GPU with Tensorflow

 

You might have used GPU for faster processing of your Machine Learning code with Pytorch. However, do you know that you can use that with Tensorflow as well?

Here are the steps on how to enable GPU acceleration for TensorFlow to achieve faster performance:

1. Verify GPU Compatibility:

• Check for CUDA Support: Ensure your GPU has a compute capability of 3.5 or higher (check NVIDIA's website).
• Install CUDA Toolkit and cuDNN: Download and install the appropriate CUDA Toolkit and cuDNN versions compatible with your TensorFlow version and GPU from NVIDIA's website.

2. Install GPU-Enabled TensorFlow:

• Use pip: If you haven't installed TensorFlow yet, use the following command to install the GPU version:
  Bash

  pip install tensorflow-gpu
  

• Upgrade Existing Installation: If you already have TensorFlow installed, upgrade it to the GPU version:
  Bash

  pip install --upgrade tensorflow-gpu
  

3. Verify GPU Detection:

• Run a TensorFlow script: Create a simple TensorFlow script and run it. If it detects your GPU, you'll see a message like "Found GPU at: /device:GPU:0".
• Check in Python: You can also check within Python:
  Python

  import tensorflow as tf
  print(tf.config.list_physical_devices('GPU'))
  

4. Place Operations on GPU:

• Manual Placement: Specify with tf.device('/GPU:0') to place operations on GPU:
  Python

  with tf.device('/GPU:0'):
      # Code to run on GPU
  

• Automatic Placement: TensorFlow often places operations on the GPU automatically if available.

5. Monitor GPU Usage:

• Tools: Use tools like NVIDIA System Management Interface (nvidia-smi) or TensorFlow's profiling tools to monitor GPU usage and memory during training.

Additional Tips:

• TensorFlow Version: Ensure your TensorFlow version is compatible with your CUDA and cuDNN versions.
• Multiple GPUs: If you have multiple GPUs, TensorFlow can utilize them by setting tf.config.set_visible_devices().
• Performance Optimization: Explore techniques like mixed precision training and XLA compilation for further performance gains.

Remember:

• Consult TensorFlow's documentation for the most up-to-date instructions and troubleshooting tips. https://www.tensorflow.org/guide/gpu
• GPU acceleration can significantly improve performance, especially for large models and datasets.