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"Dataset management command line and API"

Project description

CircleCI PyPI version pre-commit DOI

Introduction

This projects aims at grouping utilities to deal with the numerous and heterogenous datasets present on the Web. It aims at being

  1. a reference for available resources, listing datasets
  2. a tool to automatically download and process resources (when freely available)
  3. integration with the experimaestro experiment manager.
  4. (planned) a tool that allows to copy data from one computer to another

Each datasets is uniquely identified by a qualified name such as com.lecun.mnist, which is usually the inversed path to the domain name of the website associated with the dataset.

The main repository only deals with very generic processing (downloading, basic pre-processing and data types). Plugins can then be registered that provide access to domain specific datasets.

The documentation can be found at https://experimaestro.github.io/datamaestro/.

List of repositories

Command line interface (CLI)

The command line interface allows to interact with the datasets. The commands are listed below, help can be found by typing datamaestro COMMAND --help:

  • search search dataset by name, tags and/or tasks
  • download download files (if accessible on Internet) or ask for download path otherwise
  • prepare download dataset files and outputs a JSON containing path and other dataset information
  • repositories list the available repositories
  • orphans list data directories that do no correspond to any registered dataset (and allows to clean them up)
  • create-dataset creates a dataset definition

Example (CLI)

Retrieve and download

The commmand line interface allows to download automatically the different resources. Datamaestro extensions can provide additional processing tools.

$ datamaestro search tag:image
[image] com.lecun.mnist

$ datamaestro prepare com.lecun.mnist
INFO:root:Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz into /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/t10k-labels-idx1-ubyte
INFO:root:Transforming file
INFO:root:Created file /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/t10k-labels-idx1-ubyte
INFO:root:Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz into /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/t10k-images-idx3-ubyte
INFO:root:Transforming file
INFO:root:Created file /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/t10k-images-idx3-ubyte
INFO:root:Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz into /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/train-labels-idx1-ubyte
INFO:root:Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz: 32.8kB [00:00, 92.1kB/s]                                                            INFO:root:Transforming file
INFO:root:Created file /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/train-labels-idx1-ubyte
INFO:root:Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz into /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/train-images-idx3-ubyte
INFO:root:Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz: 9.92MB [00:00, 10.6MB/s]
INFO:root:Transforming file
INFO:root:Created file /home/bpiwowar/datamaestro/data/image/com/lecun/mnist/train-images-idx3-ubyte
...JSON...

The previous command also returns a JSON on standard output

{
  "train": {
    "images": {
      "path": ".../data/image/com/lecun/mnist/train_images.idx"
    },
    "labels": {
      "path": ".../data/image/com/lecun/mnist/train_labels.idx"
    }
  },
  "test": {
    "images": {
      "path": ".../data/image/com/lecun/mnist/test_images.idx"
    },
    "labels": {
      "path": ".../data/image/com/lecun/mnist/test_labels.idx"
    }
  },
  "id": "com.lecun.mnist"
}

For those using Python, this is even better since the IDX format is supported

In [1]: from datamaestro import prepare_dataset
In [2]: ds = prepare_dataset("com.lecun.mnist")
In [3]: ds.train.images.data().dtype, ds.train.images.data().shape
Out[3]: (dtype('uint8'), (60000, 28, 28))

Python definition of datasets

Each dataset (or a set of related datasets) is described in Python using a mix of declarative and imperative statements. This allows to quickly define how to download dataset using the datamaestro declarative API; the imperative part is used when creating the JSON output, and is integrated with experimaestro.

Its syntax is described in the documentation.

For MNIST, this corresponds to.

from datamaestro_image.data import ImageClassification, LabelledImages, Base, IDXImage
from datamaestro.download.single import filedownloader
from datamaestro.definitions import data, argument, datatasks, datatags, dataset
from datamaestro.data.tensor import IDX


@filedownloader("train_images.idx", "http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz")
@filedownloader("train_labels.idx", "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz")
@filedownloader("test_images.idx", "http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz")
@filedownloader("test_labels.idx", "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz")
@dataset(
  ImageClassification,
  url="http://yann.lecun.com/exdb/mnist/",
)
def MNIST(train_images, train_labels, test_images, test_labels):
  """The MNIST database

  The MNIST database of handwritten digits, available from this page, has a
  training set of 60,000 examples, and a test set of 10,000 examples. It is a
  subset of a larger set available from NIST. The digits have been
  size-normalized and centered in a fixed-size image.
  """
  return {
    "train": LabelledImages(
      images=IDXImage(path=train_images),
      labels=IDX(path=train_labels)
    ),
    "test": LabelledImages(
      images=IDXImage(path=test_images),
      labels=IDX(path=test_labels)
    ),
  }

Changelog

0.6.17

  • Allow remote access through rpyc

0.6.9

version command

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