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Installation

Install from PyPI

segment-geospatial is available on PyPI. To install segment-geospatial, run this command in your terminal:

pip install segment-geospatial

Install from conda-forge

segment-geospatial is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can install segment-geospatial using the following commands. It is recommended to create a fresh conda environment for segment-geospatial. The following commands will create a new conda environment named geo and install segment-geospatial and its dependencies:

conda create -n geo python
conda activate geo
conda install -c conda-forge mamba
mamba install -c conda-forge segment-geospatial

If your system has a GPU, but the above commands do not install the GPU version of pytorch, you can force the installation of the GPU version of pytorch using the following command:

mamba install -c conda-forge segment-geospatial "pytorch=*=cuda*"

Samgeo-geospatial has some optional dependencies that are not included in the default conda environment. To install these dependencies, run the following command:

mamba install -c conda-forge groundingdino-py segment-anything-fast

Install from GitHub

To install the development version from GitHub using Git, run the following command in your terminal:

pip install git+https://github.com/opengeos/segment-geospatial

Use docker

You can also use docker to run segment-geospatial:

docker run -it -p 8888:8888 giswqs/segment-geospatial:latest

To enable GPU for segment-geospatial, run the following command to run a short benchmark on your GPU:

docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark

The output should be similar to the following:

Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
        -fullscreen       (run n-body simulation in fullscreen mode)
        -fp64             (use double precision floating point values for simulation)
        -hostmem          (stores simulation data in host memory)
        -benchmark        (run benchmark to measure performance)
        -numbodies=<N>    (number of bodies (>= 1) to run in simulation)
        -device=<d>       (where d=0,1,2.... for the CUDA device to use)
        -numdevices=<i>   (where i=(number of CUDA devices > 0) to use for simulation)
        -compare          (compares simulation results running once on the default GPU and once on the CPU)
        -cpu              (run n-body simulation on the CPU)
        -tipsy=<file.bin> (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
GPU Device 0: "Turing" with compute capability 7.5

> Compute 7.5 CUDA device: [Quadro RTX 5000]
49152 bodies, total time for 10 iterations: 69.386 ms
= 348.185 billion interactions per second
= 6963.703 single-precision GFLOP/s at 20 flops per interaction

If you encounter the following error:

nvidia-container-cli: initialization error: load library failed: libnvidia-ml.so.1: cannot open shared object file: no such file or directory: unknown.

Try adding sudo to the command:

sudo docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark

Once everything is working, you can run the following command to start a Jupyter Notebook server:

docker run -it -p 8888:8888 --gpus=all giswqs/segment-geospatial:latest