Client library for dataTap
dataTap Python Library
The dataTap Python library is the primary interface for using dataTap's rich data management tools. Create datasets, stream annotations, and analyze model performance all with one library.
Full documentation is available at docs.datatap.dev.
Installation & Setup
The latest version of dataTap can be installed with
pip install datatap
In order to access the cloud-based functionality, you will also need to register an account at app.datatap.dev. Once you've registered, go to
Settings > Api Keys to find your personal API key. In order to avoid storing private keys in your code, you should assign this key to the
DATATAP_API_KEY environment variable.
datatap gives access to the
Api class for interacting with the dataTap cloud platform. Here is how we can use it:
import itertools from datatap import Api api = Api() # Datatap allows users to connect several databases, and also provides a # public database that contains millions of freely available annotations. # `get_default_database` will connect us to that one. database = api.get_default_database() print("Database", database) # We can then fetch a list of repositories that have already been created in # this database repository_list = database.get_repository_list() print("Available repositories:", [repo.name for repo in repository_list]) # A repository is a grouping of datasets that are associated in some manner. # For instance, a repository called `"person-keypoints"` would contain # one or more datasets of people with their keypoints. # # The datasets within a given repository are identfieid by "tags", or short # strings that identfy either how they were created, or how they should be # used. Example tags might be `all-datasets`, `open-images`, or `december-2020`. # Additionally, all repositories have one dataset identified with `latest`, # which should refer to the most recent "canonical" dataset. # # For this example, we will use the `widerperson` repository, and the dataset # identified as `latest`. repository = database.get_repository("public", "widerperson") dataset = repository.get_dataset("latest") print("Loaded dataset:", dataset) # A dataset's template describes how it is structured. We can print # it to see what type of data this dataset contains print("Dataset template:", dataset.template) # Datasets are furthermore partitioned into one or more `splits`. # In most cases, the dataset will contain two splits called `"training"` # and `"validation"`. We can stream the contents of a particular split # by using the `stream_split` function training_stream = dataset_version.stream_split("training") for annotation in itertools.islice(training_stream, 5): # Each element of the stream will be an `ImageAnnotation` print("Received annotation:", annotation)
For a more in-depth example, including model training and evaluation, please take a look at datatap/examples/torch.ipynb.
All documentation for this Python library can be found at docs.datatap.dev.
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