Example: 3ds Max 2018 Rendering

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Example: 3ds Max 2018 Rendering

Last updated: 2024-01-13 11:19:29

Getting Started
This document describes how to use BatchCompute console to submit a job, complete 3ds Max 2018 image rendering, and export the rendered image. The operation steps are presented below:
Step 1. Creating a custom image
1. For more information about how to create a custom image, see Windows Custom Images.
2. For more information about how to install 3ds Max 2018, visit the Autodesk official website.
Note：
Disable the Windows Firewall temporarily to avoid blocking software downloads.
Select a proper graphics card model to prevent graphics card initialization failures. Under normal circumstances, "Nitrous Software" is recommended. For more information, see Display Driver Selection Dialog.
Step 2. Preparing rendering files
There are two main methods for storing rendering materials, namely Cloud Object Storage (COS) and Cloud File Storage (CFS). By configuring the mount parameters, BatchCompute locally mounts a COS bucket or a CFS instance before the rendering job runs. This way, the renderer can access the COS bucket or CFS instance in the way that it accesses local files.
If the rendering materials are small-sized, we recommend that you compress them into a .gzip package and upload the package to COS. For more information, see Uploading an Object.
While the rendering materials are large-sized, upload them to the CFS.
Step 3. Creating a task template
1. Log in to the BatchCompute console. In the left sidebar, click Task Template.
2. Select a target region at the top of the Task Template page.
3. Click Create. On the New task template page, create a template, as shown below:
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Name: Enter a custom name, such as rendering.
Description: Enter a custom name, such as 3ds Max 2018 Demo.
Compute environment type: Select a compute environment as needed. Auto compute environment is selected in this example.
Resource configuration: Select S1.LARGE8 (4-core, 8 GB).
Image: Enter a custom image identifier.
Resource quantity: Enter the number of concurrent rendering instances. Example: 1.
Timeout threshold and Number of retry attempts: Keep the default values.
4. Click Next. Configure application information, as shown below:
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Execution method: Select Package.
Package address: Example: cos://barrygz-125xxxxxx4.cos.ap-guangzhou.myqcloud.com/render/max.tar.gz.
Stdout log: For more information about the format, see Entering COS & CFS Paths.
Stderr log: The same as above.
Command line: Enter 3dsmaxcmd Demo.max -outputName:c:\\\\render\\\\image.jpg.
5. Click Next. Configure the storage mapping, as shown below:
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Local path under Output path mapping: Enter C:\\\\render\\\\.
COS/CFS path under Output path mapping: For more information about the format, see Entering COS & CFS Paths.
6. Click Next. Preview the JSON file of the task.
7. Confirm that the configuration is correct and click Save.
Step 4. Submitting a job
1. In the left sidebar, click Jobs to go to the Jobs page.
2. Select a target region at the top of the Jobs page and click Create.
3. Configure the fundamental job information in the “New Job” window, as shown below:
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Job name: Enter max.
Priority: Keep the default value.
Description: Enter 3ds Max 2018 Demo.
4. On the left side of the Task flow page, find the rendering task and drag it to the canvas on the right.
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5. Confirm the configurations in the task details area on the right side of the Task flow page and click Completed.
6. For more information about how to query job running information, see Information Query.
7. Rendering Process Demo.
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8. For more information about how to query rendering results, see Viewing Object Information.
Subsequent Operations
This document illustrates a simple rendering job to demonstrate fundamental BatchCompute capabilities. You can continue to test the advanced capabilities of BatchCompute as instructed in the Console User Guide.
Various CVM configurations: BatchCompute provides a variety of CVM configuration options. You can customize your own CVM configuration based on your business scenario.
Remote storage mapping: BatchCompute optimizes storage access and simplifies access to remote storage services into operations in the local file system.
Parallel rendering of multiple images: With BatchCompute, you can specify the number of concurrent rendering instances and use environment variables to differentiate rendering instances. Each instance reads different rendering materials to achieve parallel rendering.