---
title: "BenchReport"
sidebar_label: "BenchReport"
description: "The clika_runtime.graph BenchReport class."
---

<!-- Generated by tools/api_reference/generate_api_docs.py. Do not edit. -->

One benchmark's measurement. Times are wall-clock milliseconds around each run, with every output synchronized before the clock stops, so a run's time includes the device work its outputs wait on. peak_rss_bytes is the whole process's peak resident set at the end of the benchmark, not the graph's own footprint. device_memory is the runtime's memory-pool snapshot for the device after the runs; device_memory_valid is False where none was available. to_csv() and to_json() render the report under the table's column names; to_dict() is that JSON as a Python dict.

## `batch_axis` (property)

The axis items were counted along, or -1.

## `device` (property)

Where the inputs were placed.

## `device_memory` (property)

The runtime's memory-pool counters for the device (a MemoryStats).

## `device_memory_valid` (property)

device_memory holds a snapshot.

## `fill` (property)

How synthesized inputs were filled; for caller-provided tensors the option's value, not applied.

## `first_call_ms` (property)

The first run of the call, warmup or measured.

## `items_per_iteration` (property)

The first input's extent on batch_axis, else 1.

## `iteration_ms` (property)

Every measured run's milliseconds, in order.

## `iterations` (property)

Measured runs.

## `max_ms` (property)

The slowest measured run.

## `mean_ms` (property)

The arithmetic mean of iteration_ms.

## `min_ms` (property)

The fastest measured run.

## `outputs` (property)

The output shapes produced, output_names() order.

## `outputs_on_device` (property)

The outputs live on the device (False: on the host).

## `outputs_reused` (property)

The measured runs wrote preallocated outputs.

## `p50_ms` (property)

The median of iteration_ms.

## `p95_ms` (property)

The 95th percentile of iteration_ms (linear interpolation).

## `p99_ms` (property)

The 99th percentile of iteration_ms (linear interpolation).

## `peak_rss_bytes` (property)

The process's peak resident set, in bytes.

## `shapes` (property)

The input shapes fed, input_names() order.

## `stddev_ms` (property)

The population standard deviation of iteration_ms.

## `threads` (property)

The runtime's CPU thread count during the runs.

## `throughput_items_per_s` (property)

items_per_iteration / (p50_ms / 1000).

## `warmup` (property)

Untimed runs.

## `__init__`

```python
__init__(self, /, *args, **kwargs)
```

Initialize self.  See help(type(self)) for accurate signature.

## `iterations_csv`

```python
iterations_csviterations_csv(self) -> str
```

iterations_csv(self) -> str

iterations_csv() -> str

One CSV row per measured run: columns iteration (0-based) and ms.

## `to_csv`

```python
to_csvto_csv(self) -> str
```

to_csv(self) -> str

to_csv() -> str

The report as one CSV row under a header: the shapes and outputs as name:d0xd1x... entries joined with ';', the device as API:index, the fill as zeros / uniform, then the timing, throughput and memory columns. The same columns for every report, so a sweep's rows append.

## `to_dict`

```python
to_dictto_dict(self) -> object
```

to_dict(self) -> object

to_dict() -> dict

The to_json() object as a Python dict.

## `to_json`

```python
to_jsonto_json(self) -> str
```

to_json(self) -> str

to_json() -> str

Every field as a JSON object under the table's column names: the shapes and outputs as arrays of {name, dims}, iteration_ms as an array, the device as API:index, the memory counters as the nested device_memory object.