InternalError: Failed to Create Session error in Tensorflow is a common issue faced by developers working with this popular machine learning library. In this guide, we will discuss the possible causes of this error, and provide step-by-step solutions to fix it. Additionally, we'll cover some frequently asked questions to help you better understand this issue.
Table of Contents
- Possible Causes of the Error
- Solutions to Fix the Error
- Solution 1: Update Tensorflow
- Solution 2: Check CUDA and cuDNN Compatibility
- Solution 3: Set GPU Options
- Solution 4: Set CPU as Default Device
- Frequently Asked Questions (FAQs)
- What is the purpose of a Tensorflow session?
- What is CUDA?
- What is cuDNN?
- What is the difference between Tensorflow and Keras?
- How can I check if Tensorflow is using my GPU?
Possible Causes of the Error
InternalError: Failed to Create Session error often occurs due to one of the following reasons:
- An outdated version of Tensorflow is installed on your system.
- Incompatibility between the installed Tensorflow, CUDA, and cuDNN versions.
- Insufficient GPU memory allocation.
- Hardware or driver issues.
Solutions to Fix the Error
Solution 1: Update Tensorflow
An outdated version of Tensorflow might be causing the error. To fix this, update Tensorflow to the latest version using the following command:
pip install --upgrade tensorflow
If you are using Tensorflow with GPU support, use this command instead:
pip install --upgrade tensorflow-gpu
Solution 2: Check CUDA and cuDNN Compatibility
If you are using Tensorflow with GPU support, ensure that you have the correct version of CUDA and cuDNN installed on your system. Check the official Tensorflow documentation for the compatible versions.
To check your current CUDA and cuDNN versions, execute the following commands:
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
If your installed versions are not compatible, download and install the appropriate versions from the Nvidia website.
Solution 3: Set GPU Options
It's possible that the error is occurring due to insufficient GPU memory allocation. To fix this, set the
allow_growth option to
True in your Tensorflow code. This allows Tensorflow to allocate GPU memory based on its requirements. Here's an example:
import tensorflow as tf config = tf.ConfigProto() config.gpu_options.allow_growth = True session = tf.Session(config=config)
Solution 4: Set CPU as Default Device
If none of the above solutions work, try running your Tensorflow code on the CPU instead of the GPU. To do this, set the default device to CPU in your code:
import tensorflow as tf with tf.device('/cpu:0'): # Your Tensorflow code here
What is the purpose of a Tensorflow session?
A Tensorflow session allows you to execute a computation graph or a part of it. It allocates resources (such as GPU memory) for the operations and manages the underlying hardware.
What is CUDA?
CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by Nvidia. It enables developers to use Nvidia GPUs for general-purpose computing tasks, such as machine learning and scientific simulations.
What is cuDNN?
cuDNN (CUDA Deep Neural Network library) is a GPU-accelerated library developed by Nvidia for deep learning frameworks like Tensorflow, Caffe, and Theano. It provides highly optimized primitives for deep learning operations, such as convolution and pooling, to improve the performance of neural networks on GPUs.
What is the difference between Tensorflow and Keras?
Tensorflow is an open-source machine learning library developed by Google, while Keras is a high-level neural networks API that can run on top of Tensorflow, Microsoft Cognitive Toolkit, Theano, and PlaidML. Keras provides an easy-to-use interface for building and training neural networks, while Tensorflow offers more flexibility and control over the underlying computations.
How can I check if Tensorflow is using my GPU?
To check if Tensorflow is using your GPU, run the following code snippet:
import tensorflow as tf if tf.test.gpu_device_name(): print('GPU found:', tf.test.gpu_device_name()) else: print("No GPU found.")
If your GPU is being utilized by Tensorflow, this code will display the name of the GPU device. Otherwise, it will print "No GPU found."