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User Guide
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Flyte
  • Getting Started
  • User Guide
  • Tutorials
  • Concepts
  • Deployment
  • API Reference
  • Community

Getting Started

  • Introduction to Flyte
  • Flyte Fundamentals
    • Tasks, Workflows and LaunchPlans
    • Creating a Flyte Project
    • Registering Workflows
    • Registration Patterns
    • Running and Scheduling Workflows
    • Visualizing Artifacts
    • Optimizing Tasks
    • Extending Flyte
  • Core Use Cases
    • Data Engineering
    • Machine Learning
    • Analytics

User Guide

  • 📖 User Guide
  • 🌳 Environment Setup
  • 🔤 Basics
    • Hello, World!
    • Tasks
    • Workflows
    • Launch Plans
    • Imperative Workflows
    • Documenting Workflows
    • Shell Tasks
    • Named Outputs
  • ⌨️ Data Types and IO
    • Flyte File
    • Flyte Directory
    • Structured Dataset
    • Data Class
    • Attribute Access
    • PyTorch Type
    • Enum Type
    • Pickle Type
  • 🔮 Advanced Composition
    • Conditions
    • Chain Flyte Entities
    • SubWorkflows
    • Dynamic Workflows
    • Map Tasks
    • Implementing Merge Sort
    • Eager Workflows
    • Decorating Tasks
    • Decorating Workflows
    • Intratask Checkpoints
    • Waiting for External Inputs
  • 🧩 Customizing Dependencies
    • Building Docker Images without a Dockerfile
    • Using Raw Containers
    • Multiple Container Images in a Single Workflow
  • 🏡 Development Lifecycle
    • Writing Agents in Python
    • Pulling Private Images
    • Caching
    • Cache Serializing
    • Decks
    • Creating a New Project
    • Running a Task
    • Running a Workflow
    • Running a Launchplan
    • Inspecting Workflow and Task Executions
    • Debugging Workflow and Task Executions
  • ⚗️ Testing
    • Mock Tasks for Testing
  • 🚢 Productionizing
    • Customizing Task Resources
    • Reference Task
    • Reference Launch Plan
    • Notifications
    • Scheduling Workflows
    • Configuring Logging Links in UI
    • Configuring Flyte to Access GPUs
    • Using Spot/Preemptible Instances
    • Using Secrets in a Task
    • Adding Workflow Labels and Annotations
  • 🏗 Extending
    • Writing Custom Flyte Types
    • Pre-built Container Task Plugin
    • User Container Task Plugin
    • Writing Backend Extensions
    • Container Interface
  • 📝 Contributing

Tutorials

  • Tutorials
  • Model Training
    • Diabetes Classification
      • Train and Validate a Diabetes Classification XGBoost Model
    • House Price Regression
      • Predicting House Price in a Region Using XGBoost
      • Predicting House Price in Multiple Regions Using XGBoost and Dynamic Workflows
    • MNIST Classification With PyTorch and W&B
      • Single Node, Single GPU Training
      • Single Node, Multi GPU Training
    • NLP Processing
      • Word Embeddings and Topic Modelling with Gensim
    • Forecasting Rossman Store Sales with Horovod and Spark
      • Data-Parallel Distributed Training Using Horovod on Spark
  • Feature Engineering
    • EDA, Feature Engineering, and Modeling With Papermill
      • Flyte Pipeline in One Jupyter Notebook
      • EDA and Feature Engineering in Jupyter Notebook and Modeling in a Flyte Task
      • EDA and Feature Engineering in One Jupyter Notebook and Modeling in the Other
      • Supermarket Regression Notebook
      • Supermarket Regression 1 Notebook
      • Supermarket Regression 2 Notebook
    • Feast Integration
      • Feature Engineering Tasks
      • Flyte Pipeline with Feast
      • How to Trigger the Feast Workflow using FlyteRemote
  • Bioinformatics
    • Nucleotide Sequence Querying with BLASTX
      • BLASTX Example
  • Flytelab
    • Weather Forecasting

Integrations

  • Integrations
  • Airflow Provider
    • FlyteOperator Example
  • AWS Athena
    • Athena Query
  • AWS Batch
    • AWS Batch
  • AWS Sagemaker Pytorch
    • Distributed Pytorch on Sagemaker
  • AWS Sagemaker Training
    • Built-in Sagemaker Algorithms
    • Custom Sagemaker Algorithms
  • BigQuery
    • BigQuery Query
  • Dask
    • Running a Dask Task
  • Databricks
    • Running Spark on Databricks
  • DBT
    • dbt example
  • Dolt
    • Quickstart
    • Dolt Branches
  • DuckDB
    • DuckDB Example
  • Great Expectations
    • Task Example
    • Type Example
  • Hive
    • Hive Tasks
  • Kubernetes Pods
    • Pod Example
  • MLFlow
    • MLflow Example
  • Memory Machine Cloud
    • Memory Machine Cloud
  • Modin
    • KNN Classifier
  • MPI
    • Running Distributed Training Using Horovod and MPI
  • ONNX
    • PyTorch Example
    • ScikitLearn Example
    • TensorFlow Example
  • Papermill
    • Jupyter Notebook Tasks
    • Simple Papermill Notebook
  • Pandera
    • Basic Schema Example
    • Validating and Testing Machine Learning Pipelines
  • PyTorch Distributed
    • Run PyTorch Distributed
  • Ray
    • Running Ray Tasks
  • Snowflake
    • Quering data in Snowflake
  • Spark
    • Running a Spark Task
    • Converting a Spark DataFrame to a Pandas DataFrame
  • SQL
    • Sqlite3
    • SQLAlchemy
  • TensorFlow Distributed
    • Run Distributed TensorFlow Training
  • WhyLogs
    • whylogs Example

Tags

  • Example Tags
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Development Lifecycle#

In this section, you will discover Flyte’s features that aid in local workflow development. You will gain an understanding of concepts like caching, the Flyte remote API, Agents, Decks and more.

Writing Agents in Python

Pulling Private Images

Caching

Cache Serializing

Decks

Creating a New Project

Running a Task

Running a Workflow

Running a Launchplan

Inspecting Workflow and Task Executions

Debugging Workflow and Task Executions
Next
Writing Agents in Python
Previous
Multiple Container Images in a Single Workflow
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