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Simple ETL Pipeline for PyTorch

Project description

Torchpipe: Simple ETL Pipeline for PyTorch

Torchpipe is a simple ETL framework, especially for PyTorch. It is an alternative to tf.data in TensorFlow

Requirements

  • Python 3.6+
  • PyTorch 1.2+

Installation

To install Torchpipe:

pip install torchpipe

Basic Usage

import torchpipe as tp

d = tp.Dataset(range(1_000))
d.shuffle(buffer_size=100).batch(batch_size=10).first() # [4, 44, 71, 92, 97, 86, 43, 57, 60, 62]

Usage with PyTorch

from torch.utils.data import DataLoader
import torchpipe as tp


d = tp.Dataset(range(1_000)).shuffle(100).batch(10)
loader = DataLoader(d, num_workers=4, collate_fn=lambda x: x)
for x in loader:
    ...

Usage with LineFlow

You can use Torchpipe with pre-defined datasets in LineFlow:

from torch.utils.data import DataLoader
from lineflow.datasets.wikitext import cached_get_wikitext
import torchpipe as tp

dataset = cached_get_wikitext('wikitext-2')
# Preprocessing dataset
train_data = tp.Dataset(dataset['train']) \
    .flat_map(lambda x: x.split() + ['<eos>']) \
    .window(35) \
    .parallel() \
    .shuffle(64 * 100) \
    .batch(64)

# Iterating dataset
loader = DataLoader(train_data, num_workers=4, collate_fn=lambda x: x)
for x in loader:
    ...

Project details


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