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title: SamGIS - LISA on CUDA
emoji: 🗺️
colorFrom: red
colorTo: blue
sdk: docker
pinned: false
license: mit
Segment Anything models
It's possible to prepare the model files using https://github.com/vietanhdev/samexporter/ or using the ones
from https://huggingface.co/aletrn/sam-quantized (copy them within the folder /machine_learning_models
).
SamGIS - HuggingFace version
The SamGIS HuggingSpace url is https://huggingface.co/spaces/aletrn/samgis-lisa-on-cuda. Build the docker image this way:
# clean any old active containers
docker stop $(docker ps -a -q); docker rm $(docker ps -a -q)
# build the base docker image from the repository root folder using ARGs:
# - DEPENDENCY_GROUP=fastapi used by poetry
# VITE__MAP_DESCRIPTION, VITE__SAMGIS_SPACE used by 'docker build'
(
set -o allexport && source <(cat ./static/.env|grep VITE__) && set +o allexport;
env|grep VITE__;
docker build . -f dockerfiles/dockerfile-samgis-base --progress=plain \
--build-arg DEPENDENCY_GROUP=fastapi \
--build-arg VITE__MAP_DESCRIPTION=${VITE__MAP_DESCRIPTION} \
--build-arg VITE__SAMGIS_SPACE=${VITE__SAMGIS_SPACE} \
--tag registry.gitlab.com/aletrn/gis-prediction
)
# build the image, use the tag "samgis-huggingface"
docker build . --tag example-docker-namespace/samgis-huggingface --progress=plain
Run the container (keep it on background) and show logs
docker run -d --name samgis-huggingface -p 7860:7860 example-docker-namespace/samgis-huggingface; docker logs -f samgis-huggingface
Test it with curl using a json payload:
URL=http://localhost:7860/infer_samgis
curl -d@./events/payload_point_eolie.json -H 'accept: application/json' ${URL}
or better visiting the swagger page on http://localhost:7860/docs
Dependencies installation and local tests
The docker build process needs only the base dependency group plus the aws_lambda
or fastapi
optional one.
Install also the test
and/or docs
groups if needed.
Tests
Tests are defined in the tests
folder in this project. Use PIP to install the test dependencies and run tests.
python -m pytest --cov=samgis_lisa_on_cuda --cov-report=term-missing && coverage html
How to update the static documentation with sphinx
This project documentation uses sphinx-apidoc: it's a tool for automatic generation of Sphinx sources that, using the autodoc extension, document a whole package in the style of other automatic API documentation tools. See the documentation page for details. Run the command from the project root:
# missing docs folder (run from project root) initialize this way
cd docs && sphinx-quickstart -p SamGIS -r 1.0.0 -l python --master index
# update docs folder (from project root)
sphinx-apidoc -f -o docs samgis
Then it's possible to generate the HTML pages
cd docs && make html && ../
# to clean old files
cd docs && make clean html && cd ../
The static documentation it's now ready at the path docs/_build/html/index.html
.
To create a work in progress openapi json or yaml file use
extract-openapi-fastapi.py
extract-openapi-lambda.py
(useful to export the json schema request and response from lambda app api)
Handle dynamic folder creation
it's possible to dynamically create a new support folder adding it to a json string env FOLDERS_MAP, e.g.:
{
"WORKDIR": "/var/task",
"XDG_CACHE_HOME": "/data",
"PROJECT_ROOT_FOLDER": "/data",
"MPLCONFIGDIR": "/data/.cache/matplotlib",
"TRANSFORMERS_CACHE": "/data/.cache/transformers",
"PYTORCH_KERNEL_CACHE_PATH": "/data/.cache/torch/kernels",
"FASTAPI_STATIC": "/var/task/static",
"VIS_OUTPUT": "/data/vis_output"
}
The python script create_folders_and_variables_if_not_exists.py will read this env variable, removing any files that exists with these pathnames and assert the correct creation of all the folders. Also these folders must exist as env variables, so the script assert that an env variable exists with its path.