Fresh Install of a Mask-RCNN Environment on the Google Cloud Platform

Fresh MRCNN Installation Instructions

  1. ssh into the gcloud instance

  2. Install the conda environment. Here are the instructions. Only follow till step 8, as making the conda environment will require a specific instruction.

  3. Download the Matterport implementation of the Mask-RCNN library

     git clone https://github.com/matterport/Mask_RCNN.git
    
  4. Change directory to Mask_RCNN

     cd Mask_RCNN
    
  5. Download the working-gpu.txt file

     wget https://github.com/matterport/Mask_RCNN/files/3870791/working-gpu.txt
    
  6. Create the conda environment using the file flag

     conda create --name <env-name> --file working-gpu.txt
    
  7. Activate the conda environment

     conda activate <env-name>
    
  8. Run the setup script

     python3 setup.py install
    
  9. Check for GPU details.

     nvidia-smi
    

    (If the above fails)

     sudo add-apt-repository ppa:graphics-drivers/ppa
     sudo apt-get update
     sudo apt-get install nvidia-driver-418
    

    (Check again)

     nvidia-smi
    
  10. Make a data directory for gcsfuse to connect your training data bucket to

     cd ~ && mkdir data
    

Notes

Keras Tensorflow Tensorflow-GPU cuDNN cuda-toolkit nvidia-drivers
2.0.8 1.14 1.14 7.4 10.1 418.39