# profile

Source: https://www.tensorplay.cn/docs/generated/tensorplay.profiler.profiler.profile.html

```python
class tensorplay.profiler.profiler.profile(enabled=True, *, activities=None, schedule=None, on_trace_ready=None, record_shapes=False, profile_memory=False, with_stack=False, with_flops=False, with_modules=False, use_device=None, gpu_timing=None, gpu_trace=None, with_samples=False)
```

Context manager that records dispatched operations and annotations.

```python
device_kernels(sort_by=None)
```

What the device ran, by the name the device knows it by.

```python
property device_time_by_span
```

For each collected span, the device time of the work inside it.

The per-span answer, for a caller that has a span and wants what it cost
on the device rather than what it cost to ask for.

```python
kineto_results()
```

The device’s own record of what it did, in a form meant to be read.

Named for what it is rather than for how it was collected: a caller
wants the device’s work, wants to know which piece of it belongs to which
operation, and wants the two joined by something exact.  What the
collector gathered is exactly that, so it is handed over as records that
answer those questions, and the join is already made rather than left to
the caller to reconstruct.

```python
step()
```

Advance the configured step schedule.

```python
table(sort_by=None, row_limit=100, header=None, max_src_column_width=75, max_name_column_width=55, max_shapes_column_width=80, top_level_events_only=False, time_unit=None)
```

Render the session’s spans, one row per span.

Every span gets a row rather than one row per name, so the per-span
captures the session holds – the shapes an operation was given, the
frames it was called from, the memory it allocated – can be printed
against the span they describe.  key_averages() is the aggregated
view of the same recording.