Benchmarks a tf.data.Dataset
.
tfds.core.benchmark(
ds: tf.data.Dataset,
*,
num_iter: tfds.typing.Dim
= None,
batch_size: int = 1
) -> BenchmarkResult
Usage:
ds = tfds.load('mnist', split='train').batch(32).prefetch()
tfds.core.benchmark(ds, batch_size=32)
Reports:
- Total execution time
- Setup time (first warmup batch)
- Number of examples/sec
Args |
ds
|
Dataset to benchmark
|
num_iter
|
Number of iteration to perform (iteration might be batched)
|
batch_size
|
Batch size of the dataset, used to normalize iterations
|
Returns |
statistics
|
The recorded statistics, for eventual post-processing
|