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Wednesday, February 21, 2018

How to use CNTK with QNAP Container Station

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

How to use MXNet with QNAP Container Station

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.

How to use TensorFlow on QNAP NAS (with Container Station)

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

  1. Open Container station and click on "Create Container".
  2. Search for keyword "TensorFlow". You can find the TensorFlow containers under the "AI" tab. Find "TensorFlow-GPU" and click "Install".
  3. Enter a name for the container.
  4. Click "Advanced Settings" and go to "Device".  Enable "Use GPU resource to run container".
  5. If needed, you can mount a specified NAS folder to this container in "Shared Folder". In the below screenshot, the folder "Public" is being mounted to the container.
  6. Click "Create". The container will be created and listed in the Overview page.
  7. You can now access the container using the terminal or SSH.
8.  open Jupyter Notebook

a. click URL 

b. Jupyter Notebook login need token  c. go to Terminal enter  /bin/sh  to connect into the Container 

jupyter notebook list

it will show the token

http://localhost:8888/?token=048c7741d736c2741eb375ee189a20fbe09d364870a6001d :: /notebooks                                                                                     
then you can copy 048c7741d736c2741eb375ee189a20fbe09d364870a6001d to the jupyter notbook init page

and now you have the access to the jupyter notebook:

Suggested Reading

More information and resources for the TensorFlow can be found at:
  1. Officialwebsite
  2. Tutorials
  3. GitHub
  4. TensorFlow Model Zoo

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