Advanced Pipeline Examples¶
This page covers more advanced examples for dataset pipelines.
Pre-repeat vs post-repeat transforms¶
Transformations prior to the call to
.repeat() will be cached. However, note that the initial read will not be cached unless there is a subsequent transformation or
.fully_executed() call. Transformations made to the DatasetPipeline after the repeat will always be executed once for each repetition of the Dataset.
For example, in the following pipeline, the
map(func) transformation only occurs once. However, the random shuffle is applied to each repetition in the pipeline. However, if we omitted the map transformation, then the pipeline would re-read from the base data on each repetition.
Global per-epoch shuffling is an expensive operation that will slow down your ML ingest pipeline, prevents you from using a fully-streaming ML ingest pipeline, and can cause large increases in memory utilization and spilling to disk; only use global per-epoch shuffling if your model benefits from it! If your model doesn’t benefit from global per-epoch shuffling and/or you run into performance or stability issues, you should try out windowed or local per-epoch shuffling.
# Create a pipeline that loops over its source dataset indefinitely. pipe: DatasetPipeline = ray.data \ .read_datasource(...) \ .map(func) \ .repeat() \ .random_shuffle_each_window() @ray.remote(num_gpus=1) def train_func(pipe: DatasetPipeline): model = MyModel() for batch in pipe.iter_torch_batches(): model.fit(batch) # Read from the pipeline in a remote training function. ray.get(train_func.remote(pipe))
Result caching only applies if there are transformation stages prior to the pipelining operation. If you
window() a Dataset right after the read call (e.g.,
ray.data.read_parquet(...).repeat()), then the read will still be re-executed on each repetition. This optimization saves memory, at the cost of repeated reads from the datasource. To force result caching in all cases, use
Changing Pipeline Structure¶
Sometimes, you may want to change the structure of an existing pipeline. For example, after generating a pipeline with
ds.window(k), you may want to repeat that windowed pipeline
n times. This can be done with
ds.window(k).repeat(n). As another example, suppose you have a repeating pipeline generated with
ds.repeat(n). The windowing of that pipeline can be changed with
ds.repeat(n).rewindow(k). Note the subtle difference in the two examples: the former is repeating a windowed pipeline that has a base window size of
k, while the latter is re-windowing a pipeline of initial window size of
ds.num_blocks(). The latter may produce windows that span multiple copies of the same original data if
preserve_epoch=False is set:
# Window followed by repeat. ray.data.from_items([0, 1, 2, 3, 4]) \ .window(blocks_per_window=2) \ .repeat(2) \ .show_windows() # -> # ------ Epoch 0 ------ # === Window 0 === # 0 # 1 # === Window 1 === # 2 # 3 # === Window 2 === # 4 # ------ Epoch 1 ------ # === Window 3 === # 0 # 1 # === Window 4 === # 2 # 3 # === Window 5 === # 4 # Repeat followed by window. Since preserve_epoch=True, at epoch boundaries # windows may be smaller than the target size. If it was set to False, all # windows except the last would be the target size. ray.data.from_items([0, 1, 2, 3, 4]) \ .repeat(2) \ .rewindow(blocks_per_window=2, preserve_epoch=True) \ .show_windows() # -> # ------ Epoch 0 ------ # === Window 0 === # 0 # 1 # === Window 1 === # 2 # 3 # === Window 2 === # 4 # ------ Epoch 1 ------ # === Window 3 === # 0 # 1 # === Window 4 === # 2 # 3 # === Window 5 === # 4