flyte-sdk-optimize

Flyte 2 SDK Optimize Skill

Optimize Flyte 2 workflows for performance, cost, and reliability.

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

Optimization Strategy Overview

Performance optimization in Flyte follows a hierarchy:

1. Reduce container overhead — use traces for lightweight ops
2. Parallelize work — use flyte.map for fan-out
3. Cache results — use cache="auto" for idempotent tasks
4. Tune resources — set appropriate CPU/memory/GPU
5. Optimize data transfer — choose efficient formats, reduce inline I/O
6. Use reusable containers — shared environments reduce image pull time

Caching

Enable automatic caching

@env.task(cache="auto")  # versioned by function body + inputs
async def preprocess(data: list[str]) -> flyte.io.File:
    ...

Cache key strategies

@env.task(cache="auto")  # default: function body + inputs
async def task_a(data: str) -> flyte.io.File:
    ...

@env.task(cache="override", salt="v2")  # add salt for cache key variation
async def task_b(data: str) -> flyte.io.File:
    ...

@env.task(cache="disable")  # always re-run
async def task_c(data: str) -> flyte.io.File:
    ...

Content-based caching for DataFrames

@env.task(cache="auto")
async def transform(df: flyte.io.DataFrame) -> flyte.io.DataFrame:
    """Cache key includes DataFrame content hash."""
    ...

Ignoring specific inputs in cache key

@env.task(cache="auto", cache_ignore_inputs=["api_key"])
async def fetch_data(api_key: str, url: str) -> flyte.io.File:
    """Don't include api_key in cache key."""
    ...