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CNHow to use CNTK with QNAP Container Station QNAP is heading to AI era, and start with the QuAI - QNAP AI starter kit for developer. Let's see how it work with CNTK. What is CNTK The Microsoft Cognitive Toolkit - CNTK - is a unified deep-learning toolkit by Microsoft. You may directly use Container Station' s web interface to create this framework or command-line Installation Instructions Please follow following steps to install CNTK framework container on QNAP NAS. Open Container station and click on "Create Container" . Search for keyword "CNTK" and locate CNTK container under 'AI' tab. Click "Install" . Specify container name and keep rest of the parameters as it is. In  Advanced Settings >  Device, choose "Use GPU resource to run container" If you would like to mount any specific NAS folder to this container, you may follow this step. This is optional. In this example, we are trying to mount "Public" folder t...

How to use MXNet with QNAP Container Station QNAP is heading to AI era, and start with the  QuAI  - QNAP AI starter kit for developer. Let's see how it work with MXNet. What is MXNet Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelized both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Installation Instructions Please follow following steps to install MXNet framework container on QNAP NAS. Open Container station and click on "Create Container" . Search for keyword "CNTK" and locate CNTK container under 'AI' tab. Click "Install...

How to use TensorFlow  on QNAP NAS (with Container Station) What is TensorFlow 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) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. 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, but the system is general enough to be applicable in a wide variety of other domains as well. Installation Instructions Open Container station and click on  "Create Container" . Search for keyword  "TensorFlow" . You can find the TensorFlow containers under the  ...