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Friday, March 29, 2019

如何關閉 QNAP Multimedia Console 產生縮圖功能

QNAP 在QTS 4.4.x 之後推出的新功能:Multimedia Console 主打多媒體控制全面升級,影音娛樂一手掌握 !!

Thursday, March 28, 2019

QNAP QTS 實測 各家AI Framework 建議搭配

QNAP QTS 實測 各家AI docker image 使先要注意的是QTS 怎麼使用GPU
從官網的QNAP Nvidia GPU Driver  release note 發布公告 主要分兩個版本:

Monday, March 25, 2019

QNAP QIoT 有新版本了 QIoT Suite Lite 1.2.100

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

新增的import QTS 管理者群組功能

Monday, March 11, 2019

如何在QNAP TS-2888X 上使用 Fast.AI ?

Fast.AI 在 QNAP NAS上使用問題解決方式

Fast.AI 是一個很棒的線上課程,完整教學深度學習所需要的工具

儲存設備廠商 QNAP 從前年開始就有持續再將儲存設備持續轉型,率先支援了Nvidia GPU,以及Intel OpenVINO 因此有關Fast.AI 的開發環境,我們也在QNAP NAS上做個測試。在距離資料最近的地方做運算,省掉網路的工,以NAS 高IOPS的特性來強化一下。

NAS : TS-2888X
作業系統: QTS

主要測試Docker hub 上 關於Fast Ai 的image :

Tag : 1.0-release

Wednesday, March 6, 2019

How to setup TensorFlow on QNAP NAS TS-2888X

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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