semvec/pypi-packages huggingface dataset:
async_runner 0.1.7
Async Runner
License: MIT
Keywords: async_runner

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Async Runner
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.. image:: https://img.shields.io/pypi/v/async_runner.svg
        :target: https://pypi.python.org/pypi/async_runner

.. image:: https://img.shields.io/travis/vedarthk/async_runner.svg
        :target: https://travis-ci.org/vedarthk/async_runner

.. image:: http://readthedocs.org/projects/async-runner/badge/?version=latest
        :target: http://async-runner.readthedocs.org/en/latest/?badge=latest
        :alt: Documentation Status


Async runner is a thin wrapper over Celery API. This will enable control over failed messages by moving them to separate queue (generally queue_name_error) and also allows to retry the task with the help of retry policy.


More can be found at documentation: https://async_runner.readthedocs.org


.. code-block:: python

    from async_runner import async_runner
    async_runner.send_task(
        task_fn=func,  # task function can be python module path
        queue='queue_name',  # name of the queue
        args=(arg1, arg2, arg3, ),  # tuple/list of positional arguments to task function
        kwargs={'name': 'parameter'},  # dictionary with key word arguments to task function
        options={
            'max_retries': 3,  # maximum number of times the task is retried
            'retry_policy': {
                'retry_interval': 12  # interval between retires (in seconds)
            }
        }
    )


**TODO:** Decouple from Django


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History
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0.1.0 (2016-1-22)
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* First release on PyPI.

0.1.1 (2016-1-22)
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* Fix issues.
nnClassifier 0.0.1
A command line tool for classifying images using transfer learning
License: MIT
Keywords: image classification transfer learning

# nnClassify
Command line tool for classifying images

 Toxicology – the branch of science concerned with the nature,
 effects, and detection of poisons – has 
 traditionally relied on expensive mammalian studies.
 However, due to the large number of environmental
 toxins that need testing, less expensive, high 
 throughput alternatives are required. Planaria – a small 
 asexual flatworm – provide an inexpensive and scalable
 solution. The small size of planaria not only makes
 them inexpensive to maintain, but also easy to 
 image with a high-resolution camera. Researchers can
 run many test simultaneously, record the planarias
 reaction, and use computer vision techniques to 
 analyze the results in a cost-effect and timely manner.
 The results of some of these tests can be analyzed
 using traditional computer vision techniques;
 however, many tests involve classifying features of the
 planaria – such as their body shape or eyes – as
 normal or abnormal. Deep convolutional neural
 networks have achieved state of the art results on image
 classification tasks, but they require a large training
 set of images. Researchers do not have the time
 or resources to hand-label sufficient images for this
 technique to be effective. Using transfer-learning, we
 have developed a software platform for researchers to
 quickly classify images with near state-of-the-art
 performance and minimal training data.