train-detectron2.sh

# define hashes
GIT_HASH=0a7a9d10
QUILT_HASH=3722a498
DOCKER_HASH=sha256:8a4f4123c92a7fe2e8ca4c404094ab95dc1fb868ad077d2e084ba4082a5a29c1
# pull image
DOCKER_IMAGE=quiltdata/pytorch-detectron2-demo@${DOCKER_HASH}
docker pull ${DOCKER_IMAGE}
# run image (interactively for illustration)
nvidia-docker run -it \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    --shm-size=8gb \
    -e GIT_HASH=${GIT_HASH} \
    -e QUILT_HASH=${QUILT_HASH} \
    ${DOCKER_IMAGE}
## clone and install detectron2
git clone https://github.com/facebookresearch/detectron2
cd detectron2
git checkout ${GIT_HASH}
pip install -e .
## install data
quilt3 install cv/coco2017 \
    --registry=s3://quilt-ml \
    --dest=./datasets/coco/ \
    --top-hash=${QUILT_HASH}
## train
python tools/train_net.py \
    --num-gpus 8 \
    --config-file \
        configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml