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add build-arg cuda version to dockerfile (#2139)

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Ilya Basharov authored and GitHub committed 2023-07-01 17:06:05 +00:00
1 parent f4be2b729a
commit 5c4b0a1dd6
2 files changed
+16 -6
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Dockerfile
+10 -5
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@@ -1,11 +1,13 @@
ARG CUDA_VERSION=11.8.0
ARG OS_VERSION=22.04
# Define base image.
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION}
# metainformation
LABEL org.opencontainers.image.version = "0.1.18"
LABEL org.opencontainers.image.source = "https://github.com/nerfstudio-project/nerfstudio"
LABEL org.opencontainers.image.licenses = "Apache License 2.0"
LABEL org.opencontainers.image.base.name="docker.io/library/nvidia/cuda:11.8.0-devel-ubuntu22.04"
LABEL org.opencontainers.image.base.name="docker.io/library/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION}"
# Variables used at build time.
## CUDA architectures, required by Colmap and tiny-cuda-nn.
@@ -115,7 +117,10 @@ SHELL ["/bin/bash", "-c"]
# Upgrade pip and install packages.
RUN python3.10 -m pip install --upgrade pip setuptools pathtools promise pybind11
# Install pytorch and submodules
RUN python3.10 -m pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
RUN CUDA_VER=${CUDA_VERSION%.*} && CUDA_VER=${CUDA_VER//./} && python3.10 -m pip install \
torch==2.0.1+cu${CUDA_VER} \
torchvision==0.15.2+cu${CUDA_VER} \
--extra-index-url https://download.pytorch.org/whl/cu${CUDA_VER}
# Install tynyCUDNN (we need to set the target architectures as environment variable first).
ENV TCNN_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES}
RUN python3.10 -m pip install git+https://github.com/NVlabs/tiny-cuda-nn.git@v1.6#subdirectory=bindings/torch
@@ -128,7 +133,7 @@ RUN git clone --branch v0.3.0 --recursive https://github.com/colmap/pycolmap.git
cd ..
# Install hloc master (last release (1.3) is too old) as alternative feature detector and matcher option for nerfstudio.
RUN git clone --branch master --recursive https://github.com/cvg/Hierarchical-Localization && \
RUN git clone --branch master --recursive https://github.com/cvg/Hierarchical-Localization.git && \
cd Hierarchical-Localization && \
python3.10 -m pip install -e . && \
cd ..
@@ -140,7 +145,7 @@ RUN git clone --branch main --recursive https://github.com/cvg/pyceres.git && \
cd ..
# Install pixel perfect sfm.
RUN git clone --branch main --recursive https://github.com/cvg/pixel-perfect-sfm && \
RUN git clone --branch main --recursive https://github.com/cvg/pixel-perfect-sfm.git && \
cd pixel-perfect-sfm && \
python3.10 -m pip install -e . && \
cd ..
docs/quickstart/installation.md
+6 -1
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@@ -144,7 +144,12 @@ build arg and look up [the compute capability for your GPU](https://developer.nv
For example, here's how to build with support for GeForce 30xx series GPUs:
```bash
docker build --build-arg CUDA_ARCHITECTURES=86 --tag nerfstudio-86 -f Dockerfile .
docker build \
--build-arg CUDA_VERSION=11.8.0 \
--build-arg CUDA_ARCHITECTURES=86 \
--build-arg OS_VERSION=22.04 \
--tag nerfstudio-86 \
--file Dockerfile .
```
### Using an interactive container