Key capabilities

Now that you understand the core concepts – TaskEnvironment, tasks, runs, and apps – here’s an overview of what Flyte can do. Each capability is covered in detail later in the documentation.

Environment and resources

Configure how and where your code runs.

  • Multiple environments: Create separate configurations for different use cases (dev, prod, GPU vs CPU) → Multiple environments

  • Resource specification: Request specific CPU, memory, GPU, and storage for your tasks → Resources

  • Reusable containers: Eliminate container startup overhead with pooled, warm containers for millisecond-level task scheduling → Reusable containers

Deployment

Get your code running remotely.

  • Cloud image building: Build container images remotely without needing local Docker → Container images

  • Code packaging: Your local code is automatically bundled and deployed to remote execution → Packaging

  • Local testing: Test tasks locally before deploying with flyte run --local → How task run works

Data handling

Pass data efficiently between tasks.

  • Files and directories: Pass large files and directories between tasks using flyte.io.File and flyte.io.Dir → Files and directories

  • DataFrames: Work with pandas, Polars, and other DataFrame types natively → DataFrames

Parallelism and composition

Scale out and compose workflows.

  • Fanout parallelism: Process items in parallel using flyte.map or asyncio.gather → Fanout

  • Remote tasks: Call previously deployed tasks from within your workflows → Remote tasks

Security and automation

Manage credentials and automate execution.

  • Secrets: Inject API keys, passwords, and other credentials securely into tasks → Secrets

  • Triggers: Schedule tasks on a cron schedule or trigger them from external events → Triggers

  • Webhooks: Build APIs that trigger task execution from external systems → App usage patterns

Durability and reliability

Handle failures and avoid redundant work.

  • Error handling: Catch failures and retry with different resources (e.g., more memory) → Error handling

  • Retries and timeouts: Configure automatic retries and execution time limits → Retries and timeouts

  • Caching: Skip redundant computation by caching task results based on inputs → Caching

  • Traces: Add fine-grained checkpoints within tasks for recovery and observability → Traces

Apps and serving

Deploy long-running services.

  • FastAPI apps: Deploy REST APIs and webhooks → FastAPI app

  • LLM serving: Serve large language models with vLLM or SGLang → vLLM app, SGLang app

  • Autoscaling: Scale apps up and down based on traffic, including scale-to-zero → Autoscaling apps

  • Streamlit dashboards: Deploy interactive data dashboards → Streamlit app

Notebooks

Work interactively.

  • Jupyter support: Author and run workflows directly from Jupyter notebooks, and fetch workflow metadata (inputs, outputs, logs) → Notebooks

Next steps

Ready to put it all together? Head to First project to build an end-to-end ML system with training tasks and a serving app.