Practical solutions to simplify installation configuration
To simplify the installation process, the following measures can be taken:
- Pre-built containers:Use Docker images to encapsulate complete environments and circumvent version conflicts
- Step-by-step verification:Check the installation status of CUDA → Pytorch → Dependent Libraries in order
- Environmental isolation:Strictly use conda virtual environments to manage Python versions
- Pre-compiled packages:Consider using precompiled versions for complex dependencies such as torchsparse.
For team usage scenarios, it is recommended to make one-click installation scripts to automate the dependency and version checking process.
This answer comes from the articleSpatialLM: Sweep the room, AI automatically draws 3D models for youThe
May not be reproduced without permission:AI productivity tools " How to optimize the installation and configuration process of SpatialLM in different hardware environments?
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