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Build status Supported Python version

Hideout

Data processing projects such as machine learning or data mining take sorts of large amount of input data. After input data are loaded, such programs create or convert intermediate objects using input data. Getting or converting data also takes long time. This aspect of data processing projects prevents software engineers to refactor codes or keep them clean.

Install

We can install hideout with pip. Run the following command.

$ pip install hideout

Basic Usage

Hideout support two types of caching. One is decoration style, the other is registering function to a hideout function adding the target function as the argument.

Decoration

We just add resumable to the target function.

@resumable()
def generate():
    sleep(10)
    return {"foobar": "bar"}

If you want to cache the result of a instance method of a object, we just add the resumable decoration to the instance method.

class Generator2:
    @resumable()
    def generate(self, baz):
        return {"foobar": baz}

Function argument

Hideout save and load object with hideout.resume. If the cache file for the object exist, hideout loads it otherwise call specified function to generate expected object.

large_object = hideout.resume_or_generate(
    label="large_object",
    func=generate_large_object,
    func_args={"source": "s3-northeast-8.amazonaws.com/large-dic.txt"}
)

hideout.resume have func_args option which contains the parameters of specified function to generate the expected object. We can specify the prefix of cache file with label option. When we do not specify the label option, resume_or_generate method automatically name the cache file from function name and the arguments.

Usage

Enable / Disable Cache

In default, Hideout is not activated and therefore does not save and load cache files. To enable cache we set the provided environment variable HIDOUT_ENABLE_CACHE to True.

$ HIDEOUT_ENBALE_CACHE=True your_data_engineering_program.py

Disable Cache for Specified Stages

Hideout provide stage for skipping caches for specified points. Users can add the stage names to the object generation by hideout.resume_or_generate with stage parameter.

large_object = hideout.resume_or_generate(
    label="large_object",
    func=generate_large_object,
    stage="preliminaries",
    func_args={"source": "s3-northeast-8.amazonaws.com/large-dic.txt"}
)

If you use decoration style, add stage parameter to the decorator.

@resumable(stage="preliminaries")
def generate():
    sleep(10)
    return {"foobar": "bar"}

Specifing stage names with HIDEOUT_SKIP_STAGES, hideout skip the caching. For example, the following command skip caching named preliminaries and integrate.

$ HIDEOUT_SKIP_STAGES=preliminaries,integrate your_data_engineering_program.py

Specify directory to Store Cache Files

In default, Hideout saves the cache files in caches under the top project directory. If we specify the directory, we specify it with environment variable HIDEOUT_CACHE_DIR.

Inject logger

When you want to apply the logger which you use throughout an application, you can inject the logger with hideout.set_logger() function.

For Developers

We can install the hideout package and upload it to pypi repository.

Install to local environment

$ python setup.py install

Upload to pypi

$ python setup.py sdist upload

License

MIT

Contribution

See CONTRIBUTING.md.

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Caching tool for data processing projects such as machine learning and data engineering projects

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