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Adding support for raw python generator
in addition to Dataset
for pipelines
#14352
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The main goal is to ease the create of streaming data to the pipe. `Dataset` is more involved and pytorch specific. This PR, provides a way to use a python iterator too. This enabled huggingface#14250 but can be proposed as a standalone PR. ```python from transformers import pipeline def read_data(filename): with open(filename, 'r') as f: for line in f: yield f pipe = pipeline("text-classification") for classified in pipe(read_data("large_file.txt")): print("Success ! ", classified) ``` The main caveat of this, is the interaction with `DataLoader` with `num_workers>1`. When you have multiple workers, each receive a copy of the generator (like `IterableDataset`). That means the naive Iterator will fail since all workers iterate on all items of the generator. There are ways to do clever "skipping", but it could be bad still because all workers still do have to pass through all items of the generator (they just ignore items they don't handle), depending on the case it might be bad. Using `num_workers=1` is the simplest fix and if the cost of loading your data is small enough should be good enough. In the above example trying to do smart tricks to skip some lines is unlikely to be a net positive for instance. If there are better ways to do "jumps" on some data, then using `Dataset` is more advised (since then differents workers can just jump themselves).
Narsil
changed the title
# What does this PR do?
Adding support for raw python Nov 10, 2021
generato
r in addition to Dataset
for pipelines
Narsil
changed the title
Adding support for raw python
Adding support for raw python Nov 10, 2021
generato
r in addition to Dataset
for pipelinesgenerator
in addition to Dataset
for pipelines
sgugger
approved these changes
Nov 10, 2021
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Nice improvement, thanks for adding this!
LysandreJik
approved these changes
Nov 11, 2021
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Ok nice! Looks cool and clean. Thanks for working on that, @Narsil.
Albertobegue
pushed a commit
to Albertobegue/transformers
that referenced
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Jan 27, 2022
…or pipelines (huggingface#14352) * Adding support for raw python `generator` in addition to `Dataset` The main goal is to ease the create of streaming data to the pipe. `Dataset` is more involved and pytorch specific. This PR, provides a way to use a python iterator too. This enabled huggingface#14250 but can be proposed as a standalone PR. ```python from transformers import pipeline def read_data(filename): with open(filename, 'r') as f: for line in f: yield f pipe = pipeline("text-classification") for classified in pipe(read_data("large_file.txt")): print("Success ! ", classified) ``` The main caveat of this, is the interaction with `DataLoader` with `num_workers>1`. When you have multiple workers, each receive a copy of the generator (like `IterableDataset`). That means the naive Iterator will fail since all workers iterate on all items of the generator. There are ways to do clever "skipping", but it could be bad still because all workers still do have to pass through all items of the generator (they just ignore items they don't handle), depending on the case it might be bad. Using `num_workers=1` is the simplest fix and if the cost of loading your data is small enough should be good enough. In the above example trying to do smart tricks to skip some lines is unlikely to be a net positive for instance. If there are better ways to do "jumps" on some data, then using `Dataset` is more advised (since then differents workers can just jump themselves). * Adding iterator support for `tf` too.
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The main goal is to ease the create of streaming data to the pipe.
Dataset
is more involved and pytorch specific.This PR, provides a way to use a python iterator too.
This enabled #14250 but can be proposed as a standalone PR.
The main caveat of this, is the interaction with
DataLoader
withnum_workers>1
. When you have multiple workers, each receive a copyof the generator (like
IterableDataset
). That means the naive Iteratorwill fail since all workers iterate on all items of the generator.
There are ways to do clever "skipping", but it could be bad still
because all workers still do have to pass through all items of the
generator (they just ignore items they don't handle), depending on
the case it might be bad.
Using
num_workers=1
is the simplest fix and if the cost of loadingyour data is small enough should be good enough. In the above example
trying to do smart tricks to skip some lines is unlikely to be a net
positive for instance.
If there are better ways to do "jumps" on some data, then using
Dataset
is more advised (since then differents workers can just jumpthemselves).
Fixes # (issue)
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