semvec/pypi-packages huggingface dataset:
matillion_columns 0.0.8
A tool to generate columns in our Matillion grid variables using CSVs on S3
License: MIT

# How to Run

**Before you do anything, make sure you've followed [this play](https://edusource.atlassian.net/wiki/spaces/PLAYB/pages/510460716/Create+AWS+Access+Credentials+and+Configure+AWS+CLI).**

### Installation

```bash
brew install python2
pip install --user matillion-columns
```

### Run

Once you've followed these steps, you should be able to run the script, using (it may take a few seconds to run, depending on the size of the file):

`matillion-columns [OPTIONS] csv_file.csv`

For more help info, run:

`matillion-columns -h`
htuneml 0.0.6
Monitor machine learning experiments
License: MIT

# HTUNEML - machine learning experiments monitoring and tuning

**Quickstart:** ``pip install htuneml``. See the "Installing" section for more details.


Project links:

-  `PyPI <https://pypi.python.org/pypi/htuneml/>`__
-  `Source code <https://github.com/johnsmithm/htuneml>`__
-  `Issue tracker <https://github.com/johnsmithm/htuneml/issues>`__


Examples
--------

See the ``examples/`` `directory <https://github.com/htuneml/htuneml/tree/master/examples>`__ in the repository root for usage examples:

-  `Tensorflow Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__
-  `Keras Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__
-  `Pytorch Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__


Requirements
------------

To use all of the functionality of the library, you should have:

* **Python** 2.6, 2.7, or 3.3+ (required)
* **PyAudio** 0.2.11+ (required only if you need to use microphone input, ``Microphone``)


Quick start
------------

Register on website http://registru.ml, copy the api_key:

.. code:: python

    import htuneml as ht
    
    job = Job('api_key')
    
    @job.monitor
    def train(par1=2,par2=2):
        for i in range(par1):
            #do training here
            job.log({'loss':i*4,'ep':i})

    job.setName('l2')
    #job.debug()# uncomment and no experiment will be created and no logs sent
    train(10, 2)

This will print out something like the following:

::

    make experiment
    got key experimnet 5c5c8eaacbcfb9146641367a

Also it is possible to sent the parameters from the web app. First on gpu/cpu set the lisener:

.. code:: python

    import htuneml as ht
    
    job = Job('api_key')
    
    def train(par1=2,par2=2):
        for i in range(par1):
            #do training here
            job.log({'loss':i*4,'ep':i})

    job.sentParams(train)#sent the parameters list to the app
    job.waitTask(train)#wait for parameters from app
Fattoush 0.4.0
A delicious testing framework
License: MIT

Fattoush
========

Fattoush is a package that combines lettuce, webdriver and sauce to make
a tasty UI testing salad.


Running
-------

Fattoush provides its own test runner which invokes lettuce such that it
runs in saucelabs, with support of parallel test runs.

    $ fattoush --parallel=webdriver


Usage
-----

Just write your lettuce tests as normal,
but in your terrain file add

    from fattoush import hooks

You will now find that in each step you are able to add

    from fattoush import Driver

This returns a subclass of WebDriver with a class method `instance()`
which takes the current step or feature and returns the active WebDriver
instance.

License
-------
(c) 2014 Mind Candy Ltd. All Rights Reserved.
Licensed under the MIT License; you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT.