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    "info": {
        "author": "Stefan Webb",
        "author_email": "stefan.webb@eng.ox.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "tensorflow-utils\n===================\nThis library contains classes and methods to make using TensorFlow easier.\n\nAt the moment it only contains context managers for creating graphs and session.\n\nInstallation\n------------\n**User Install**\n\n.. code-block:: bash\n\n  pip install --user tensorflow-utils\n\n**Developer Install**\n\n.. code-block:: bash\n\n  git clone https://github.com/stefanwebb/tensorflow-utils.git\n  cd tensorflow-utils\n  python setup.py develop\n\nCreating graphs\n------------\nAfter importing the library,\n\n.. code-block:: python\n\n  import tensorflow_utils as tf_utils\n\nwe enter a context manager for a new graph as,\n\n.. code-block:: python\n\n  with tf_utils.contexts.GraphContext() as graph_context:\n\nThis context manager takes a number of useful arguments for a default device, random seeds to NumPy and TensorFlow, and an existing graph object. For example,\n\n.. code-block:: python\n\n  with tf_utils.contexts.GraphContext(graph=existing_graph) as graph_context:\n  with tf_utils.contexts.GraphContext(device='gpu:0') as graph_context:\n  with tf_utils.contexts.GraphContext(device='gpu:0', np_seed=1, tf_seed=2) as graph_context:\n\nCreating sessions\n------------\nCreating sessions is likewise as simple with the following context manager,\n\n.. code-block:: python\n\n  with tf_utils.contexts.SessionContext() as session_context:\n\nIt has a number of useful keyword parameter options,\n\n.. code-block:: python\n\n  with tf_utils.contexts.SessionContext(\n    soft_placement=True, \n    gpu_allow_growth=True,\n    gpu_restrict_devices=0) as session_context:\n\nUsing the session manager has a number of benefits,\n\n- global and local variables are initialized after entering the manager\n- a ``tf.train.Saver`` is created if trainable variables have been defined in the current graph\n- queue runners are started and the queue populated if they have been defined in the graph, and these threads are terminated when the manager is exited\n- stopping queue runners with the ``stop()`` method, and testing whether they are running with ``running()``\n\n",
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        "keywords": "tensorflow utils",
        "license": "MIT",
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        "name": "tensorflow-utils",
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        "summary": "Classes and methods to make using TensorFlow easier",
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