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.

TUI agent run

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 my_pipeline.py my_task --arg value

# With the interactive TUI
flyte run --local --tui my_pipeline.py my_task --arg value

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

Drop --local to run on a remote cluster when one is configured:

flyte run my_pipeline.py my_task --arg value

Terminal UI

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

flyte run --local --tui my_pipeline.py pipeline --epochs 5

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

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 remote

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

Aspect	Local	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

When you’re ready to run on a remote Flyte cluster, see Connecting to a cluster to configure the CLI and SDK.