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Fixing libcuda.so.1 Missing Error in Google Colab
When using CUDA libraries in Google Colab, it's common to encounter shared library errors. Here’s a systematic approach to resolving the error involving libcuda.so.1.
Step 1: Verify Library Presence
First, check if the libcuda.so.1 file exists and identify its location using:
!sudo find /usr/ -name 'libcuda.so.*'
Assuming you find /usr/local/cuda-12.2/compat/libcuda.so.1 and it points to a valid shared library version like libcuda.so.535.129.03, proceed to the next step.
Step 2: Set LD_LIBRARY_PATH Correctly
The LD_LIBRARY_PATH should include the directory containing libcuda.so.1, not the file path itself. Set it correctly in Python:
import os
# Set the directory path where libcuda.so.1 is located
cuda_lib_path = '/usr/local/cuda-12.2/compat'
current_ld_library_path = os.environ.get('LD_LIBRARY_PATH', '')
new_ld_library_path = f"{cuda_lib_path}:{current_ld_library_path}"
# Update the environment variable
os.environ['LD_LIBRARY_PATH'] = new_ld_library_path
print("Updated LD_LIBRARY_PATH:", os.environ['LD_LIBRARY_PATH'])
Step 3: Restart the Runtime
Changes to environment variables in Colab do not take effect until you restart the runtime. Restart it by:
- Navigating to
Runtime -> Restart runtime from the Colab menu.
Step 4: Verify Library Path
After restarting, check that the LD_LIBRARY_PATH includes your directory:
!echo $LD_LIBRARY_PATH
Step 5: Test Library Accessibility
Ensure the system recognizes libcuda.so.1 in the updated path:
!ldconfig -p | grep libcuda.so.1
This command should list libcuda.so.1 if it’s correctly located.
Step 6: Execute Darknet
Run your Darknet command again. If set up properly, it should now find libcuda.so.1 without errors.
!./darknet detector train <your_config_file_path>
Troubleshooting Tips
Library Still Not Found? You might try setting the library path explicitly when executing your binary using LD_PRELOAD:
!LD_PRELOAD=/usr/local/cuda-12.2/compat/libcuda.so.1 ./darknet <args>
Ensure GPU Usage: Confirm that your Colab session is using a GPU by checking the runtime type:
