If you want to deploy your own local Computer Vision Machine Learning server to use with the Computer Vision activities, follow the steps in this page:
Note:
The information in this page is valid only after performing the steps explained in the Setup for Machine Learning Solutions page, which describes how to install all the necessary prerequisites on the target GPU Machine.
Requirements
The supported operating systems for the CV Server are:
- Ubuntu v16.04
- Red Hat Enterprise Linux v7.x
- Windows 10
Hardware Requirements
You need a machine with the following hardware specifications:
GPU: nVidia K80
GPU: nVidia P40
Resolution
Time
Resolution
Time
1280x800
1.349
1280x800
0.437
1440x900
1.674
1440x900
0.541
1680x1050
2.124
1680x1050
0.675
1920x1080
2.336
1920x1080
0.756
1920x1200
2.593
1920x1200
0.825
2560x1600
4.180
2560x1600
1.341
For more detailed hardware specifications, see the respective official documentation for Ubuntu 16.04 Desktop Edition, Red Hat Enterprise Linux, and Windows 10.
Software Requirements
For the details regarding software requirements and installation prerequisites, go here
Deploying the Computer Vision Machine Learning Model
Note:
For Windows 10, please skip to this section.
Moving the ML Model File to the GPU Machine
On the Physical GPU Machine
- Download the
Controls_detection.zipfile to your machine from the link provided to you by your sales rep.
Connecting Remotely to the GPU Machine
- Download the
Controls_detection.zipfile to your machine from the link provided to you by your sales rep. - Open a new PowerShell terminal on the local machine and use
cd FULL_PATH_TO_Controls_detection.zipto navigate to the folder where theControls_detection.zipfile is located. For example, if the path of the file isC:\Users\[WINDOWS-USER]\Downloads\Controls_detection.zip, the command should look like:
cd C:\Users\[WINDOWS-USER]\Downloads\- Use the
scp .\Controls_detection.zip [email protected]:/GPUMachinePathcommand to copy the files over to the GPU machine. To find out what the path on the GPU machine is, use thepwdcommand in the terminal connected viasshto the GPU machine. For example, if the path is/home/ML-Test, the command should look like:
scp .\Controls_detection.zip ServerUser@server-ip://home/ML-TestNote:
sudo apt-get install -y zip
unzip controls_detection.zipsudo yum install unzip
unzip controls_detection.zipStarting the Server
- On the GPU Machine, use the
cd Controls_detectioncommand to navigate to the Controls_detection folder. - Use the
bash start_server.shcommand to begin deploying the server. - Wait until the installation is 100% complete and press
Ctrl + Cto stop any programs that might be running in the background. - Run
bash test.shto test the speed of the neural network.
For Windows 10
Note:
The Windows 10 CV Server is available in Private Preview only.
- Download and run the
UiPathCVService.msiexecutable on the GPU machine. The Setup Wizard is opened.
- Click Next. The Destination Folder selection window is opened.
- Select the destination folder path. By default, it is set to
C:\Program Files\UiPathComputerVision\. To select a different folder, click the Change... button. Once done, click Next.
- To continue the installation process, click Install. The Installation Progress window is opened.
Important!
Administrator permissions are required to install the UiPath Computer Vision server.
- Once the installation is complete, click Finish to close the installer.
Connecting to the Computer Vision Server
Once the server is deployed, to connect to it when using the Computer Vision activities, you must change the value of the URL property of the CV Screen Scope activity to http://[MACHINE_URL]:[PORT]/v1/models/controls_detection:predict where [MACHINE_URL] is the address of the machine where the server is deployed, and [PORT] is the unique port which the Docker container requires. For example, if the URL of your machine is k80-ubuntu.azure.com, and the port you have chosen to work with is 8501, then the value of the property should look like http://k80-ubuntu.azure.com:8501/v1/models/controls_detection:predict.
The default URL can also be changed from the Project Settings page, under the Computer Vision activities tab.
Important!
Make sure you have the port that is used by the Computer Vision activities (default 8501) open on the machine the model is deployed on.
To open the port on the server machine, you have to run the following console commands, replacing [PORT] with the actual port you want to use and [your-default-zone] with your system's zone (by default public):
For Ubuntu
apt install firewalld
systemctl enable firewalld
firewall-cmd --zone=[your-default-zone] --permanent --add-port=[PORT]/tcp
firewall-cmd --reloadyum install firewalld
systemctl enable firewalld
firewall-cmd --zone=[your-default-zone] --permanent --add-port=[PORT]/tcp
firewall-cmd --reloadFor more info on firewallId, you can view their official documentation here.
Changing the Default Port
The default port through which the model is built to connect through is 8501. It is specified in the ./controls_detection/start_server.sh file. The line -p 8501:8501 designates the chosen port.
To change the default port, you must edit the start_server.sh file and modify the -p 8501:8501 line to -p [NEW_PORT]:8501, where [NEW_PORT] is the port you want to use. The default start_server.sh file looks like this:
docker run \
--name tensorflow-server-controls-detection \
-p 8501:8501 \
--mount type=bind,source=$(pwd)/models,target=/models \
-e MODEL_NAME=controls_detection \
-t tensorflow/serving:1.12.0-gpuSo, for example, if you wanted to change the port to 80, the content of the start_server.sh file should look like:
docker run \
--name tensorflow-server-controls-detection \
-p 80:8501 \
--mount type=bind,source=$(pwd)/models,target=/models \
-e MODEL_NAME=controls_detection \
-t tensorflow/serving:1.12.0-gpu
Updated about a month ago