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QNAP QIoT Suite release new version v1.2.100, now you can import QTS user !!

QIoT Suite Lite 1.2.100
( 2019/03/15 )
[New Features]
1. QTS users can now be imported into QIoT Suite Lite.
2. QTS users can now log in to QIoT Suite Lite using their QTS account.
3. Default Node-RED nodes have been updated to the following versions:
  a. node-red-dashboard:2.13.2
  b. node-red-node-email:1.2.0
  c. node-red-node-feedparser:0.1.14
  d. node-red-node-rbe:0.2.4
  e. node-red-node-twitter:1.1.4


How to setup Fast.AI on QNAP NAS TS-2888X

How to setup Fast.AI on QNAP NAS
Fast.AI website: 

online courses (all are free and have no ads):

           software: fastai v1 for PyTorch

          We are testing Fast.AI on QNAP NAS
          docker image from dockerhub :
          Tag : 1.0-release


          How to use PyTorch on QNAP NAS with Container Station?

          How to use PyTorch with Container Station?

          What is PyTorch?

          It’s a Python-based scientific computing package targeted at two sets of audiences:
          • A replacement for NumPy to use the power of GPUs
          • A deep learning research platform that provides maximum flexibility and speed

          How to setup GPU on QNAP NAS (QTS 4.3.5+)

          How to setup GPU on QNAP NAS (QTS 4.3.5+) !

          Actually QNAP start to support GPU in QTS 4.3.5, so that if you are using QTS 4.3.6 or QTS 4.4.0 you can use GPU as well.

          in this tutorial we will  introduce QTS & setup GPU with QNAP NAS


          How to setup TensorFlow on QNAP NAS

          QNAP NAS for AI TS-2888X
          QNAP TS-2888X

          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
          follow up QNAP latest AI news in QNAP QuAI Community
          setup Pytorch with QNAP NAS
          setup NVIDIA DIGITS with QNAP NAS 

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