tfds.benchmark
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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
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.
[{
"type": "thumb-down",
"id": "missingTheInformationINeed",
"label":"Missing the information I need"
},{
"type": "thumb-down",
"id": "tooComplicatedTooManySteps",
"label":"Too complicated / too many steps"
},{
"type": "thumb-down",
"id": "outOfDate",
"label":"Out of date"
},{
"type": "thumb-down",
"id": "samplesCodeIssue",
"label":"Samples / code issue"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
"type": "thumb-up",
"id": "easyToUnderstand",
"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
{"lastModified": "Last updated 2024-04-26 UTC."}
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