Running locally

Flyte runs locally with no cluster or Docker needed. Install the SDK, write tasks, and run them on your machine. When you’re ready to scale, drop the --local flag and the same code runs on a remote cluster with GPUs.

Getting started

If you haven’t already, install the SDK and configure local persistence as described in the Quickstart.

Running tasks locally

The --local flag tells Flyte to execute a task in your local Python environment rather than on a remote cluster. Add --tui to launch the interactive Terminal UI for real-time monitoring.

Basic local execution:

flyte run --local hello.py main

With the interactive TUI:

flyte run --local --tui hello.py main

You can also run tasks programmatically using the Python SDK with flyte.run(). See Run and deploy tasks for details.

Terminal UI

The TUI is an interactive split-screen dashboard. Task tree on the left, details and logs on the right.

flyte run --local hello.py main

TUI agent run

What you see:

  • Task tree with live status: ● running, ✓ done, ✗ failed
  • Cache indicators: $ cache hit, ~ cache enabled but missed
  • Live logs: print() output streams in real time
  • Details panel: inputs, outputs, timing, report paths
  • Traced sub-tasks: child nodes for @flyte.trace decorated functions

Keyboard shortcuts:

Key Action
q Quit
d Details tab
l Logs tab

Exploring past runs

If you created a config file via flyte create config --local-persistence, Flyte persists the inputs and outputs of every task run locally, so you can always go back and inspect what a task received and produced. Launch the TUI on its own to browse past runs, compare inputs and outputs, and review reports:

flyte start tui

What works locally

Most Flyte features work in both local and remote execution. The table below summarizes how each feature behaves locally.

Feature Local behavior Details
Caching Outputs stored in local SQLite, keyed on task name and inputs. Same inputs = instant results. Caching
Tracing @flyte.trace functions appear as child nodes in the TUI with their own timing, inputs, and outputs. Traces
Reports HTML files saved locally. TUI shows the file path. Reports
Serving Run apps locally with python serve.py or flyte.with_servecontext(mode="local"). Serve and deploy apps
Plugins Same decorators and APIs as remote. Secrets come from environment variables. Integrations
Secrets Read from .env files or environment variables. No flyte create secret needed. Secrets

Local to devbox/remote

The same code runs in both environments. Here’s what changes:

Aspect Local Devbox/Remote
Run pipeline flyte run --local flyte run
TUI --tui flag Dashboard in Flyte UI
Caching Local SQLite Cluster-wide distributed cache
Reports Local HTML files Rendered in the Flyte UI
Serving python serve.py flyte deploy serve.py env
Secrets .env / environment variables flyte create secret / flyte.Secret
Compute Your CPU/GPU Resources(cpu=2, memory="4Gi", gpu=1)

The TaskEnvironment is the bridge. Locally, image and resource settings are ignored. On the cluster, Flyte builds containers and allocates compute from the same definition.

Next steps