TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
This container has the TensorFlow Python package installed and ready to use.
/opt/tensorflow contains the complete source of this version of TensorFlow.
TensorFlow includes TensorBoard, a data visualization toolkit.
Additionally, this container image also includes several built-in TensorFlow examples, which can be run using commands like the following:
python -m tensorflow.models.image.mnist.convolutional
python -m tensorflow.models.image.cifar10.cifar10_multi_gpu_train
You can choose to use TensorFlow as provided by NVIDIA, or you can choose to customize it.
TensorFlow is run simply by importing it as a Python module:
$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> sess.run(hello)
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a+b)
42
You can customize TensorFlow one of two ways:
(1) Modify the version of the source code in this container and run your
customized version, or (2) use docker build to add your customizations on top
of this container if you want to add additional packages.
NVIDIA recommends option 2 for ease of migration to later versions of the TensorFlow container image.
For more information, see https://docs.docker.com/engine/reference/builder/ for
a syntax reference. Several example Dockerfiles are provided in the container
image in /workspace/docker-examples.
For more information about TensorFlow, including tutorials, documentation, and examples, see the TensorFlow tutorials and [TensorFlow API] ( https://www.tensorflow.org/api_docs/python).
For information about optimizing TensorFlow models for TensorCore instructions available on NVIDIA Volta GPUs, see Training with Mixed Precision