mirror of
https://github.com/deepseek-ai/DreamCraft3D
synced 2024-12-05 02:25:45 +00:00
60 lines
2.8 KiB
Markdown
60 lines
2.8 KiB
Markdown
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# Installation
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## Prerequisite
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- NVIDIA GPU with at least 6GB VRAM. The more memory you have, the more methods and higher resolutions you can try.
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- [NVIDIA Driver](https://www.nvidia.com/Download/index.aspx) whose version is higher than the [Minimum Required Driver Version](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) of CUDA Toolkit you want to use.
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## Install CUDA Toolkit
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You can skip this step if you have installed sufficiently new version or you use Docker.
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Install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
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- Example for Ubuntu 22.04:
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- Run [command for CUDA 11.8 Ubuntu 22.04](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local)
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- Example for Ubuntu on WSL2:
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- `sudo apt-key del 7fa2af80`
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- Run [command for CUDA 11.8 WSL-Ubuntu](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local)
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## Git Clone
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```bash
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git clone https://github.com/threestudio-project/threestudio.git
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cd threestudio/
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```
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## Install threestudio via Docker
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1. [Install Docker Engine](https://docs.docker.com/engine/install/).
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This document assumes you [install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/ubuntu/).
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2. [Create `docker` group](https://docs.docker.com/engine/install/linux-postinstall/).
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Otherwise, you need to type `sudo docker` instead of `docker`.
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3. [Install NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#setting-up-nvidia-container-toolkit).
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4. If you use WSL2, [enable systemd](https://learn.microsoft.com/en-us/windows/wsl/wsl-config#systemd-support).
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5. Edit [Dockerfile](../docker/Dockerfile) for your GPU to speed-up build.
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The default Dockerfile takes into account many types of GPUs.
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6. Run Docker via `docker compose`.
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```bash
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cd docker/
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docker compose build # build Docker image
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docker compose up -d # create and start a container in background
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docker compose exec threestudio bash # run bash in the container
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# Enjoy threestudio!
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exit # or Ctrl+D
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docker compose stop # stop the container
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docker compose start # start the container
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docker compose down # stop and remove the container
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```
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Note: The current Dockerfile will cause errors when using the OpenGL-based rasterizer of nvdiffrast.
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You can use the CUDA-based rasterizer by adding commands or editing configs.
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- `system.renderer.context_type=cuda` for training
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- `system.exporter.context_type=cuda` for exporting meshes
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[This comment by the nvdiffrast author](https://github.com/NVlabs/nvdiffrast/issues/94#issuecomment-1288566038) could be a guide to resolve this limitation.
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