metadata

title: SamGIS - LISA on CUDA
emoji: 🗺️
colorFrom: red
colorTo: blue
sdk: docker
pinned: false
license: mit

LISA and SamGIS

LISA (Reasoning Segmentation via Large Language Model) applied to geospatial data thanks to SamGIS.

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.

It's possible to use the project in a bare metal installation (not within a docker container). To do this

download this project
prepare a virtualenv and install the python dependencies
install nodejs LTS

create a .env_source file (in this case HOME=/home/jovyan)

export FOLDERS_MAP='{"WORKDIR":"/home/jovyan/workspace/samgis-lisa-on-cuda","XDG_CACHE_HOME":"/home/jovyan/.cache","PROJECT_ROOT_FOLDER":"/home/jovyan/","MPLCONFIGDIR":"/home/jovyan/.cache/matplotlib","TRANSFORMERS_CACHE":"/home/jovyan/.cache/transformers","PYTORCH_KERNEL_CACHE_PATH":"/home/jovyan/.cache/torch/kernels","FASTAPI_STATIC":"/home/jovyan/workspace/samgis-lisa-on-cuda/static","VIS_OUTPUT":"/home/jovyan/workspace/samgis-lisa-on-cuda/vis_output"}'
export WORKDIR="$HOME/workspace/samgis-lisa-on-cuda"
export XDG_CACHE_HOME="$HOME/.cache"
export PROJECT_ROOT_FOLDER="$HOME/"
export MPLCONFIGDIR="$HOME/.cache/matplotlib"
export TRANSFORMERS_CACHE="$HOME/.cache/transformers"
export PYTORCH_KERNEL_CACHE_PATH="$HOME/.cache/torch/kernels"
export FASTAPI_STATIC="$HOME/workspace/samgis-lisa-on-cuda/static"
export VIS_OUTPUT="$HOME/workspace/samgis-lisa-on-cuda/vis_output"
export WRITE_TMP_ON_DISK=${VIS_OUTPUT}

execute the script baremetal_entrypoint.sh instead than docker_entrypoint.sh.