gpu-jupyter/master/extra/jupyterlab-overview.png)
First of all, thanks to [docker-stacks](https://github.com/jupyter/docker-stacks) First of all, thanks to [docker-stacks](https://github.com/jupyter/docker-stacks)
for creating and maintaining a robost Python, R and Julia toolstack for Data Analytics/Science for creating and maintaining a robost Python, R and Julia toolstack for Data Analytics/Science
@ -14,8 +14,8 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
1. [Requirements](#requirements) 1. [Requirements](#requirements)
2. [Quickstart](#quickstart) 2. [Quickstart](#quickstart)
3. [Tracing](#tracing) 3. [Tracing](#tracing)
4. [Deployment](#deployment-in-the-docker-swarm) 4. [Configuration](#configuration)
5. [Configuration](#configuration) 5. [Deployment](#deployment-in-the-docker-swarm)
6. [Issues and Contributing](#issues-and-contributing) 6. [Issues and Contributing](#issues-and-contributing)
@ -58,58 +58,41 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
## Quickstart ## Quickstart
First of all, it is necessary to generate the `Dockerfile` based on the NIVIDA base image and the First of all, it is necessary to generate the `Dockerfile` that is based on
[docker-stacks](https://github.com/jupyter/docker-stacks). the NIVIDA base image and the [docker-stacks](https://github.com/jupyter/docker-stacks).
As soon as you have access to your GPU within Docker containers As soon as you have access to your GPU within Docker containers
(make sure the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi` shows your (make sure the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi`
GPU statistics), you can generate a Dockerfile, build and run it. shows your GPU statistics), you can generate the Dockerfile, build and run it.
The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) with the default The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848)
password `asdf`. with the default password `asdf`.
```bash ```bash
./generate-Dockerfile.sh # generate a Dockerfile with python and without Julia and R
./generate-Dockerfile.sh --no-datascience-notebook
docker build -t gpu-jupyter .build/ # will take a while docker build -t gpu-jupyter .build/ # will take a while
docker run -d -p [port]:8888 gpu-jupyter # starts gpu-jupyter WITHOUT GPU support docker run -d -p [port]:8888 gpu-jupyter # starts gpu-jupyter WITHOUT GPU support
``` ```
To run the container with GPU support, a local data volume and , run: To run the container WITH GPU support, a local data volume and some other configurations, run:
```bash ```bash
docker run -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root --restart always --name gpu-jupyter_1 gpu-jupyter docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root --restart always --name gpu-jupyter_1 gpu-jupyter
``` ```
### Start via Docker Compose
## Parameter The script `start-local.sh` is a wrapper for a quick configuration of the
underlying `docker-compose.yml`:
The script `generate-Dockerfile.sh` has multiple parameters:
* `-c|--commit`: specify a commit or `"latest"` for the `docker-stacks`, the default commit is a working one.
* `-s|--slim`: Generate a slim Dockerfile.
As some installations are not needed by everyone, there is the possibility to skip some installations
to reduce the size of the image.
Here the `docker-stack` `scipy-notebook` is used instead of `datascience-notebook` that comes with Julia and R.
Moreover, none of the packages within `src/Dockerfile.usefulpackages` is installed.
* `--no-datascience-notebook`: As the name suggests, the `docker-stack` `datascience-notebook` is not installed
on top of the `scipy-notebook`, but the packages within `src/Dockerfile.usefulpackages` are.
* `--no-useful-packages`: On top of the `docker-stack` `datascience-notebook`, the essential `gpulibs` are installed
but not the packages within `src/Dockerfile.usefulpackages`.
The script `start-local.sh` is a wrapper for a quick configuration of the underlying `docker-compose.yml`.
It is equal to these commands:
```bash ```bash
docker build -t gpu-jupyter .build/ ./start-local.sh -p 8848 # the default port is 8888
docker run -d -p [port]:8888 gpu-jupyter
``` ```
## Tracing ## Tracing
With these commands we can see if everything worked well: With these commands we can see if everything worked well:
```bash ```bash
bash show-local.sh # a env-var safe wrapper for a 'docker-compose logs -f' bash show-local.sh # a env-var safe wrapper for 'docker-compose logs -f'
docker ps docker ps
docker logs [service-name] docker logs [service-name]
``` ```
@ -121,6 +104,105 @@ In order to stop the local deployment, run:
``` ```
## Configuration
### Configuration of the Dockerfile-Generation
The script `generate-Dockerfile.sh` generates a Dockerfile within the `.build/`
directory.
This implies that this Dockerfile is overwritten by each generation.
The Dockerfile-generation script `generate-Dockerfile.sh`
has the following parameters (note that 2, 3 and 4 are exclusive):
* `-c|--commit`: specify a commit or `"latest"` for the `docker-stacks`,
the default commit is a working one.
* `-s|--slim`: Generate a slim Dockerfile.
As some installations are not needed by everyone, there is the possibility to skip some
installations to reduce the size of the image.
Here the `docker-stack` `scipy-notebook` is used instead of `datascience-notebook`
that comes with Julia and R.
Moreover, none of the packages within `src/Dockerfile.usefulpackages` is installed.
* `--no-datascience-notebook`: As the name suggests, the `docker-stack` `datascience-notebook`
is not installed
on top of the `scipy-notebook`, but the packages within `src/Dockerfile.usefulpackages` are.
* `--no-useful-packages`: On top of the `docker-stack` `datascience-notebook` (Julia and R),
the essential `gpulibs` are installed, but not the packages within `src/Dockerfile.usefulpackages`.
### Custom Installations
**As `.build/Dockerfile` is overwritten, it is suggested to append custom installations either
within `src/Dockerfile.usefulpackages` or in `generate-Dockerfile.sh`.**
If you think some package is missing in the default stack, please let us know!
### Set the Password
Please set a new password using `src/jupyter_notebook_config.json`.
Therefore, hash your password in the form (password)(salt) using a sha1 hash generator, e.g., the sha1 generator of [sha1-online.com](http://www.sha1-online.com/).
The input with the default password `asdf` is appended by a arbitrary salt `e49e73b0eb0e` to `asdfe49e73b0eb0e` and should yield the hash string as shown in the config below.
**Never give away your own unhashed password!**
Then update the config file as shown below and restart the service.
```json
{
"NotebookApp": {
"password": "sha1:e49e73b0eb0e:32edae7a5fd119045e699a0bd04f90819ca90cd6"
}
}
```
### Updates
#### Update CUDA to another version
Please check version compatibilities for [CUDA and Pytorch](https://pytorch.org/get-started/locally/)
respectively [CUDA and Tensorflow](https://www.tensorflow.org/install/gpu) previously.
To update CUDA to another version, change in `Dockerfile.header`
the line:
FROM nvidia/cuda:10.1-base-ubuntu18.04
and in the `Dockerfile.pytorch` the line:
cudatoolkit=10.1
Then re-generate and re-run the image, as closer described above:
```bash
./generate-Dockerfile.sh
./start-local.sh -p 8848
```
#### Update Docker-Stack
The [docker-stacks](https://github.com/jupyter/docker-stacks) are used as a
submodule within `.build/docker-stacks`. Per default, the head of the commit is reset to a commit on which `gpu-jupyter` runs stable.
To update the generated Dockerfile to a specific commit, run:
```bash
./generate-Dockerfile.sh --commit c1c32938438151c7e2a22b5aa338caba2ec01da2
```
To update the generated Dockerfile to the latest commit, run:
```bash
./generate-Dockerfile.sh --commit latest
```
A new build can last some time and may consume a lot of data traffic. Note, that the latest version may result in
a version conflict!
More info to submodules can be found in
[this tutorial](https://www.vogella.com/tutorials/GitSubmodules/article.html).
## Deployment in the Docker Swarm ## Deployment in the Docker Swarm
A Jupyter instance often requires data from other services. A Jupyter instance often requires data from other services.
@ -184,10 +266,14 @@ Finally, *GPU-Jupyter* can be deployed in the Docker Swarm with the shared netwo
``` ```
where: where:
* **-p:** port specifies the port on which the service will be available. * **-p:** port specifies the port on which the service will be available.
* **-n:** docker-network is the name of the attachable network from the previous step, e.g., here it is **elk_datastack**. * **-n:** docker-network is the name of the attachable network from the previous step,
* **-r:** registry port is the port that is published by the registry service, see [Set up Docker Swarm and Registry](set-up-docker-swarm-and-registry). e.g., here it is **elk_datastack**.
* **-r:** registry port is the port that is published by the registry service, default is `5000`.
Now, *gpu-jupyter* will be accessable here on [localhost:8848](http://localhost:8848) with the default password `asdf` and shares the network with the other data-source, i.e., all ports of the data-source will be accessable within *GPU-Jupyter*, even if they aren't routed it the source's `docker-compose` file. Now, *gpu-jupyter* will be accessible here on [localhost:8848](http://localhost:8848)
with the default password `asdf` and shares the network with the other data-source, i.e.,
all ports of the data-source will be accessible within *GPU-Jupyter*,
even if they aren't routed it the source's `docker-compose` file.
Check if everything works well using: Check if everything works well using:
```bash ```bash
@ -200,70 +286,12 @@ In order to remove the service from the swarm, use:
./remove-from-swarm.sh ./remove-from-swarm.sh
``` ```
## Configuration
Please set a new password using `src/jupyter_notebook_config.json`.
Therefore, hash your password in the form (password)(salt) using a sha1 hash generator, e.g., the sha1 generator of [sha1-online.com](http://www.sha1-online.com/).
The input with the default password `asdf` is appended by a arbitrary salt `e49e73b0eb0e` to `asdfe49e73b0eb0e` and should yield the hash string as shown in the config below.
**Never give away your own unhashed password!**
Then update the config file as shown below and restart the service.
```json
{
"NotebookApp": {
"password": "sha1:e49e73b0eb0e:32edae7a5fd119045e699a0bd04f90819ca90cd6"
}
}
```
### Updates
#### Update CUDA to another version
Please check version compatibilities for [CUDA and Pytorch](https://pytorch.org/get-started/locally/)
respectively [CUDA and Tensorflow](https://www.tensorflow.org/install/gpu) previously.
To update CUDA to another version, change in `Dockerfile.header`
the line:
FROM nvidia/cuda:10.1-base-ubuntu18.04
and in the `Dockerfile.pytorch` the line:
cudatoolkit=10.1
Then re-generate and re-run the image, as closer described above:
```bash
./generate-Dockerfile.sh
./start-local.sh -p 8848
```
#### Update Docker-Stack
The [docker-stacks](https://github.com/jupyter/docker-stacks) are used as a
submodule within `.build/docker-stacks`. Per default, the head of the commit is reset to a commit on which `gpu-jupyter` runs stable.
To update the generated Dockerfile to a specific commit, run:
```bash
./generate-Dockerfile.sh --commit c1c32938438151c7e2a22b5aa338caba2ec01da2
```
To update the generated Dockerfile to the latest commit, run:
```bash
./generate-Dockerfile.sh --commit latest
```
A new build can last some time and may consume a lot of data traffic. Note, that the latest version may result in
a version conflict!
More info to submodules can be found in
[this tutorial](https://www.vogella.com/tutorials/GitSubmodules/article.html).
## Issues and Contributing ## Issues and Contributing
This project has the intention to create a robust image for CUDA-based GPU-applications, which is built on top of the [docker-stacks](https://github.com/jupyter/docker-stacks). You are free to help to improve this project, by: This project has the intention to create a robust image for CUDA-based GPU-applications,
which is built on top of the [docker-stacks](https://github.com/jupyter/docker-stacks).
You are free to help to improve this project, by:
* [filing a new issue](https://github.com/iot-salzburg/gpu-jupyter/issues/new) * [filing a new issue](https://github.com/iot-salzburg/gpu-jupyter/issues/new)
* [open a pull request](https://help.github.com/articles/using-pull-requests/) * [open a pull request](https://help.github.com/articles/using-pull-requests/)

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generate-Dockerfile.sh
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@ -40,7 +40,12 @@ else
fi fi
# Write the contents into the DOCKERFILE and start with the header # Write the contents into the DOCKERFILE and start with the header
echo "# This adaptive Dockerfile is generated by 'generate-Dockerfile.sh' from parts within src/ echo "# This Dockerfile is generated by 'generate-Dockerfile.sh' from elements within 'src/'
# **Please do not change this file directly!**
# To adapt this Dockerfile, adapt 'generate-Dockerfile.sh' or 'src/Dockerfile.usefulpackages'.
# More information can be found in the README under configuration.
" > $DOCKERFILE " > $DOCKERFILE
cat src/Dockerfile.header >> $DOCKERFILE cat src/Dockerfile.header >> $DOCKERFILE

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src/Dockerfile.usefulpackages
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@ -2,12 +2,6 @@ LABEL authors="Christoph Schranz <christoph.schranz@salzburgresearch.at>, Mathem
USER root USER root
# Install elasticsearch libs
USER root
RUN apt-get update \
&& curl -sL https://repo1.maven.org/maven2/org/elasticsearch/elasticsearch-hadoop/6.8.1/elasticsearch-hadoop-6.8.1.jar
RUN pip install --no-cache-dir elasticsearch==7.1.0
RUN pip install --no-cache-dir ipyleaflet plotly==4.8.* "ipywidgets>=7.5" RUN pip install --no-cache-dir ipyleaflet plotly==4.8.* "ipywidgets>=7.5"
# Install important packages and Graphviz # Install important packages and Graphviz