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**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.
Each model is built into a separate Docker image with the appropriate Python, C++, and Java/Scala Runtime Libraries for training or prediction. Use the same Docker Image from Local Laptop to Production to avoid dependency surprises.
This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being rapidly expanded over the next few months. Foundational methods. Core mathematical methods - optimisation, interpolation, root finders, linear algebra, random and quasi-random number generation, etc.
The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.
Base image with Tensorflow and Keras with GPU support. The purpose of this project is to run a single Pod on Kubernetes on a GPU backed node. First, we need to label the gpu instance (if not already done). If you choose to use a different labeling, you need to adjust the nodeSelector part in the example-pod.yaml file.
The application we will develop is a simple image classification service, where we will submit an image and get back what class the image belongs to. If you already have a Docker image that you would like to deploy you can skip the first four notebooks.
ergo (from the Latin sentence "Cogito ergo sum") is a tool that makes deep learning with Keras easier. Start by printing the available actions by running ergo help, you can also print the software version (ergo, keras and tensorflow versions) and some hardware info with ergo info to verify your installation.
Head over to the releases section to download the latest version of the paperspace CLI for Linux, Mac, and Windows. After downloading, make sure the 'paperspace' binary is permitted to run on your system by marking its permissions appropriately. Also, add the directory containing the 'paperspace' binary to your path using a method appropriate for your platform.
A tiled DeepDream project for creating any size of image, on both CPU and GPU. Tensorflow should be compiled for either the CPU, or GPU depending on what your prefer. The CPU is slower, but this project should allow anyone to create an image of any size. The tiling code is based on the Tensorflow DeepDream example code. This project was inspired by jnordberg's DreamCanvas project. In order to control the desired output size, resize your image prior to running pb_dreamer.py. Any "blurriness" caused by resizing a smaller image to a larger size, should disappear after the DeepDream process.