Both models will be downloaded using the vit_h weights.
docker build -t sam-builder -f Dockerfile-build .
docker run -d --name sam-builder1 sam-builder
docker cp sam-builder1:/home/model-store ./
We copy these to model-store and use this locally by both the GPU and the CPU Torchserve containers.
you can delete the container once models are copied
docker rm -f sam-builder1
With the GPU, inference time should be about 1.8 seconds or less depending on the GPU. On an older 1080 Ti Pascal GPU, inference time is 1.67 seconds without compilation.
docker build -t sam-gpu -f Dockerfile-gpu .
bash start_serve_encode_gpu.sh
docker build -t sam-cpu -f Dockerfile-cpu .
bash start_serve_decode_cpu.sh
The CPU service is served on 7080 by default. 8080 for the GPU service by default.
curl http://127.0.0.1:7080/predictions/sam_vit_h_encode -T ./data/sample-img-fox.jpg
If you have access, download from the devseed s3:
aws s3 sync s3://segment-anything/model-weights/ model-weights
otherwise, get checkpoints from the original repo: https://github.com/facebookresearch/segment-anything/tree/main#model-checkpoints
This step takes a long time presumably because the uncompiled weights are massive. Packaging the ONNX model is faster in the later steps.
mkdir -p model_store_encode
torch-model-archiver --model-name sam_vit_h_encode --version 1.0.0 --serialized-file model-weights/sam_vit_h_4b8939.pth --handler handler_encode.py
mv sam_vit_h_encode.mar model_store_encode/sam_vit_h_encode.mar
mkdir -p models
python scripts/export_onnx_model.py --checkpoint model-weights/sam_vit_h_4b8939.pth --model-type vit_h --output models/sam_vit_h_decode.onnx
We'll put this in the model_store_decode directory, to keep the onnx model files distinct from the torchserve .mar model archives. model_store/ is created automatically by Torchserve in the container, which is why we're make a local folder here called "model_store_decode".
mkdir -p model_store_decode
torch-model-archiver --model-name sam_vit_h_decode --version 1.0.0 --serialized-file models/sam_vit_h_decode.onnx --handler handler_decode.py
mv sam_vit_h_decode.mar model_store_decode/sam_vit_h_decode.mar
Use this container to test the model in a GPU enabled jupyter notebook server with geospatial and pytorch dependencies installed.
docker build -t sam-dev -f Dockerfile-dev .
You can run test_endpoint.ipynb to then use the two running services you started above. The dependencies are minimal for this notebook, install them on your own or you can run them in the jupyter server below.
This is a GPU enabled container that is set up with SAM and some other dependencies we commonly use. You can use it to try out SAM model in a notebook environment. Remove the --gpus arg if you don't have a GPU.
docker run -it --rm \
-v $HOME/.aws:/root/.aws \
-v "$(pwd)":/segment-anything-services \
-p 8888:8888 \
-e AWS_PROFILE=devseed \
--gpus all sam-dev
Q: Why two services?
A: We're exploring cost effective ways to run image encoding in a separate, on-demand way from the CPU decoder. Eventually we'd like to remove the need for the CPU torserve on the backend and run the decoding in the browser.
Q: Can I contribute or ask questions?
A: This is currently more of a "working in the open" type repo that we'd like to share with others, rather than a maintained project. But feel free to open an issue if you have an idea. Please understand if we don't respond or are slow to respond.
The model and code is licensed under the Apache 2.0 license.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., ... Girshick, R. (2023). Segment Anything. arXiv:2304.02643. https://github.com/facebookresearch/segment-anything
The scripts/export_onnx_model.ipynb and notebooks/sam_onnx_model_example_fox.ipynb are from the original repo.