GpuInfo#

class ignite.metrics.GpuInfo[source]#

Provides GPU information: a) used memory percentage, b) gpu utilization percentage values as Metric on each iterations. This metric requires pynvml package of version <12.

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 its accumulated state.
`reset`	Resets the metric to its initial state.
`update`	Updates the metric's state using the passed batch output.

attach(engine, name='gpu', event_name=Events.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 (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, EventEnum]) –

Return type

None

Examples

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(). If metrics’ value is torch tensor, it is explicitly sent to CPU device.

Parameters

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.

Changed in version 0.4.5: metric’s value is put on CPU if torch tensor.

compute()[source]#

Computes the metric based on its 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 its 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[Tensor, Tensor]) – the is the output from the engine’s process function.
Return type: None