GDAL and CUDA Installation

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GDAL and CUDA Installation

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Pre-Installed Software

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Available Configurations

• GDAL 3.9.1: Geospatial Data Abstraction Library for raster and vector data
• CUDA 12.2: NVIDIA CUDA for GPU acceleration
• Python 3.10/3.11: Multiple Python versions available

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Kernel Selection

• Python 3.10, GDAL 3.9.1 CUDA 12.2
• Python 3.11, GDAL 3.9.1 CUDA 12.2
• Python 3.10, GDAL 3.9.1 CUDA 12

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Ready to Use

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No Installation Required

• Pre-configured: All geospatial libraries are ready to use
• GPU Ready: CUDA drivers and libraries are installed
• Immediate Access: Start analyzing geospatial data right away

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Direct Access

• Workspace Integration: Direct access to workspace files
• Large Dataset Support: Optimized for TB-PB sized geospatial data
• Performance Optimized: GPU acceleration for raster processing

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Usage Examples

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GDAL Operations

import rasterio
import geopandas as gpd

# Read raster data
with rasterio.open('/workspace/data/satellite.tif') as src:
    data = src.read()

# Read vector data
gdf = gpd.read_file('/workspace/data/shapefile.shp')

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CUDA Acceleration

import cupy as cp
import numpy as np

# GPU-accelerated array operations
gpu_array = cp.array(np.random.random((1000, 1000)))
result = cp.linalg.svd(gpu_array)

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Next Steps

1. Start Analysis: Begin your geospatial analysis immediately
2. Load Data: Access your workspace data files
3. Use GPU: Leverage CUDA for accelerated processing
4. Scale Up: Work with large geospatial datasets efficiently

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