yodiaditya / docker-rapids-spark-zeppelin

Simple Dockerfile Zeppelin and Spark Standalone clusters

Docker Zeppelin + Spark + RAPIDS AI

Repository to create spark, zeppelin and rapidsai NVIDIA GPU development environment. Works with NVIDIA both consumer and enterprise GPU attached.

Using the latest version Spark 3.5.1 and Zeppelin 0.11.1 base image

This will running Spark Master and Node to replicate near production environment.
You can extend this with Zeppelin, Spark, Flink, DuckDB, Parquet, Tensorflow, PyTorch and many more.

This already tested with local PC and laptop running on Ubuntu 23.10 and RTX 4090.

Pre-requisites

Clone this git and download required packages into project folder

git clone https://github.com/yodiaditya/docker-zeppelin-spark.git zeppelin-simple
cd zeppelin-simple
wget -c https://github.com/conda-forge/miniforge/releases/download/24.3.0-0/Mambaforge-24.3.0-0-Linux-x86_64.sh
wget -c https://dlcdn.apache.org/spark/spark-3.5.1/spark-3.5.1-bin-hadoop3.tgz

Conda / Mamba Package Cache

Copy folder conda-cache in Docker after first installation

docker cp <container_id>:/opt/zeppelin/conda-cache .

Use root by docker exec -u 0 -it zeppelin bash for mamba or conda installation. While pip should be works using another pip-cache folder.

Building and Runnning

Now you can start to build and run it:

docker compose up --build

To access services : http://localhost:9999

Spark and Zeppelin configuration

• Adjusting any Spark memory and settings, can be done by edit spark-defaults.conf.
• While for Zeppelin, you can create conf and copy into /opt/zeppelin/conf including the files configuration.
• Python and default packages can be found at env_python_3_with_R.yml
• The Dockerfile can be customized easily

Docker Access

Root Login (For apt install and other root permission)

docker exec -u 0 -it zeppelin bash

While, for pip installation, use User Login

docker exec -it zeppelin bash

You can login and do nvtop to see whether GPU is detected.

Docker Installation on Ubuntu

Follow this Docker CE installation : https://docs.docker.com/engine/install/ubuntu/. Don't use snap because NVIDIA Toolkit only works with Docker CE

If you received weird NVIDIA errors when running the dockers, Suggested to uninstall everything and re-install Docker CE. Here are the steps:

sudo snap remove --purge docker
sudo apt-get purge docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-ce-rootless-extras
sudo rm -rf /var/lib/docker
sudo rm -rf /var/lib/containerd
for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
sudo systemctl restart docker

Running docker without root permission

sudo groupadd docker
sudo usermod -aG docker $USER

Docker config for Zeppelin

Docker Configuration https://zeppelin.apache.org/docs/latest/quickstart/docker.html

Because DockerInterpreterProcess communicates via docker's tcp interface. By default, docker provides an interface as a sock file, so you need to modify the configuration file to open the tcp interface remotely.

To listen on both - socket and tcp:

create folder: /etc/systemd/system/docker.socket.d create file 10-tcp.conf touch /etc/systemd/system/docker.socket.d and copy this:

[Socket]
ListenStream=0.0.0.0:2375

restart everything:

sudo systemctl daemon-reload
sudo systemctl stop docker.socket
sudo systemctl stop docker.service
sudo systemctl start docker

Plus are: it us user space systemd drop-in, i.e. would not disappear after upgrade of the docker would allow to use both - socket and tcp connection

If there is DNS issue when download

You can enable another DNS at /etc/docker/daemon.json and add your local DNS or Google DNS

{ "dns" : [ "114.114.114.114" , "8.8.8.8" ] } 

Docker GPU Installation

This steps required to enable GPU in the docker https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-with-apt

Or you for Ubuntu 23.10 Docker GPU Nvidia you can follow this:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

Then configure it

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Run this to test whether its works.

sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

This will enabled in docker-compose.yaml on zeppelin section:

deploy:
  resources:
    reservations:
      devices:
      - driver: nvidia
        device_ids: ['0']
        capabilities: [gpu]

You can read details at https://docs.docker.com/compose/gpu-support/

If not, running the docker and go inside it docker exec -it zeppelin bash and install NVIDIA driver

wget -c https://us.download.nvidia.com/XFree86/Linux-x86_64/550.67/NVIDIA-Linux-x86_64-550.67.run 

CUDA and CUDNN Installation in Docker Zeppelin

I'm using CUDA 11.8 and RTX 3060 / 4090 for this example. We need to download the installation

Start from main project folder

cd zeppelin
mkdir cuda && cd cuda
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run

Next, download CUDNN 8.9.7 and extract it as folder cudnn

https://developer.download.nvidia.com/compute/redist/cudnn/v8.7.0/local_installers/11.8/cudnn-linux-x86_64-8.7.0.84_cuda11-archive.tar.xz
tar -xvvf cudnn-linux-x86_64-8.7.0.84_cuda11-archive.tar.xz
mv cudnn-linux-x86_64-8.7.0.84_cuda11-archive cudnn
rm -rf cudnn-linux-x86_64-8.7.0.84_cuda11-archive.tar.xz

You can see in zeppelin/Dockerfile there is operation to copy this into Docker and set installation