metadata

title: SamGIS - LISA on ZeroGPU
emoji: 🗺️
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 4.37.2
app_file: app.py
pinned: true
license: mit

LISA + SamGIS on Zero GPU!

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

I also adapted LISA to HuggingFace lisa-on-cuda ZeroGPU space.

Custom environment variables for HuggingFace ZeroGPU Space

Fundamental environment variables you need are:

XDG_CACHE_HOME="/data/.cache"
PROJECT_ROOT_FOLDER="/home/user/app"
WORKDIR="/home/user/app"

Derived ones:

MPLCONFIGDIR="/data/.cache/matplotlib"
TRANSFORMERS_CACHE="/data/.cache/transformers"
PYTORCH_KERNEL_CACHE_PATH="/data/.cache/torch/kernels"
FASTAPI_STATIC="/home/user/app/static"
VIS_OUTPUT="/home/user/app/vis_output"
MODEL_FOLDER="/home/user/app/machine_learning_models"
FOLDERS_MAP='{"WORKDIR":"/home/user/app","XDG_CACHE_HOME":"/data/.cache","PROJECT_ROOT_FOLDER":"/home/user/app","MPLCONFIGDIR":"/data/.cache/matplotlib","TRANSFORMERS_CACHE":"/data/.cache/transformers","PYTORCH_KERNEL_CACHE_PATH":"/data/.cache/torch/kernels","FASTAPI_STATIC":"/home/user/app/static","VIS_OUTPUT":"/home/user/app/vis_output"}'

The function build_frontend() from lisa_on_cuda package create all the folders required for this project using the environment variable FOLDERS_MAP. That's useful for cache folders (XDG_CACHE_HOME, MPLCONFIGDIR, TRANSFORMERS_CACHE, PYTORCH_KERNEL_CACHE_PATH) because missing these can slow down the inference process. Also you could keep these folders in a permanent storage disk mounted on a custom path.

To change the base relative url for custom frontend add the VITE_PREFIX environment variable, e.g.:

VITE_PREFIX="/custom-url"