Outputs¶

Every entry point returns a StageContext, and writes a set of files to the run's output directory (<output_path>/<experiment_id>/). This page covers both.

The result context¶

After a run, read results off the returned context with ctx.get(...):

Key	What it is
`total_latency`	Total scheduled latency (cycles) for the workload.
`group_latencies`	Per-fusion-group latency breakdown.
`scheduler`	The `SteadyStateScheduler` - the full schedule and timing.
`workload`	The parsed computation graph.
`accelerator`	The parsed hardware model.

ctx = optimize_allocation_co_generic(...)
print(ctx.get("total_latency"))     # e.g. 14344.0
print(ctx.get("group_latencies"))
scheduler = ctx.get("scheduler")

Files written to disk¶

summary.yaml - a machine-readable summary of the run (e.g. total_latency, per-group latencies).
Visualizations (PNG) - workload graph, tiling, and the schedule, written into the run directory.

Schedule trace (Perfetto)¶

The schedule can be exported as a Perfetto JSON trace and opened at https://ui.perfetto.dev to inspect each core's timeline and the inter-core transfers. See stream/visualization/ for the trace and plotting helpers.

Typed IR (for tools and agents)¶

For structured, JSON-serializable output, convert the context's objects into the typed IR models. These are the same models the MCP server returns:

from stream.ir import WorkloadIR, AcceleratorIR, AllocationIR

workload_ir    = WorkloadIR.from_internal(ctx.get("workload"))
accelerator_ir = AcceleratorIR.from_internal(ctx.get("accelerator"))
allocation_ir  = AllocationIR.from_internal(ctx.get("scheduler"))

allocation_data = allocation_ir.model_dump()      # JSON-compatible dict

AllocationIR exposes persona views - .algorithmic_view(), .hardware_view(), .compiler_view() - each shaping the same result for a different consumer. The performance view surfaces bottleneck (compute- vs transfer-bound) cycles and per-node utilization. See Using Stream with an AI agent for details.