tfds.benchmark | TensorFlow Datasets (original) (raw)
tfds.benchmark
Benchmarks any iterable (e.g tf.data.Dataset).
tfds.benchmark(
ds: Iterable[Any], *, num_iter: Optional[int] = None, batch_size: int = 1
) -> BenchmarkResult
Used in the notebooks
| Used in the tutorials |
|---|
| TensorFlow Datasets |
Usage:
ds = tfds.load('mnist', split='train')
ds = ds.batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)
tfds.benchmark(ds, batch_size=32)
Reports:
- Total execution time
- Setup time (first warmup batch)
- Number of examples/sec
| Args | |
|---|---|
| ds | Dataset to benchmark. Can be any iterable. Note: The iterable will be fully consumed. |
| 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 |
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Last updated 2024-04-26 UTC.