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TensorFlow is a popular open-source machine learning library developed by the Google Brain team. It supports both CPU and GPU acceleration to train and deploy machine learning models efficiently. In this tutorial, we'll guide you through the process of setting up TensorFlow with GPU support on your machine.
NVIDIA GPU: Ensure that your machine has a compatible NVIDIA GPU. TensorFlow relies on CUDA and cuDNN, which are GPU-accelerated libraries provided by NVIDIA.
NVIDIA CUDA Toolkit: Download and install the CUDA Toolkit from the NVIDIA website. Make sure to install a version compatible with your GPU and TensorFlow.
cuDNN: Download the cuDNN library from the NVIDIA website. Install it by following the provided instructions.
Python and pip: Ensure you have Python installed on your machine. You can download the latest version from python.org. Pip is usually included with Python.
Open a terminal or command prompt and run the following command to install TensorFlow using pip. This will install the GPU-enabled version if your system is properly configured.
Now that TensorFlow is installed, let's check if it's utilizing the GPU. Create a Python script (e.g., verify_gpu.py) with the following code:
Run the script using the command:
If everything is set up correctly, you should see the message "GPU is available."
Now, let's create a simple TensorFlow script to demonstrate GPU acceleration. Create a Python script (e.g., gpu_example.py) with the following code:
Run the script using the command:
This script defines a basic convolutional neural network using TensorFlow's Keras API. If TensorFlow is configured correctly with GPU support, the model summary should show GPU-related information.
Congratulations! You have successfully set up TensorFlow with GPU support and created a simple GPU-accelerated script. Now you can leverage the power of your GPU for training and running machine learning models.
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