40 lines
1.4 KiB
Docker
40 lines
1.4 KiB
Docker
# Start from Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
|
|
FROM nvcr.io/nvidia/pytorch:19.08-py3
|
|
|
|
# Create working directory
|
|
RUN mkdir -p /usr/src/app
|
|
WORKDIR /usr/src/app
|
|
|
|
# Copy contents
|
|
COPY . /usr/src/app
|
|
|
|
# Install dependencies (pip or conda)
|
|
# RUN pip install -U -r requirements.txt
|
|
# RUN conda update -n base -c defaults conda
|
|
# RUN conda install -y -c anaconda future numpy opencv matplotlib tqdm pillow
|
|
# RUN conda install -y -c conda-forge scikit-image tensorboard pycocotools
|
|
# conda install pytorch torchvision -c pytorch
|
|
|
|
# Install OpenCV with Gstreamer support
|
|
# ...
|
|
|
|
# Move model into container
|
|
# RUN mv yolov3-spp.pt ./weights
|
|
|
|
|
|
# --------------------------------------------------- Extras Below ---------------------------------------------------
|
|
|
|
# Build container
|
|
# rm -rf yolov3 # Warning: remove existing
|
|
# git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 detect.py
|
|
# sudo docker image prune -af && sudo docker build -t ultralytics/yolov3:v0 .
|
|
|
|
# Run container
|
|
# sudo nvidia-docker run --ipc=host ultralytics/yolov3:v0 python3 detect.py
|
|
|
|
# Run container accesing local directory
|
|
# sudo nvidia-docker run --ipc=host --mount type=bind,source="$(pwd)"/coco,target=/usr/src/coco ultralytics/yolov3:v0 python3 train.py
|
|
|
|
# Push container to https://hub.docker.com/u/ultralytics
|
|
# docker push ultralytics/yolov3:v0
|