kedro-mlflow
0.9.0
  • Introduction
    • Kedro vs Mlflow
      • What is Kedro?
      • What is Mlflow?
      • A brief comparison between Kedro and Mlflow
        • Configuration and prototyping: Kedro 1 - 0 Mlflow
        • Versioning: Kedro 1 - 1 Mlflow
        • Model packaging and service: Kedro 1 - 2 Mlflow
        • Conclusion: Use Kedro and add Mlflow for machine learning projects
    • Motivation behind the plugin
      • When should I use kedro-mlflow?
      • Why should I use kedro-mlflow?
        • Benchmark of existing solutions
        • Enforcing Kedro principles
  • Installation
    • Install the plugin
      • Pre-requisites
        • Create a virtual environment
        • Check your kedro version
      • Install the plugin
        • Install from PyPI
        • Install from sources
      • Check the installation
      • Available commands
    • Setup your kedro project
      • Create a kedro project
      • Activate kedro-mlflow in your kedro project
        • Setting up the kedro-mlflow configuration file
        • Declaring kedro-mlflow hooks
    • Migration guide between versions
      • Migration from 0.7.x to 0.8.x
      • Migration from 0.6.x to 0.7.x
      • Migration from 0.5.x to 0.6.x
      • Migration from 0.4.x to 0.5.x
      • Migration from 0.4.0 to 0.4.1
      • Migration from 0.3.x to 0.4.x
        • Catalog entries
        • Hooks
        • KedroPipelineModel
  • Getting Started
    • Goal of the tutorial
    • Create an example project
      • Install the plugin in a virtual environment
      • Install the toy project
        • Installation with kedro>=0.16.3
        • Installation with kedro>=0.16.0, <=0.16.2
      • Install dependencies
    • First steps with ``kedro-mlflow``
      • Initialize kedro-mlflow
      • Run the pipeline
      • Open the UI
        • Parameters versioning
        • Journal information
        • Artifacts
      • Going further
  • Experimentation tracking
    • Configure mlflow
      • Context: mlflow tracking under the hood
      • The mlflow.yml file
        • Configure the tracking server
        • Deactivate tracking under conditions
        • Configure mlflow experiment
        • Configure the run
        • Extra tracking configuration
        • Configure the user interface
    • Version parameters
      • Automatic parameters versioning
      • How does MlflowNodeHook operates under the hood?
      • Frequently Asked Questions
        • Will parameters be recorded if the pipeline fails during execution?
        • How are parameters detected by the plugin?
        • How can I register a parameter if I use a TemplatedConfigLoader?
    • Version datasets
      • What is artifact tracking?
      • How to version data in a kedro project?
      • Frequently asked questions
        • Can I pass extra parameters to the MlflowArtifactDataSet for finer control?
        • Can I use the MlflowArtifactDataSet in interactive mode?
        • How do I upload an artifact to a non local destination (e.g. an S3 or blog storage)?
        • Can I log an artifact in a specific run?
        • Can I reload an artifact from an existing run to use it in another run ?
        • Can I create a remote folder/subfolders architecture to organize the artifacts?
    • Version models
      • What is model tracking?
      • How to track models using MLflow in Kedro project?
      • Frequently asked questions?
        • How is it working under the hood?
        • How can I track a custom MLflow model flavor?
        • How can I save model locally and log it in MLflow in one step?
    • Version metrics
      • What is metric tracking?
      • How to version metrics in a kedro project?
        • Saving a single float as a metric with MlflowMetricDataSet
        • Saving the evolution of a metric during training with MlflowMetricHistoryDataSet
        • Saving several metrics with their entire history with MlflowMetricsDataSet
      • How to return metrics from a node?
    • Open the User Interface
      • The mlflow user interface
      • The kedro-mlflow helper
  • Pipeline serving
    • Log a Pipeline as model with ``KedroPipelineModel``
    • Log a Pipeline as model with the CLI
    • Automatically log inference pipeline after training
      • Getting started
      • Complete step by step demo project with code
      • Motivation
  • A mlops framework for continuous model serving
    • Why we need a mlops framework for development lifecycle
      • Machine learning deployment is hard because it comes with a lot of constraints and no adequate tooling
        • Identifying the challenges to address when deploying machine learning
        • A comparison between traditional software development and machine learning projects
      • Deployment issues addressed by kedro-mlflow and their solutions
        • Out of scope
        • Issue 1: The training process is poorly reproducible
        • Issue 2: The data scientist and stakeholders focus on training
        • Issue 3: Inference and training are entirely decoupled
        • Issue 4: Data scientists do not handle business objects
        • Overcoming these problems: support an organisational solution with an efficient tool
    • The architecture of a machine learning project
      • Definition: apps of a machine learning projects
      • Difference between an app and a Kedro pipeline
      • Apps development lifecycle in a machine learning project
        • The data scientist creates at least part of the 3 apps
        • The etl_app
        • The ml_app
        • The user_app
    • An efficient tool for model serving and training / inference synchronization
      • Reminder
      • Enforcing these principles with a dedicated tool
        • Synchronizing training and inference pipeline
        • Packaging and serving a Kedro Pipeline
        • kedro-mlflow’s magic: inference autologging
        • Reuse the model in kedro
  • Interactive use
    • How to use in a notebook
      • Reminder on mlflow’s limitations with interactive use
      • Setup mlflow configuration in your notebook
      • Difference with running through the CLI
      • Guidelines and best practices suggestions
  • Python objects
    • DataSets
      • MlflowArtifactDataSet
      • Metrics DataSets
        • MlflowMetricDataSet
        • MlflowMetricHistoryDataSet
      • Models DataSets
        • MlflowModelLoggerDataSet
        • MlflowModelSaverDataSet
    • Hooks
      • MlflowPipelineHook
      • MlflowNodeHook
    • Pipelines
      • PipelineML and pipeline_ml_factory
    • CLI
      • init
      • ui
    • Configuration
  • API documentation
    • Datasets
      • Artifact DataSet
      • Metrics DataSet
      • Models DataSet
    • CLI
      • init
      • ui
      • modelify
    • Pipelines
    • Custom Mlflow Models
    • Configuration
    • Notebook
    • Hooks
      • Node Hook
      • Pipeline Hook
kedro-mlflow
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  • Introduction
  • Edit on GitHub

Introduction

  • Goal of the tutorial
  • Create an example project
    • Install the plugin in a virtual environment
    • Install the toy project
      • Installation with kedro>=0.16.3
      • Installation with kedro>=0.16.0, <=0.16.2
    • Install dependencies
  • First steps with ``kedro-mlflow``
    • Initialize kedro-mlflow
    • Run the pipeline
    • Open the UI
      • Parameters versioning
      • Journal information
      • Artifacts
    • Going further
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© Copyright 2020, Yolan Honoré-Rougé. Revision c409432f.

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