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DevOps by Example
31.01.2018
Streamline application deployments with
Kubernetes and Helm
Maciej Sawicki
Machine learning platform as a
service
Our client (stealth mode startup) asked codilime to help them with automated
provisioning ready to work ML/Data Scientist environments.
Key requirements:
● Provide environment easy to use for data science engineers with various experience:
○ easy to use for beginners
○ do not get in the way of power users
● One click deployment of complex ML/DS stacks and tools:
○ Hadoop
○ Spark
○ JupyterHub
○ Seahorse
● Support for Azure, AWS, GCP do not get in the way of power users
Challenges
● Each cloud has its pros and cons and its own challenges
● We shouldn’t make any assumptions about operators environment
● Secrets/credentials management
● CI/CD testing
Technology stack
● Each cloud has its pros and cons and its own challenges
● We shouldn’t make any assumptions about operators environment
● Secrets/credentials management
● CI/CD testing
Technology stack
● Kubernetes
● Terraform
● Helm
● Docker
● Vault
● Ansible
● Python 3
● Jenkins
● Prometheus
● Hadoop, Spark, Scikit-learn, TensorFlow, etc, tl;dr;
Introduction to Helm
Helm helps you manage Kubernetes applications — Helm Charts helps you
define, install, and upgrade even the most complex Kubernetes application.
Charts are easy to create, version, share, and publish — so start using Helm and
stop the copy-and-paste madness.
Kube Apps
Writing own charts
● helm create mychart
● Charts.yaml
● Values.yaml
● Helm template language
Demo
Thank You!
Creative Commons
thenounproject.com
Dinosoft Labs,
Jae Designer,
Oksana Latysheva,
Till Grzegorczyk,
Gregor Cesnar,
Chameleon Desing,
Buzzyrobot,
Sam Smith
DevOps by Example
31.01.2018
Thanks!

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CodiLime Tech Talk - Maciej Sawicki: Streamline application deployments with Kubernetes and Helm

  • 2. Streamline application deployments with Kubernetes and Helm Maciej Sawicki
  • 3. Machine learning platform as a service Our client (stealth mode startup) asked codilime to help them with automated provisioning ready to work ML/Data Scientist environments. Key requirements: ● Provide environment easy to use for data science engineers with various experience: ○ easy to use for beginners ○ do not get in the way of power users ● One click deployment of complex ML/DS stacks and tools: ○ Hadoop ○ Spark ○ JupyterHub ○ Seahorse ● Support for Azure, AWS, GCP do not get in the way of power users
  • 4. Challenges ● Each cloud has its pros and cons and its own challenges ● We shouldn’t make any assumptions about operators environment ● Secrets/credentials management ● CI/CD testing
  • 5. Technology stack ● Each cloud has its pros and cons and its own challenges ● We shouldn’t make any assumptions about operators environment ● Secrets/credentials management ● CI/CD testing
  • 6. Technology stack ● Kubernetes ● Terraform ● Helm ● Docker ● Vault ● Ansible ● Python 3 ● Jenkins ● Prometheus ● Hadoop, Spark, Scikit-learn, TensorFlow, etc, tl;dr;
  • 7. Introduction to Helm Helm helps you manage Kubernetes applications — Helm Charts helps you define, install, and upgrade even the most complex Kubernetes application. Charts are easy to create, version, share, and publish — so start using Helm and stop the copy-and-paste madness.
  • 9. Writing own charts ● helm create mychart ● Charts.yaml ● Values.yaml ● Helm template language
  • 10. Demo
  • 12. Creative Commons thenounproject.com Dinosoft Labs, Jae Designer, Oksana Latysheva, Till Grzegorczyk, Gregor Cesnar, Chameleon Desing, Buzzyrobot, Sam Smith