How to Tell if Tensorflow is Using GPU Acceleration from Inside Python Shell

As a data scientist or software engineer, you may find yourself working with Tensorflow, a popular open-source machine learning library. Tensorflow is known for its ability to perform computations on both CPUs and GPUs, making it a powerful tool for data scientists and machine learning practitioners alike.

One question you may have while working with Tensorflow is how to tell if it is using GPU acceleration from inside the Python shell. In this blog post, we will explore the answer to this question and explain the solution step-by-step.

Table of Contents

1. What is GPU Acceleration?
2. Checking if Tensorflow is Using GPU Acceleration
3. Configuring Tensorflow to Use GPU Acceleration
4. Common Errors and How to Handle Them
5. Conclusion

What is GPU Acceleration?

Before we dive into the solution, let’s briefly explain what GPU acceleration is. GPU stands for Graphics Processing Unit, which is a specialized processor originally designed to process graphics. However, GPUs are also useful for performing parallel computations, making them ideal for machine learning tasks.

GPU acceleration refers to the use of a GPU to speed up computations. This is possible because GPUs have many more cores than CPUs, allowing them to perform many computations in parallel. When Tensorflow is configured to use GPU acceleration, it can perform computations much faster than when using only the CPU.

Checking if Tensorflow is Using GPU Acceleration

To check if Tensorflow is using GPU acceleration from inside the Python shell, follow these steps:

1. Import Tensorflow into your Python shell by typing import tensorflow as tf.
2. Create a Tensorflow session by typing sess = tf.Session().
3. Run the following code to check if Tensorflow is using GPU acceleration:

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

This code will print a list of the devices available on your system, including the GPUs. If Tensorflow is using GPU acceleration, you should see one or more GPU devices listed in the output.

Here’s an example output:

[name: "/device:CPU:0"
 device_type: "CPU"
 memory_limit: 268435456
 locality {
 }
 incarnation: 8877027196266987643, name: "/device:GPU:0"
 device_type: "GPU"
 memory_limit: 11324640282
 locality {
   bus_id: 1
   links {
   }
 }
 incarnation: 18318543663795231630
 physical_device_desc: "device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1"]

In this example, we can see that there are two devices listed: /device:CPU:0 and /device:GPU:0. The GPU device is a GeForce GTX 1080, and Tensorflow is using it for computations.

Configuring Tensorflow to Use GPU Acceleration

If you find that Tensorflow is not using GPU acceleration, you may need to configure it to do so. Here are the steps to configure Tensorflow to use GPU acceleration:

1. Install the GPU version of Tensorflow by typing pip install tensorflow-gpu in your terminal or command prompt.
2. Verify that you have a compatible version of CUDA and cuDNN installed on your system. You can find the compatible versions in the Tensorflow documentation.
3. Set the CUDA_VISIBLE_DEVICES environment variable to the index of the GPU you want to use. For example, if you have only one GPU, set CUDA_VISIBLE_DEVICES=0. If you have multiple GPUs, you can specify a comma-separated list of indices, such as CUDA_VISIBLE_DEVICES=0,1.
4. When you create a Tensorflow session, specify that you want to use the GPU by passing the gpu_options argument to the session constructor. Here’s an example:

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

This code sets the allow_growth option to True, which allows Tensorflow to allocate GPU memory on an as-needed basis. This can be useful if you have limited GPU memory.

Common Errors and How to Handle Them

Ensuring that TensorFlow effectively utilizes GPU acceleration can be accompanied by various challenges. Here are some common errors you might encounter and detailed instructions on how to handle them:

CUDA Toolkit and cuDNN Compatibility

One frequent source of errors is a mismatch between the versions of CUDA Toolkit and cuDNN installed on your system and the versions expected by TensorFlow. To check compatibility, refer to the official TensorFlow documentation for the version you have installed.

# Example code to check CUDA and cuDNN versions
from tensorflow.python.platform import build_info as tf_build_info

print("CUDA Version:", tf_build_info.cuda_version_number)
print("cuDNN Version:", tf_build_info.cudnn_version_number)

Ensure that your installed versions are compatible with the TensorFlow version. If there’s a mismatch, consider upgrading or downgrading either TensorFlow or the CUDA Toolkit and cuDNN to achieve compatibility.

Incorrect TensorFlow Installation

An incorrectly installed TensorFlow can lead to fallback to CPU usage, even if GPU resources are available. To handle this, follow these steps:

• Check TensorFlow Installation:

  import tensorflow as tf
  print("TensorFlow Version:", tf.__version__)
  

• Reinstall TensorFlow: If the version is not displayed or indicates an error, consider reinstalling TensorFlow following the official installation instructions for your system.

pip uninstall tensorflow
pip install tensorflow

GPU Memory Issues

Insufficient GPU memory can result in TensorFlow reverting to CPU. To address GPU memory issues:

• Monitor GPU Memory Usage:

  # Example code to monitor GPU memory usage
  physical_devices = tf.config.list_physical_devices('GPU')
  if physical_devices:
      try:
          for device in physical_devices:
              tf.config.experimental.set_memory_growth(device, True)
          print("GPU memory growth set to True.")
      except RuntimeError as e:
          print(e)
  

• Optimize Model or Upgrade GPU: If your model consumes too much memory, consider optimizing the model architecture or reducing the batch size. Alternatively, upgrading to a GPU with more memory may be necessary.

Conclusion

Determining if TensorFlow is using GPU acceleration is essential for optimizing machine learning workflows. By following the methods outlined in this article, you can easily check and troubleshoot GPU usage directly from the Python shell. Keep in mind the common errors to ensure a smooth GPU-accelerated TensorFlow experience.

SHARE:

About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Request a demo today to learn more.