270 lines
11 KiB
Markdown
270 lines
11 KiB
Markdown
# GPU-Jupyter
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#### Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks.
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First of all, thanks to [docker-stacks](https://github.com/jupyter/docker-stacks)
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for creating and maintaining a robost Python, R and Julia toolstack for Data Analytics/Science
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applications. This project uses the NVIDIA CUDA image as the base image and installs their
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toolstack on top of it to enable GPU calculations in the Jupyter notebooks.
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The image of this repository is available on [Dockerhub](https://hub.docker.com/r/cschranz/gpu-jupyter).
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## Contents
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1. [Requirements](#requirements)
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2. [Quickstart](#quickstart)
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3. [Tracing](#tracing)
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4. [Deployment](#deployment-in-the-docker-swarm)
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5. [Configuration](#configuration)
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6. [Issues and Contributing](#issues-and-contributing)
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## Requirements
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1. A NVIDIA GPU
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2. Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
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and [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**.
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3. Get access to your GPU via CUDA drivers within Docker containers. Therfore, check out this
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[medium article](https://medium.com/@christoph.schranz/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43).
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The CUDA toolkit is not required on the host system, as it will be deployed
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in [NVIDIA-docker](https://github.com/NVIDIA/nvidia-docker).
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You can be sure that you can access your GPU within Docker,
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if the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi`
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returns a result similar to this one:
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```bash
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Mon Jun 22 09:06:28 2020
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.1 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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|===============================+======================+======================|
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| 0 GeForce RTX 207... Off | 00000000:01:00.0 On | N/A |
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| 0% 46C P8 9W / 215W | 424MiB / 7974MiB | 6% Default |
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+-------------------------------+----------------------+----------------------+
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+-----------------------------------------------------------------------------+
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| Processes: GPU Memory |
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| GPU PID Type Process name Usage |
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|=============================================================================|
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+-----------------------------------------------------------------------------+
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```
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4. Clone the Repository or pull the image from
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[Dockerhub](https://hub.docker.com/repository/docker/cschranz/gpu-jupyter):
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```bash
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git clone https://github.com/iot-salzburg/gpu-jupyter.git
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cd gpu-jupyter
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```
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## Quickstart
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First of all, it is necessary to generate the `Dockerfile` based on the NIVIDA base image and the
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[docker-stacks](https://github.com/jupyter/docker-stacks).
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As soon as you have access to your GPU within Docker containers
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(make sure the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi` shows your
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GPU statistics), you can generate a Dockerfile, build and run it.
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The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) with the default
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password `asdf`.
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```bash
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./generate-Dockerfile.sh
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docker build -t gpu-jupyter .build/ # will take a while
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docker run -d -p [port]:8888 gpu-jupyter # starts gpu-jupyter WITHOUT GPU support
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```
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To run the container with GPU support, a local data volume and , run:
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```bash
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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
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```
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## Parameter
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The script `generate-Dockerfile.sh` has multiple parameters:
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* `-c|--commit`: specify a commit or `"latest"` for the `docker-stacks`, the default commit is a working one.
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* `-s|--slim`: Generate a slim Dockerfile.
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As some installations are not needed by everyone, there is the possibility to skip some installations
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to reduce the size of the image.
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Here the `docker-stack` `scipy-notebook` is used instead of `datascience-notebook` that comes with Julia and R.
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Moreover, none of the packages within `src/Dockerfile.usefulpackages` is installed.
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* `--no-datascience-notebook`: As the name suggests, the `docker-stack` `datascience-notebook` is not installed
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on top of the `scipy-notebook`, but the packages within `src/Dockerfile.usefulpackages` are.
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* `--no-useful-packages`: On top of the `docker-stack` `datascience-notebook`, the essential `gpulibs` are installed
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but not the packages within `src/Dockerfile.usefulpackages`.
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The script `start-local.sh` is a wrapper for a quick configuration of the underlying `docker-compose.yml`.
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It is equal to these commands:
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```bash
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docker build -t gpu-jupyter .build/
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docker run -d -p [port]:8888 gpu-jupyter
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```
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## Tracing
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With these commands we can see if everything worked well:
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```bash
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bash show-local.sh # a env-var safe wrapper for a 'docker-compose logs -f'
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docker ps
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docker logs [service-name]
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```
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In order to stop the local deployment, run:
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```bash
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./stop-local.sh
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```
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## Deployment in the Docker Swarm
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A Jupyter instance often requires data from other services.
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If that data-source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons,
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then connecting the data-source with *GPU-Jupyter* within a Docker Swarm is a great option!
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### Set up Docker Swarm and Registry
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This step requires a running [Docker Swarm](https://www.youtube.com/watch?v=x843GyFRIIY) on a cluster or at least on this node.
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In order to register custom images in a local Docker Swarm cluster,
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a registry instance must be deployed in advance.
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Note that the we are using the port 5001, as many services use the default port 5000.
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```bash
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sudo docker service create --name registry --publish published=5001,target=5000 registry:2
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curl 127.0.0.1:5001/v2/
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```
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This should output `{}`. \
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Afterwards, check if the registry service is available using `docker service ls`.
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### Configure the shared Docker network
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Additionally, *GPU-Jupyter* is connected to the data-source via the same *docker-network*. Therefore, This network must be set to **attachable** in the source's `docker-compose.yml`:
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```yml
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services:
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data-source-service:
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...
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networks:
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- default
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- datastack
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...
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networks:
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datastack:
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driver: overlay
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attachable: true
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```
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In this example,
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* the docker stack was deployed in Docker swarm with the name **elk** (`docker stack deploy ... elk`),
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* the docker network has the name **datastack** within the `docker-compose.yml` file,
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* this network is configured to be attachable in the `docker-compose.yml` file
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* and the docker network has the name **elk_datastack**, see the following output:
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```bash
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sudo docker network ls
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# ...
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# [UID] elk_datastack overlay swarm
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# ...
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```
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The docker network name **elk_datastack** is used in the next step as a parameter.
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### Start GPU-Jupyter in Docker Swarm
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Finally, *GPU-Jupyter* can be deployed in the Docker Swarm with the shared network, using:
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```bash
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./generate-Dockerfile.sh
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./add-to-swarm.sh -p [port] -n [docker-network] -r [registry-port]
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# e.g. ./add-to-swarm.sh -p 8848 -n elk_datastack -r 5001
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```
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where:
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* **-p:** port specifies the port on which the service will be available.
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* **-n:** docker-network is the name of the attachable network from the previous step, e.g., here it is **elk_datastack**.
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* **-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).
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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.
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Check if everything works well using:
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```bash
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sudo docker service ps gpu_gpu-jupyter
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docker service ps gpu_gpu-jupyter
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```
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In order to remove the service from the swarm, use:
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```bash
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./remove-from-swarm.sh
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```
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## Configuration
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Please set a new password using `src/jupyter_notebook_config.json`.
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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/).
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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.
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**Never give away your own unhashed password!**
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Then update the config file as shown below and restart the service.
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```json
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{
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"NotebookApp": {
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"password": "sha1:e49e73b0eb0e:32edae7a5fd119045e699a0bd04f90819ca90cd6"
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}
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}
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```
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### Updates
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#### Update CUDA to another version
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Please check version compatibilities for [CUDA and Pytorch](https://pytorch.org/get-started/locally/)
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respectively [CUDA and Tensorflow](https://www.tensorflow.org/install/gpu) previously.
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To update CUDA to another version, change in `Dockerfile.header`
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the line:
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FROM nvidia/cuda:10.1-base-ubuntu18.04
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and in the `Dockerfile.pytorch` the line:
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cudatoolkit=10.1
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Then re-generate and re-run the image, as closer described above:
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```bash
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./generate-Dockerfile.sh
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./start-local.sh -p 8848
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```
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#### Update Docker-Stack
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The [docker-stacks](https://github.com/jupyter/docker-stacks) are used as a
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submodule within `.build/docker-stacks`. Per default, the head of the commit is reset to a commit on which `gpu-jupyter` runs stable.
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To update the generated Dockerfile to a specific commit, run:
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```bash
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./generate-Dockerfile.sh --commit c1c32938438151c7e2a22b5aa338caba2ec01da2
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```
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To update the generated Dockerfile to the latest commit, run:
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```bash
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./generate-Dockerfile.sh --commit latest
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```
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A new build can last some time and may consume a lot of data traffic. Note, that the latest version may result in
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a version conflict!
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More info to submodules can be found in
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[this tutorial](https://www.vogella.com/tutorials/GitSubmodules/article.html).
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## Issues and Contributing
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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:
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* [filing a new issue](https://github.com/iot-salzburg/gpu-jupyter/issues/new)
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* [open a pull request](https://help.github.com/articles/using-pull-requests/)
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