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Distributed TensorFlow This document shows how to create a cluster of TensorFlow servers, and how to distribute a computation graph across that cluster. We assume that you are familiar with the basic concepts of writing TensorFlow programs. Hello distributed TensorFlow! To see a simple TensorFlow cluster in action, execute the following: # Start a TensorFlow server as a single-process "cluster". $
TensorFlow-Slim TF-Slim is a lightweight library for defining, training and evaluating models in TensorFlow. It enables defining complex networks quickly and concisely while keeping a model's architecture transparent and its hyperparameters explicit. [TOC] Teaser As a demonstration of the simplicity of using TF-Slim, compare the simplicity of the code necessary for defining the entire VGG network
root@5b1e79697b49:~# python Python 2.7.6 (default, Jun 22 2015, 17:58:13) [GCC 4.8.2] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcublas.so.7.0 locally I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcudnn.so.6.5 lo
The first part of this tutorial describes how to install the necessary tools and use the already trained models provided in this release. In the second part of the tutorial we provide more background about the models, as well as instructions for training models on other datasets. Contents Installation Getting Started Parsing from Standard Input Annotating a Corpus Configuring the Python Scripts Ne
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. TFLearn features include: Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast p
For bugs/issues, please fill in the following. The more information you provide, the more likely we can help you. Environment info Operating System: Mac OS (El Capitan / Python 2.7) If installed from binary pip package, provide: Which pip package you installed. Virtualenv The output from python -c "import tensorflow; print(tensorflow.version)". 0.7.1 If installed from sources, provide the commit h
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