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Dockerfile for Maya
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Branch: 2020sp1
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Dockerfile Update Maya 2020SP1 May 25, 2020
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README.md Update README.md May 25, 2020

README.md

Supported tags

  • 2013sp1, 2013sp2, 2014sp1, 2014sp2, 2014sp3, 2014sp4, 2015sp1, 2015sp2, 2015sp3, 2015sp4, 2015sp5, 2015sp6, 2016sp1, 2017, 2018, 2019, 2020 and 2020sp1

For more information about this image and its history, please see its the GitHub repository.

Usage

To use this image and any of it's supported tags, use docker run.

$ docker run -ti --rm mottosso/maya

Without a "tag", this would download the latest available image of Maya. You can explicitly specify a version with a tag.

$ docker run -ti --rm mottosso/maya:2016sp1

Images occupy around 5 gb of virtual disk space once installed, and about 1.5 gb of bandwidth to download.

Example

This example will run the latest available version of Maya, create a new scene and save it in your current working directory.

$ docker run -ti -v $(pwd):/root/workdir --rm mottosso/maya
$ mayapy
>>> from maya import standalone, cmds
>>> standalone.initialize()
>>> cmds.file(new=True)
>>> cmds.polySphere(radius=2)
>>> cmds.file(rename="my_scene.ma")
>>> cmds.file(save=True, type="mayaAscii")
>>> exit()
$ cp /root/maya/projects/default/scenes/my_scene.ma workdir/my_scene.ma
$ exit
$ cat my_scene.ma

What's in this image?

This image builds on mayabase-centos which has the following software installed.

Each tag represents a particular version of Maya, such as 2016 SP1. In this image, python is an alias to maya/bin/mayapy which has the following Python packages installed via pip.

User Feedback

Documentation

Documentation for this image is stored in the GitHub wiki for this project.

Issues

If you have any problems with or questions about this image, please contact me through a GitHub issue.

Contributing

You are invited to contribute new features, fixes, or updates, large or small; I'm always thrilled to receive pull requests, and do my best to process them as fast as I can.

Before you start to code, we recommend discussing your plans through a GitHub issue, especially for more ambitious contributions. This gives other contributors a chance to point you in the right direction, give you feedback on your design, and help you find out if someone else is working on the same thing.

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