Kubernetes for data scientist
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This document contains the agenda for the SQLDay 2018 conference. It lists the gold, silver, and bronze sponsors of the event, as well as the strategic partner. It then includes slides on various topics that will be presented, including Kubernetes for data scientists, containers, Docker, GPU and Docker, Kubernetes architecture, deploying to Kubernetes, autoscaling in Kubernetes, Kubeflow, JupyterHub, Tensorflow Training Controller, and Tensorflow Serving. It concludes with a thank you slide.
![SQLDay 2018
Dockerfile
FROM node:9.4.0-alpine
WORKDIR /usr/src/app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 3000
CMD [ "npm", "run", "container" ]](https://image.slidesharecdn.com/kubernetesfordatascientist-180515164352/85/Kubernetes-for-data-scientist-6-320.jpg)
![SQLDay 2018
Sample job
apiVersion: batch/v1
kind: Job
metadata:
labels:
app: samples-tf-mnist-demo
name: samples-tf-mnist-demo
spec:
template:
metadata:
labels:
app: samples-tf-mnist-demo
spec:
containers:
- name: samples-tf-mnist-demo
image: microsoft/samples-tf-mnist-demo:gpu
args: ["--max_steps", "500"]
imagePullPolicy: IfNotPresent
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 1
volumeMounts:
- name: nvidia
mountPath: /usr/local/nvidia
restartPolicy: OnFailure
volumes:
- name: nvidia
hostPath:
path: /usr/local/nvidia https://docs.microsoft.com/en-us/azure/aks/gpu-cluster](https://image.slidesharecdn.com/kubernetesfordatascientist-180515164352/85/Kubernetes-for-data-scientist-25-320.jpg)
Kubernetes for data scientist
- 1. SQLDay 2018 GOLD SPONSORS SILVER SPONSORS BRONZE SPONSOR STRATEGIC PARTNER
- 2. SQLDay 2018 Kubernetes for data scientist Łukasz Kałużny MVP: Microsoft Azure Cloud Technology Leader @ BlueSoft kaluzny.io | @kaluzaaa
- 4. SQLDay 2018 Containers Type 1 Hardware Hypervisor 1 VM VM VM Virtual machine Guest OS Dependencies Application Hardware Host OS Docker Engine Dependency 1 Dependency 2 C C C C C Container App dependencies Application XYZ Virtualization
- 5. SQLDay 2018 The container advantage For developers Fast iteration Agile delivery Immutability For IT Cost savings Efficient deployment Elastic bursting
- 6. SQLDay 2018 Dockerfile FROM node:9.4.0-alpine WORKDIR /usr/src/app COPY package*.json ./ RUN npm install COPY . . EXPOSE 3000 CMD [ "npm", "run", "container" ]
- 7. SQLDay 2018 GPU and Docker https://devblogs.nvidia.com/nvidia-docker-gpu-server-application-deployment-made-easy/
- 8. SQLDay 2018
- 9. SQLDay 2018 The elements of orchestration Scheduling Affinity/anti- affinity Health monitoring Failover Scaling Networking Service discovery Coordinated app upgrades
- 10. SQLDay 2018 Kubernetes: the de-facto orchestrator Portable Public, private, hybrid, multi-cloud Extensible Modular, pluggable, hookable, composable Self-healing Auto-placement, auto-restart, auto-replication, auto-scaling
- 11. SQLDay 2018 Kubernetes Architecture Components api-server etcd controller-manager scheduler kubelet kube-proxy docker dns master components node components
- 12. SQLDay 2018 Pod
- 13. SQLDay 2018 rating-api port 3000 rating-web port 8080 rating-db port 27017
- 14. SQLDay 2018 app=heroes-web , tier=web port label image azurecr.io/rating-web:v1 heroes-web heroes-web-1 port 8080 heroes-web-2 port 8080
- 15. SQLDay 2018 port app=heroes-web, tier=weblabel image azurecr.io/rating-web:v1 heroes-web heroes-web-1 port 8080 heroes-web-2 port 8080 Heroes-web selector app=heroes-web port IP 8080:8080 10.0.2.20
- 16. SQLDay 2018 micro-web port 8000 rating-web port 8080 rating-web port 8080 rating-web port 8080 rating-web port 8080 rating-web port 8080 api-server
- 17. SQLDay 2018 k8s objects - computing • Cron Jobs • Daemon Sets • Deployments • Jobs • Pods • Stateful Sets
- 18. SQLDay 2018 k8s objects - network • Ingresses • Services
- 19. SQLDay 2018 k8s objects - config • Config Map • Secrets
- 20. SQLDay 2018 YAML - Deployment apiVersion: apps/v1 kind: Deployment metadata: name: my-nginx spec: selector: matchLabels: run: my-nginx replicas: 2 template: metadata: labels: run: my-nginx spec: containers: - name: my-nginx image: nginx ports: - containerPort: 80 https://kubernetes.io/docs/concepts/services-networking/connect-applications-service/#exposing-pods-to-the-cluster
- 21. SQLDay 2018 YAML - Services apiVersion: v1 kind: Service metadata: name: my-nginx labels: run: my-nginx spec: ports: - port: 80 protocol: TCP selector: run: my-nginx https://kubernetes.io/docs/concepts/services-networking/connect-applications-service/#creating-a-service
- 23. SQLDay 2018 How to deploy k8s in Azure Azure Container Servcies acs-engine Azure Kubernetes Services
- 24. SQLDay 2018 AKS i GPU – How to start? az group create --name myGPUCluster --location eastus az aks create --resource-group myGPUCluster --name myGPUCluster --node-vm-size Standard_NC6 az aks get-credentials --resource-group myGPUCluster --name myGPUCluster https://docs.microsoft.com/en-us/azure/aks/gpu-cluster
- 25. SQLDay 2018 Sample job apiVersion: batch/v1 kind: Job metadata: labels: app: samples-tf-mnist-demo name: samples-tf-mnist-demo spec: template: metadata: labels: app: samples-tf-mnist-demo spec: containers: - name: samples-tf-mnist-demo image: microsoft/samples-tf-mnist-demo:gpu args: ["--max_steps", "500"] imagePullPolicy: IfNotPresent resources: limits: alpha.kubernetes.io/nvidia-gpu: 1 volumeMounts: - name: nvidia mountPath: /usr/local/nvidia restartPolicy: OnFailure volumes: - name: nvidia hostPath: path: /usr/local/nvidia https://docs.microsoft.com/en-us/azure/aks/gpu-cluster
- 26. SQLDay 2018 Autoscaling in Kubernetes https://medium.com/@wbuchwalter/autoscaling-a-kubernetes-cluster-created-with-acs-engine-on-azure-5e24ddc6402e
- 27. SQLDay 2018 Autoscaling in Kubernetes https://medium.com/@wbuchwalter/autoscaling-a-kubernetes-cluster-created-with-acs-engine-on-azure-5e24ddc6402e
- 28. SQLDay 2018 Autoscaling in Kubernetes https://medium.com/@wbuchwalter/autoscaling-a-kubernetes-cluster-created-with-acs-engine-on-azure-5e24ddc6402e
- 29. SQLDay 2018 kubeflow „The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.”
- 30. SQLDay 2018 kubeflow JupyterHub to create and manage interactive Jupyter notebooks. Project Jupyter is a non-profit, open-source project to support interactive data science and scientific computing across all programming languages. TensorFlow Training Controller that can be configured to use either CPUs or GPUs and dynamically adjusted to the size of a cluster with a single setting TensorFlow Serving container to export trained TensorFlow models to Kubernetes
- 31. SQLDay 2018 JupyterHub • A single hub & proxy for managing interactive sessions • Can run entirely within Kubernetes - notebooks are backed by Kubernetes pods • Can request required resources - CPUs, GPUs, etc • Has pluggable authentication (oauth, kdc, etc) Made possible by: https://github.com/jupyterhub/kubespawner
- 32. SQLDay 2018 Tensorflow Training Controller • A Kubernetes “operator” to help run distributed/non-distributed TF training. • Exposes an API through a CustomResourceDefinition • Controller manages complexity of distributed training using Tensorflow. Made possible by: https://github.com/tensorflow/k8s
- 33. SQLDay 2018 Tensorflow Serving • A Kubernetes Deployment that can serve saved models • Deployment - replicas can be scaled.
- 35. SQLDay 2018 DEMO Autoscaling in Kubernetes
- 37. SQLDay 2018 GOLD SPONSORS SILVER SPONSORS BRONZE SPONSOR STRATEGIC PARTNER