Kubeflow Kale: from Jupyter notebooks to Complex pipelines

`Kubeflow Kale` lets you deploy Jupyter Notebooks that run on your laptop to Kubeflow Pipelines, without requiring any of the Kubeflow SDK boilerplate.

You can define pipelines by annotating notebook’s code cells and clicking a deployment button in the Jupyter UI.

Kale will take care of converting the Notebook to a valid Kubeflow Pipelines deployment, taking care of resolving data dependencies and managing the pipeline’s lifecycle.

Transcript

1. Kubeflow Kale @leriomaggio valeriomaggio@gmail.com @leriomaggio Valerio Maggio Scaling Jupyter Notebooks to Complex Pipelines Stefano Fioravanzo
2. ML + Dev + Ops
3. Reference for the Introductory (MLOps) part
4. ML is hard!
5. “toy” ML Pipeline Data Loading Preprocessing Model 
 learning API Interface
 (Model Serving) Adapted from “What about tests in Machine Learning projects?” Sarah Diot-Girard - EuroSciPy 2019
6. Data Analysis Data Transformation Data Validation Data Splitting Model Building Model Validation Train 
 at scale Roll-out Serving Monitoring Data Loading Logging Preprocessing Model Learning Model Serving Adapted from “Managing Machine Learning in Production with Kubeflow and MLOps” David Aronchick - KubeCon EU 2019
7. Adapted from “Managing Machine Learning in Production with Kubeflow and MLOps” David Aronchick - KubeCon EU 2019
10. Cowboys and Rangers can be friends Adapted from “Managing Machine Learning in Production with Kubeflow and MLOps” David Aronchick - KubeCon EU 2019
11. Adapted from “Managing Machine Learning in Production with Kubeflow and MLOps” David Aronchick - KubeCon EU 2019
12. ML/DL Specialised Infrastructure 1. Specialised Chipset 2. Specialised Software 3. (Specialised?) Computing Platform
13. State of the Art Technologies Support Machine Learning workloads in the Cloud Cloud
 Agnostic Polyaxon Kubeflow Kubernetes
14. Kubernetes Kubernetes Master API Server Scheduler Controllers Cluster State Kubernetes Node1 Node2 Node3 Remote Configuration Cluster Settings Declarative API Kubernetes: Container Orchestration Engine Developed internally at Google and open sourced in 2014
15. Extending Core Components Kubernetes Supported Languages: • GoLang • Python • Javascript • Java • Rust • … Remote Configuration Build custom component Kubernetes Master API Server Scheduler Controllers Cluster State CustomComp Automate Application specific services and tasks with custom components
16. Polyaxon Platform for managing the whole lifecycle of large scale deep learning and machine learning applications. https://github.com/polyaxon/polyaxon
17. Distributed Training Hyperparameter Tuning Model Serving Jupyter Notebook Kubernetes Kubeflow Infrastructure Infrastructure management via declarative API Kubeflow: Developed internally at Google and open sourced in 2016 https://github.com/kubeflow/kubeflow Kubeflow Kubernetes Components for Machine Learning
18. Distributed DL Training + Example of distributed deep learning training on Kubeflow Worker1 TF Distributed Training model.py DL Training Controller Cluster Settings Deploy Kubernetes Master API Server Similar process for other DL frameworks: PyTorch, MXNet, Caffe, … Worker2 Parameter Server User provided model
20. Introducing Kubeflow Pipelines: announced by Google Cloud. 
 A platform for building and deploying portable and scalable end-to-end ML workflows, based on containers. pipelines The Kubeflow Pipelines platform consists of: • User interface for managing and tracking experiments, jobs, and runs • Engine for scheduling multi-step ML workflows • SDK for defining and manipulating pipelines and components • Jupyter Notebooks for interacting with the system using the SDK • Integration with the other tools in the Kubeflow Toolkit 
 (es: tf-operator for distributed training) https://github.com/kubeflow/pipelines/
21. @dsl.pipeline( name=‘ML Workflow', ) def xgb_train_pipeline( output, project, region='us-central1', train_data='gs://ml-pipeline-playground/sfpd/train.csv', …, ): with dsl.ExitHandler(exit_op=delete_cluster_op): create_cluster_op = CreateClusterOp('create-cluster', project, region, output) analyze_op = AnalyzeOp('analyze', project, region, create_cluster_op.output, schema, train_data, '%s/{{workflow.name}}/analysis' % output) transform_op = TransformOp('transform', project, region, create_cluster_op.output, train_data, eval_data, target, analyze_op.output, '%s/{{workflow.name}}/transform' % output) train_op = TrainerOp('train', project, region, create_cluster_op.output, transform_op.outputs['train'],transform_op.outputs['eval'], target, analyze_op.output, workers, rounds, '%s/{{workflow.name}}/model' % output) predict_op = PredictOp('predict', project, region, create_cluster_op.output, transform_op.outputs['eval'], train_op.output, target, analyze_op.output, '%s/{{workflow.name}}/predict' % output) cm_op = ConfusionMatrixOp('confusion-matrix', predict_op.output, '%s/{{workflow.name}}/confusionmatrix' % output) roc_op = RocOp('roc', predict_op.output, true_label, '%s/{{workflow.name}}/roc' % output)
22. Kubeflow Python SDK Standalone Python function Create Lightweight Components as Python Standalone functions transpose SDK
23. create-matrices transpose matmul Authoring Pipelines SDK
24. KALE (/ˈkeɪliː/) Kubeflow Automated PipeLines Engine https://kubeflow-kale.github.io
25. KALE Abstracting from KubeFlow Pipelines SDK Kale Kale From (local) Jupyter Notebooks to (remote) Kubeflow Pipelines Kubeflow
26. Jupyter Notebook to KFP Local Jupyter Notebook On cloud Kubeflow Pipeline Kale transpose matmul create-matrices
28. Kale recognised list of tags https://github.com/kubeflow-kale/jupyterlab-kubeflow-kale Jupyter-lab extension
29. nbparser static_analyzer marshal codegen Kale main components Kale Derive pipeline structure Identify dependencies Inject data objects Generate & Deploy Pipeline networkx Pyflakes Odo dill Jinja2 support for HyperParam Tuning
30. static_analysis Pipeline Step create-matrices Pipeline Step transpose Pipeline Step matmul Kale Pipeline Step create-matrices Pipeline Step transpose Pipeline Step matmul marshal
31. Marshalling PatternDispatcher TypeDispatcher
32. codegen Sample template used to generate a standalone function Kale Code generation via templates
33. Examples https://github.com/kubeflow-kale/kale/tree/master/examples https://github.com/kubeflow-kale/examples
34. Thank you very much 
 for your kind attention @leriomaggio valeriomaggio@gmail.com @leriomaggio https://kubeflow-kale.github.io