Docker image

General Information

Pixyz SDK is cloud-ready. Unity Cloud currently uses Pixyz-based Docker images for its automatic CAD and 3D transformation pipelines, including thumbnail generation, GLB web-previews, metadata extraction, file conversions, 3D data streaming, and more.

As a standalone toolkit, Pixyz SDK is available for on-premise (local) or VPC deployments for Unity external customers who cannot leverage Unity Cloud online services.

Get Started

The Pixyz SDK Docker image is available on Docker Hub.

A floating license server is required to run the Pixyz SDK Docker container.

Set Up the License Server

The license server must be running on the network and accessible from the container. Ports 27000 to 27010 must be opened.

License server installation guides:

• Windows
• Linux

Get the SDK Docker Container

docker pull pixyzinc/sdk:2024.2.1.3

Install License

A license file must be mounted (with write permission) to /usr/share/PixyzSDK/licenses/pixyz.lic for the container to run.

Create the pixyz.lic file with the following content:

<?xml version="1.0" encoding="utf-8" ?>
<license product="PixyzSDK">
  <version/>
  <validity start="" end=""/>
  <customer>
    <company/>
    <name/>
    <email/>
    <site/>
    <phone/>
  </customer>
  <floatting>True</floatting>
  <server>
    <port>27000</port>
    <host>##YOUR_SERVER_IP_OR_HOSTNAME###</host>
    <flexLM>True</flexLM>
  </server>
  <machine/>
</license>

Replace ##YOUR_SERVER_IP_OR_HOSTNAME### with the IP address or hostname of the license server.

Configure License Server

Use the following code at the top of your datap reparation script to configure the license server:

import pxz
from pxz import core

# init Pixyz
pxz.initialize()

# print Pixyz version
print(core.getVersion())

# set log level to INFO so you can see the logs in the console
core.configureInterfaceLogger(True, True, True)
core.addConsoleVerbose(core.Verbose.INFO)

# if no license is found, try to configure a license server
if not core.checkLicense():
    core.configureLicenseServer("company-server-hostname", 27000, True)

# add all tokens
for token in pxz.core.listTokens():
    try:
        core.needToken(token)
    except:
        pass

# use io.importScene to import a file and enjoy :)

Replace company-server-hostname with the IP address or hostname of the license server.

You can alternatively require tokens individually. For example:

pxz.core.needToken("PixyzSDK")
pxz.core.needToken("IO")
pxz.core.needToken("Script")
pxz.core.needToken("PxzImport")
pxz.core.needToken("PxzExport")
pxz.core.needToken("ImportBundle0")
pxz.core.needToken("ImportBundle1")
pxz.core.needToken("ImportBundle2")
pxz.core.needToken("ImportBundle3")
pxz.core.needToken("ExportBundle0")
pxz.core.needToken("ExportBundle1")
pxz.core.needToken("ExportBundle2")
pxz.core.needToken("Algo")
pxz.core.needToken("Process")
pxz.core.needToken("AllFormats")
pxz.core.needToken("ScenarioProcessor")

For more detailed information on licensing, please refer to the licensing documentation.

GPU Requirement

Some Pixyz algorithms require a GPU to run. More information.

The GPU must be allocated to the container if you intend to use these algorithms.

Run Your Data Preparation Scripts

The Pixyz SDK is located at /opt/pixyz in the container. Its entrypoint is designed to automatically initialize Pixyz SDK and run the provided Python script path.

Locally

export LIC_FILE=/path/to/your/pixyz.lic
export FILES_DIR=/path/to/your/cad_files
export SCRIPTS_DIR=/path/to/your/dataprep_scripts

docker run \
  -d --rm \
  --name=pixyz_sdk \
  --runtime=nvidia \
  --gpus all \
  -e NVIDIA_VISIBLE_DEVICES=all \
  -e NVIDIA_DRIVER_CAPABILITIES=all \
  -e DISPLAY=$DISPLAY \
  -v /usr/share/vulkan/icd.d/nvidia_icd.json:/usr/share/vulkan/icd.d/nvidia_icd.json \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -v $LIC_FILE:/usr/share/PixyzSDK/licenses/pixyz.lic \
  -v $FILES_DIR:/files \
  -v $SCRIPTS_DIR:/scripts \
  pixyzinc/sdk:2024.2.0.52 /scripts/myconvertionscript.py /files/myfiletoconvert.obj

To improve the performance of the container, you can use the --cpus ##NUMBER## and --memory ##VALUE## flags.

Running in the Cloud

The Docker container is compatible with all cloud providers.

When running with Kubernetes with GPU:

• The NVIDIA drivers must be provided on the node.
• The NVIDIA daemonset must be deployed (e.g., on Azure).

Unity Cloud Transformations and Asset Manager

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