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DeepStack GPU Version serves requests 5 - 20 times faster than the CPU version if you have an NVIDIA GPU.

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The deepstack-ui is designed to be run in a docker container. The UI picks up the information about your deepstack instance from environment variables which are passed into the container using the -e VARIABLE=value approach. All environment variables that. Current and Upcoming Tournament Series Please check back for updates. Previous Tournament Series DeepStack Extravaganza I January 27-March 1, 2020 Schedule Structures & Results DeepStack Extravaganza New Year's December 12, 2019-January 12, 2020 Schedule Structures & Results DeepStack Extravaganza IV October 28-December 1, 2019 Schedule Structures & Results $225,000 Lucky Shot Poker. UI for working with Deepstack. Allows uploading an image and performing object detection or face recognition with Deepstack. Also faces can be registered with Deepstack. The effect of various parameters can be explored, including filtering objects by confidence, type and location in the image.

NOTE: THE GPU VERSION IS ONLY SUPPORTED ON LINUX

DeepStack's developer centre. DeepStack across all supported platforms provies in-built state-of-the-art AI APIs and support for Custom APIs for custom objects detection and recognition. This documentation has been moved to Last updated 2 months ago 2 months ago.

Before you install the GPU Version, you need to follow the steps below.

Step 1: Install Docker¶

If you already have docker installed, you can skip this step.

Step 2: Setup NVIDIA Drivers¶

Install the NVIDIA Driver

Step 3: Install NVIDIA Docker¶

The native docker engine does not support GPU access from containers, however nvidia-docker2 modifies your docker installto support GPU access.

Run the commands below to modify the docker engine

If you run into issues, you can refer to this GUIDE

Step 4: Install DeepStack GPU Version¶

Step 5: RUN DeepStack with GPU Access¶

Once the above steps are complete, when you run deepstack, add the args –rm –runtime=nvidia

Step 6: Activate DeepStack¶

The first time you run deepstack, you need to activate it following the process below.

Once you initiate the run command above, visit localhost:80/admin in your browser.The interface below will appear.

You can obtain a free activation key from register.deepstack.cc https://register.deepstack.cc

Enter your key and click Activate Now

The interface below will appear.

This step is only required the first time you run deepstack.

Released:

DeepStack: Ensembles for Deep Learning

Project description

DeepStack: Ensembles for Deep Learning

DeepStack is a Python module for building Deep Learning Ensembles originally built on top of Keras and distributed under the MIT license.

Installation

Stacking

Stacking is based on training a Meta-Learner on top of pre-trained Base-Learners.DeepStack offers an interface to fit the Meta-Learner on the predictions of the Base-Learners.In the following an Example based on top of pre-trained Keras Models (there is also an interface for generic models):

Usage

Check an example on the CIFAR-10 dataset: Cifar10.py.

Randomized Weighted Ensemble

Ensemble Technique that weights the prediction of each ensemble member, combining the weights to calculate a combined prediction. Weight optimization search is performed with randomized search based on the dirichlet distribution on a validation dataset.

It follows the same interface of the StackEnsemble. An example can be found in Cifar10.py.

Citing DeepStack

If you use DeepStack in a scientific publication, we would appreciate citations:

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