Metadata-Version: 2.1
Name: tensorflow-determinism
Version: 0.1.0
Summary: Debugging and patching non-determinism in TensorFlow
Home-page: https://github.com/NVIDIA/tensorflow-determinism
Author: NVIDIA
Author-email: duncan@nvidia.com
License: Apache 2.0
Description: # TensorFlow Determinism
        
        The TensorFlow determinism debug tool described in the GTC 2019 talk
        [_Determinism in Deep Learning_][1] will be released here. Note that the
        features of this tool have not yet been released.
        
        Updates on the status of determinism in deep learning will also be conveyed here
        along with dynamic patches for TensorFlow.
        
        ## Installation
        
        Use `pip` to install:
        
        ```
        pip install tensorflow-determinism
        ```
        
        This will install a package that can be imported as `tfdeterminism`. The
        installation of `tensorflow-determinism` will not automatically install
        TensorFlow. The intention of this is to allow you to install your chosen
        version of TensorFlow. You will need to install your chosen version of
        TensorFlow before you can import and use `tfdeterminism`.
        
        ## Deterministic TensorFlow Solutions
        
        There are currently two main ways to access GPU-deterministc functionality in
        TensorFlow for most deep learning applications. The first way is to use an
        NVIDIA NGC TensorFlow container. The second way is to use version 1.14.0 of
        pip-installed TensorFlow with GPU support plus the application of a patch
        supplied in this repo.
        
        ### NVIDIA NGC TensorFlow Containers
        
        NGC TensorFlow container versions 19.06 (based on TensorFlow 1.13.1) and 19.07
        (based on TensorFlow 1.14.0) both implement GPU-deterministic TensorFlow
        functionality. In Python code running inside the container, this can be enabled
        as follows:
        
        
        ```
        import tensorflow as tf
        import os
        os.environ['TF_DETERMINISTIC_OPS'] = '1'
        # Now build your graph and train it
        ```
        
        For information about pulling and running the NVIDIA NGC containers, see [these
        instructions][2].
        
        ### PyPI (pip-installed) TensorFlow
        
        Version 1.14.0 of stock TensorFlow implements a reduced form of GPU
        determinism, which must be supplemented with a patch provided in this repo.
        The following Python code is running on a machine in which `pip` package
        `tensorflow-gpu=1.14.0` has been installed correctly and on which
        `tensorflow-determinism` has also been installed (as shown in the
        [installation](#installation) section above).
        
        ```
        import tensorflow as tf
        from tfdeterminism import patch
        patch()
        # build your graph and train it
        ```
        
        Tensorflow with GPU support can be installed as follows:
        
        ```
        pip install tensorflow-gpu=1.14.0
        ```
        
        The TensorFlow project includes [detailed instructions][3] for installing
        TensorFlow with GPU support.
        
        [1]: http://bit.ly/determinism-in-deep-learning
        [2]: https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow
        [3]: https://www.tensorflow.org/install/gpu
Keywords: tensorflow gpu deep-learning determinism
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
