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GDAL and CUDA Installation

GDAL and CUDA are pre-installed on all ephemeral VM kernels that your notebooks have direct access to. No manual installation required.

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Pre-Installed Software

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Available Configurations

All VM kernels come with:
  • 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

When starting your machine, you can choose from:
  • 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
GDAL CUDA Pre-installed

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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

Now that you have GDAL and CUDA ready:
  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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Need Help?

Happy analyzing!