GpuInfo¶

class ignite.contrib.metrics.GpuInfo[source]¶

Provides GPU information: a) used memory percentage, b) gpu utilization percentage values as Metric on each iterations.

Note

In case if gpu utilization reports “N/A” on a given GPU, corresponding metric value is not set.

Examples

# Default GPU measurements
GpuInfo().attach(trainer, name='gpu')  # metric names are 'gpu:X mem(%)', 'gpu:X util(%)'

# Logging with TQDM
ProgressBar(persist=True).attach(trainer, metric_names=['gpu:0 mem(%)', 'gpu:0 util(%)'])
# Progress bar will looks like
# Epoch [2/10]: [12/24]  50%|█████      , gpu:0 mem(%)=79, gpu:0 util(%)=59 [00:17<1:23]

# Logging with Tensorboard
tb_logger.attach(trainer,
                 log_handler=OutputHandler(tag="training", metric_names='all'),
                 event_name=Events.ITERATION_COMPLETED)

Methods

`attach`	Attaches current metric to provided engine.
`completed`	Helper method to compute metric’s value and put into the engine.
`compute`	Computes the metric based on it’s accumulated state.
`reset`	Resets the metric to it’s initial state.
`update`	Updates the metric’s state using the passed batch output.

Return type: None

attach(engine, name='gpu', event_name=<Events.ITERATION_COMPLETED: 'iteration_completed'>)[source]¶

Attaches current metric to provided engine. On the end of engine’s run, engine.state.metrics dictionary will contain computed metric’s value under provided name.

Parameters

engine (ignite.engine.engine.Engine) – the engine to which the metric must be attached
name (str) – the name of the metric to attach
usage – the usage of the metric. Valid string values should be ignite.metrics.metric.EpochWise.usage_name (default) or ignite.metrics.metric.BatchWise.usage_name.
event_name (Union[str, ignite.engine.events.EventEnum]) –

Return type

None

Example:

metric = ...
metric.attach(engine, "mymetric")

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine)

Example with usage:

metric = ...
metric.attach(engine, "mymetric", usage=BatchWise.usage_name)

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine, usage=BatchWise.usage_name)

completed(engine, name)[source]¶

Helper method to compute metric’s value and put into the engine. It is automatically attached to the engine with attach().

Parameters

engine (ignite.engine.engine.Engine) – the engine to which the metric must be attached
name (str) – the name of the metric used as key in dict engine.state.metrics

Return type

None

Changed in version 0.4.3: Added dict in metrics results.

compute()[source]¶

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]¶

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type: None

update(output)[source]¶

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters: output (Tuple[torch.Tensor, torch.Tensor]) – the is the output from the engine’s process function.
Return type: None