flyte-sdk-run

Flyte 2 SDK Run Skill

Run workflows, interact with runs, and manage the execution lifecycle.

Grounding References

Resource	URL
Official docs	https://www.union.ai/docs/v2/flyte
Docs index (LLMs)	https://www.union.ai/docs/v2/flyte/llms.txt
SDK API reference	https://www.union.ai/docs/v2/union/api-reference/flyte-sdk/
CLI API reference	https://www.union.ai/docs/v2/union/api-reference/flyte-cli/
flyte-sdk source	https://github.com/flyteorg/flyte-sdk
Example code	https://github.com/unionai/unionai-examples
Flyte MCP tools	Available via the flyte-cluster and flyte-docs MCP servers

Tool Priority

1. Flyte MCP — if the harness has Flyte MCP tools, prefer them over shelling out to the CLI. They cover listing runs, fetching run details and inputs/outputs, polling to completion, executing a task, and aborting a run, and they return structured data instead of text you have to parse.
2. flyte CLI — for local run commands, and anything MCP does not expose
3. Python SDK — for programmatic run control

Running Workflows

Via Python SDK

import flyte

if __name__ == "__main__":
    # Run with defaults from config
    result = flyte.run(main, inputs={"data": ["a", "b", "c"]})
    print(f"Run name: {result.name}")
    print(f"Status: {result.status}")

Via CLI

# Run with local config
flyte run pipeline.py main --data '[1,2,3]'

# Run with specific project/domain
flyte run pipeline.py main --data '[1,2,3]' --project flytesnacks --domain development

# Run with custom run name
flyte run pipeline.py main --data '[1,2,3]' --name my-custom-run

# Run with specific image
flyte run pipeline.py main --data '[1,2,3]' --image ghcr.io/myorg/task:v1.0

# Run with local mode (in-process, no remote)
flyte run --local pipeline.py main --data '[1,2,3]'

# Run with TUI
flyte run --tui --local pipeline.py main --data '[1,2,3]'

# Pass arguments by type
flyte run pipeline.py main \
  --data '[1,2,3]' \
  --learning-rate 0.001 \
  --batch-size 32 \
  --train-data s3://bucket/train.parquet \
  --flag true