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How to Contribute

Guidelines for open source enthusiasts to contribute to our open-source data format.

How to Contribute to Activeloop Open-Source

Deep Lake relies on feedback and contributions from our wonderful community. Let's make it amazing with your help! Any and all contributions are appreciated, including code profiling, refactoring, and tests.

Providing Feedback

We love feedback! Please join our Slack Community or raise an issue in Github.

Getting Started With Development

Clone the repository:

git clone https://github.com/activeloopai/deeplake 
cd deeplake

If you are using Linux, install environment dependencies:

apt-get -y update
apt-get -y install git wget build-essential python-setuptools python3-dev libjpeg-dev libpng-dev zlib1g-dev
apt install build-essential

If you are planning to work on videos, install codecs:

apt-get install -y ffmpeg libavcodec-dev libavformat-dev libswscale-dev

Install the package locally with plugins and development dependencies:

pip install -r deeplake/requirements/plugins.txt
pip install -r deeplake/requirements/tests.txt
pip install -e .

Run local tests to ensure everything is correct:

pytest -x --local .

Using Docker (optional)

You can use docker-compose for running tests

docker-compose -f ./bin/docker-compose.yaml up --build local

and even work inside the docker by building the image and bashing into.

docker build -t activeloop-deeplake:latest -f ./bin/Dockerfile.dev .
docker run -it -v $(pwd):/app activeloop-deeplake:latest bash
$ python3 -c "import deeplake"

Now changes done on your local files will be directly reflected into the package running inside the docker.

Contributing Guidelines

Linting

Deep Lake uses the black python linter. You can auto-format your code by running pip install black, and the run black . inside the directory you want to format.

Docstrings

Deep Lake uses Google Docstrings. Please refer to this example to learn more.

Typing

Deep Lake uses static typing for function arguments/variables for better code readability. Deep Lake has a GitHub action that runs mypy ., which runs similar to pytest . to check for valid static typing. You can refer to mypy documentation for more information.

Testing

Deep Lake uses pytest for tests. In order to make it easier to contribute, Deep Lake also has a set of custom options defined here.

Prerequisites

Options

To see a list of Deep Lake's custom pytest options, run this command: pytest -h | sed -En '/custom options:/,/\[pytest\] ini\-options/p'.

Fixtures

You can find more information on pytest fixtures here.

  • memory_storage: If --memory-skip is provided, tests with this fixture will be skipped. Otherwise, the test will run with only a MemoryProvider.

  • local_storage: If --local is not provided, tests with this fixture will be skipped. Otherwise, the test will run with only a LocalProvider.

  • s3_storage: If --s3 is not provided, tests with this fixture will be skipped. Otherwise, the test will run with only an S3Provider.

  • storage: All tests that use the storage fixture will be parametrized with the enabled StorageProviders (enabled via options defined below). If --cache-chains is provided, storage may also be a cache chain. Cache chains have the same interface as StorageProvider, but instead of just a single provider, it is multiple chained in a sequence, where the last provider in the chain is considered the actual storage.

  • ds: The same as the storage fixture, but the storages that are parametrized are wrapped with a Dataset.

Each StorageProvider/Dataset that is created for a test via a fixture will automatically have a root created, and it will be destroyed after the test. If you want to keep this data after the test run, you can use the --keep-storage option.

Fixture Examples

Single storage provider fixture:

def test_memory(memory_storage):
    # Test will skip if `--memory-skip` is provided
    memory_storage["key"] = b"1234"  # This data will only be stored in memory

def test_local(local_storage):
    # Test will skip if `--local` is not provided
    memory_storage["key"] = b"1234"  # This data will only be stored locally

def test_local(s3_storage):
    # Test will skip if `--s3` is not provided
    # Test will fail if credentials are not provided
    memory_storage["key"] = b"1234"  # This data will only be stored in s3

Multiple storage providers/cache chains:

from deeplake.core.tests.common import parametrize_all_storages, parametrize_all_caches, parametrize_all_storages_and_caches

@parametrize_all_storages
def test_storage(storage):
    # Storage will be parametrized with all enabled `StorageProvider`s
    pass

@parametrize_all_caches
def test_caches(storage):
    # Storage will be parametrized with all common caches containing enabled `StorageProvider`s
    pass

@parametrize_all_storages_and_caches
def test_storages_and_caches(storage):
    # Storage will be parametrized with all enabled `StorageProvider`s and common caches containing enabled `StorageProvider`s
    pass

Dataset storage providers/cache chains:

from deeplake.core.tests.common import parametrize_all_dataset_storages, parametrize_all_dataset_storages_and_caches

@parametrize_all_dataset_storages
def test_dataset(ds):
    # `ds` will be parametrized with 1 `Dataset` object per enabled `StorageProvider`
    pass

@parametrize_all_dataset_storages_and_caches
def test_dataset(ds):
    # `ds` will be parametrized with 1 `Dataset` object per enabled `StorageProvider` and all cache chains containing enabled `StorageProvider`s
    pass

Benchmarks

Deep Lake uses pytest-benchmark for benchmarking, which is a plugin for pytest.

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Deep Lake would not be possible without the work of our community.

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