- class deeplake.core.transform.Pipeline(functions: List[ComputeFunction])
- eval(data_in, ds_out: Optional[Dataset] = None, num_workers: int = 0, scheduler: str = 'threaded', progressbar: bool = True, skip_ok: bool = False, check_lengths: bool = True, pad_data_in: bool = False, **kwargs)
Evaluates the pipeline on
data_into produce an output dataset
data_in – Input passed to the transform to generate output dataset. Should support __getitem__ and __len__. Can be a Deep Lake dataset.
ds_out (Dataset, optional) –
The dataset object to which the transform will get written. If this is not provided,
data_inwill be overwritten if it is a Deep Lake dataset, otherwise error will be raised.
It should have all keys being generated in output already present as tensors. It’s initial state should be either:
Empty, i.e., all tensors have no samples. In this case all samples are added to the dataset.
All tensors are populated and have same length. In this case new samples are appended to the dataset.
num_workers (int) – The number of workers to use for performing the transform. Defaults to 0. When set to 0, it will always use serial processing, irrespective of the scheduler.
scheduler (str) – The scheduler to be used to compute the transformation. Supported values include: ‘serial’, ‘threaded’, ‘processed’ and ‘ray’. Defaults to ‘threaded’.
progressbar (bool) – Displays a progress bar if
skip_ok (bool) – If
True, skips the check for output tensors generated. This allows the user to skip certain tensors in the function definition. This is especially useful for inplace transformations in which certain tensors are not modified. Defaults to
check_lengths (bool) – If
True, checks whether
ds_outhas tensors of same lengths initially.
pad_data_in (bool) – If
True, pads tensors of
data_into match the length of the largest tensor in
data_in. Defaults to
**kwargs – Additional arguments.
InvalidInputDataError – If
data_inpassed to transform is invalid. It should support __getitem__ and __len__ operations. Using scheduler other than “threaded” with deeplake dataset having base storage as memory as
data_inwill also raise this.
InvalidOutputDatasetError – If all the tensors of
ds_outpassed to transform don’t have the same length. Using scheduler other than “threaded” with deeplake dataset having base storage as memory as
ds_outwill also raise this.
TensorMismatchError – If one or more of the outputs generated during transform contain different tensors than the ones present in ‘ds_out’ provided to transform.
UnsupportedSchedulerError – If the scheduler passed is not recognized. Supported values include: ‘serial’, ‘threaded’, ‘processed’ and ‘ray’.
TransformError – All other exceptions raised if there are problems while running the pipeline.
@deeplake.compute def my_fn(sample_in: Any, samples_out, my_arg0, my_arg1=0): samples_out.my_tensor.append(my_arg0 * my_arg1) # This transform can be used using the eval method in one of these 2 ways:- # Directly evaluating the method # here arg0 and arg1 correspond to the 3rd and 4th argument in my_fn my_fn(arg0, arg1).eval(data_in, ds_out, scheduler="threaded", num_workers=5) # As a part of a Transform pipeline containing other functions pipeline = deeplake.compose([my_fn(a, b), another_function(x=2)]) pipeline.eval(data_in, ds_out, scheduler="processed", num_workers=2)
pad_data_inis only applicable if
data_inis a Deep Lake dataset.