🐶
Tensorflow

Fix: cudart64_101.dll Error on TensorFlow CPU Installation

By Ondřej Dolanský on 12/09/2024

Troubleshooting tips for the "Could not load dynamic library 'cudart64_101.dll'" error in TensorFlow CPU-only installations, ensuring smooth and CUDA-free deep learning.

Fix: cudart64_101.dll Error on TensorFlow CPU Installation

Table of Contents

Introduction

The error message "Could not load dynamic library 'cudart64_101.dll'" is a common issue encountered when working with TensorFlow and GPUs. It usually signals a compatibility problem between your TensorFlow installation, your NVIDIA CUDA version, and the NVIDIA cuDNN library version. This guide will walk you through the steps to troubleshoot and fix this error.

Step-by-Step Guide

The error "Could not load dynamic library 'cudart64_101.dll'" typically occurs when there's a mismatch between your TensorFlow installation, CUDA version, and cuDNN version. Here's a breakdown of how to resolve this:

  1. Verify TensorFlow Installation:

    • Ensure you've installed the GPU version of TensorFlow.
    • You can check this using pip list | grep tensorflow
    • It should show tensorflow-gpu and the installed version.
  2. Check CUDA Version Compatibility:

    • The '101' in 'cudart64_101.dll' indicates CUDA version 10.1.
    • Confirm that your installed CUDA version matches.
    • You can usually find this information in the NVIDIA Control Panel or by running nvidia-smi in your command prompt or terminal.
  3. cuDNN Installation and Compatibility:

    • Verify that you have cuDNN installed and that its version is compatible with your CUDA version and TensorFlow installation.
    • You can find compatible cuDNN versions on the NVIDIA website.
  4. Environment Variables and Paths:

    • Make sure your system's environment variables are set correctly.
    • CUDA_PATH and Path should include the directories containing CUDA binaries and libraries.
  5. DLL Path:

    • If the 'cudart64_101.dll' file exists in your CUDA installation directory but is not being found, you might need to add its location to your system's Path environment variable.
  6. Reinstall TensorFlow (if necessary):

    • If you've confirmed compatibility issues, try reinstalling TensorFlow:
      pip uninstall tensorflow-gpu
      pip install tensorflow-gpu==<compatible_version> 
      • Replace <compatible_version> with a version that matches your CUDA and cuDNN setup.
  7. Restart:

    • After making changes to environment variables or reinstalling software, restart your system or, at least, your Python environment.

If you're still facing issues, provide the following information for more specific guidance:

  • Operating System (Windows, Linux, macOS)
  • TensorFlow version
  • CUDA version
  • cuDNN version
  • How you installed TensorFlow (pip, conda, from source)

Code Example

This Python code checks for a working TensorFlow GPU installation. It verifies TensorFlow and CUDA versions, checks environment variables, and provides instructions for troubleshooting. It includes code to check for available GPUs and reinstall TensorFlow if needed. The code is annotated with explanations and reminders for customization.

import tensorflow as tf
import os

# **1. Verify TensorFlow Installation**
try:
    tf_gpu = tf.test.is_gpu_available()
    print("TensorFlow-GPU installed:", tf_gpu)
    print("TensorFlow version:", tf.__version__)
except Exception as e:
    print("Error checking TensorFlow:", e)

# **2. Check CUDA Version Compatibility**
try:
    cuda_version_output = os.popen('nvidia-smi --query-gpu=driver_version --format=csv,noheader').read().strip()
    print("CUDA Version:", cuda_version_output)
except Exception as e:
    print("Error getting CUDA version:", e)

# **3. cuDNN Installation and Compatibility (Check cuDNN version manually)**
# (No direct Python check for cuDNN version, refer to cuDNN installation directory)

# **4. Environment Variables and Paths (Example for Windows)**
print("CUDA_PATH:", os.environ.get('CUDA_PATH'))
print("Path:", os.environ.get('Path'))

# **5. DLL Path (Example for Windows)**
# (Ensure 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin' 
#  or similar is in your Path environment variable)

# **6. Reinstall TensorFlow (if necessary)**
# (Uncomment and run if needed, replace <compatible_version>)
# os.system('pip uninstall tensorflow-gpu')
# os.system('pip install tensorflow-gpu==<compatible_version>')

# **7. Restart**
# (Restart your system or Python environment after changes)

# Test if TensorFlow can access the GPU
if tf_gpu:
    print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
else:
    print("No GPU access detected.")

Explanation:

  1. TensorFlow Installation:

    • We attempt to import TensorFlow and check if GPU support is available using tf.test.is_gpu_available().
    • The TensorFlow version is printed using tf.__version__.
  2. CUDA Version:

    • We use os.popen to execute the nvidia-smi command and retrieve the CUDA driver version.
  3. cuDNN Version:

    • There's no direct Python command to check the cuDNN version. You'll need to manually verify the version in your cuDNN installation directory.
  4. Environment Variables:

    • We use os.environ.get() to display the values of the CUDA_PATH and Path environment variables.
  5. DLL Path:

    • This part provides guidance on ensuring the CUDA bin directory (containing cudart64_101.dll) is in your system's Path.
  6. Reinstall TensorFlow:

    • This section is commented out but provides the commands to uninstall and reinstall TensorFlow with a specific version.
  7. Restart:

    • A reminder to restart your system or Python environment after making changes to environment variables or reinstalling software.

Remember:

  • Replace <compatible_version> with the appropriate TensorFlow version compatible with your CUDA and cuDNN setup.
  • This code provides a starting point. You might need to adapt it based on your specific operating system and environment.
  • If you're still facing issues, provide the information requested at the end of the original prompt for more tailored assistance.

Additional Notes

  • Common Causes: The error often arises after system updates (like NVIDIA drivers), new software installations, or environment changes.
  • CUDA Toolkit vs. Driver: Remember, CUDA Toolkit (with cudart64_101.dll) is different from the NVIDIA graphics driver. Both need to be compatible.
  • TensorFlow Website: Always cross-reference compatibility information on the official TensorFlow website for your specific versions.
  • Conda Environments: If using conda, ensure the correct environment is activated and has the right TensorFlow and CUDA packages.
  • Virtual Environments: Similar to conda, virtual environments (like venv) need the correct packages installed within them.
  • 32-bit vs. 64-bit: The '64' in 'cudart64_101.dll' implies a 64-bit library. Ensure your Python and TensorFlow installations are also 64-bit.
  • System Reboot: Sometimes a full system restart is necessary for environment variable changes to take effect properly.
  • Clean Installation: If all else fails, consider a clean installation of CUDA, cuDNN, and TensorFlow, following the official guides closely.
  • Community Forums: Online forums like Stack Overflow and the TensorFlow forum are valuable resources for finding solutions to similar issues.
  • Error Message Variations: Be aware that the error message might slightly vary (e.g., different DLL name) depending on the specific CUDA version.
  • Troubleshooting is Iterative: Resolving this error often involves trying different solutions and carefully checking compatibility at each step.

Summary

This error indicates a mismatch between your TensorFlow, CUDA, and cuDNN versions. Here's a summary of how to troubleshoot:

Step Description Action
1. Verify TensorFlow Installation Ensure you have the GPU version of TensorFlow installed. Run pip list | grep tensorflow. It should show tensorflow-gpu.
2. Check CUDA Version Compatibility The error message indicates you need CUDA version 10.1. Confirm your installed CUDA version matches using the NVIDIA Control Panel or by running nvidia-smi.
3. cuDNN Installation and Compatibility Verify cuDNN is installed and its version is compatible with your CUDA and TensorFlow versions. Check for compatible versions on the NVIDIA website.
4. Environment Variables and Paths Ensure CUDA_PATH and Path environment variables include directories containing CUDA binaries and libraries. Update environment variables if necessary.
5. DLL Path If cudart64_101.dll exists in your CUDA directory but isn't found, add its location to the Path environment variable. Update the Path environment variable.
6. Reinstall TensorFlow (if necessary) If compatibility issues persist, reinstall TensorFlow with a version matching your CUDA and cuDNN setup. Run pip uninstall tensorflow-gpu followed by pip install tensorflow-gpu==<compatible_version>.
7. Restart After making changes, restart your system or Python environment. Restart to apply changes.

If the issue persists, provide the following information for further assistance:

  • Operating System
  • TensorFlow version
  • CUDA version
  • cuDNN version
  • TensorFlow installation method (pip, conda, source)

Conclusion

By addressing potential conflicts between TensorFlow, CUDA, and cuDNN, you can overcome the "Could not load dynamic library 'cudart64_101.dll'" error. Remember to verify installations, check compatibility, configure environment variables, and restart your system when necessary. If problems persist, providing detailed system and installation information will help in diagnosing and resolving the issue effectively.

References

  • Could not load dynamic library 'cudart64_101.dll' · Issue #40804 ... Could not load dynamic library 'cudart64_101.dll' · Issue #40804 ... | System information Windows 10 Laptop: Asus gl553vw TensorFlow installed with pip tensorflow-gpu 2.2.0 Python 3.8.3 CUDA version 10.1 (update2) cudnn-10.1-windows10-x64-v7.6.5.32 Nvidia GTX 960M I w...
  • Python Tensorflow-GPU: "Successfully opened dynamic libraray ... Python Tensorflow-GPU: "Successfully opened dynamic libraray ... | Apr 24, 2020 ... Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation · 6 · Tensorflow not recognising cudart64_101.dll · 1.
  • cudart64_101.dll not found, but I have CUDA installed · Issue #36111 cudart64_101.dll not found, but I have CUDA installed · Issue #36111 | Please make sure that this is a build/installation issue. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:...
  • Rasa train (rasa 1.9.x | TensorFlow 2) on GPU? - Rasa Open Source ... Rasa train (rasa 1.9.x | TensorFlow 2) on GPU? - Rasa Open Source ... | Version: dev-CC Hi all, Is there anyone successfully trained a bot/model using Rasa 1.9.X on GPU? I was using Rasa 1.7 with gpu and training was fast and straight forward. But we recently switched to Rasa 1.9 to be able using DIET classifier improving entity extraction. From performance (accuracy) aspect, DIET classifier is great but quite slow and always running on CPUs. We did a deep dive in the codes and there was/is nothing preventing DIET from GPU, even it seems it tries to load data on G...
  • Installation problems DeepSpeech inference - DeepSpeech ... Installation problems DeepSpeech inference - DeepSpeech ... | I’m trying to run DeepSpeech on my Linux Mint 20. My Python version: Python 3.8.2, Procesor: AMD Ryzen 3 3200G. I’d like to run tensorflow CPU only for now because I don’t have GPU with CUDA yet. So what I did is: pip3 install deepspeech Then downloaded mic_vad_streaming And after that I ran: pip3 install -r requirements.txt Had some error with webrtcvad~=2.0.10, so I changed it to webrtcvad-wheels~=2.0.10. After that I did this: pip3 install tensorflow` And finally with models from d...
  • Computer to speed up deeplabcut analysis - Image Analysis - Image ... Computer to speed up deeplabcut analysis - Image Analysis - Image ... | I am interested to buy a computer to run deeplabcut analysis in a faster way (for now, I am using CPU combined with colab). However, I am affraid of some incompatability between GPU and deeplabcut. I am planning to buy GPU (A4000, 16GB/32GB, 6.144 CUDA nuclei per card, 192 colors Tensor, 16 GB GDDR6 and 416 GB/s, 19.20 Tflops single precision and 153,4 Tflops Tensorflops) and the processor (AMD Ryzen 3900X de 3,8 GHz). Any suggestion, It will be wellcome. Thanks in advance. Best Julia
  • Message "Some layers from the model were not used" - Beginners ... Message "Some layers from the model were not used" - Beginners ... | Hi, I have a local Python 3.8 conda environment with tensorflow and transformers installed with pip (because conda does not install transformers with Python 3.8) But I keep getting warning messages like “Some layers from the model checkpoint at (model-name) were not used when initializing (…)” Even running the first simple example from the quick tour page generates 2 of these warning (although slightly different) as shown below Code: from transformers import pipeline classifier = pipeline('...
  • Dlerror: cudnn64_7.dll not found - Usage & Issues - Image.sc Forum Dlerror: cudnn64_7.dll not found - Usage & Issues - Image.sc Forum | I’m attempting to install the current DLC with GPU support in a Windows Anaconda environment. Based on the instructions in the DLC Releases page on Github https://github.com/DeepLabCut/DeepLabCut/releases I installed DLC using: pip install “deeplabcut[gui]” (I am eager to use this installation method since it incorporates the correct versions of wxPython and TensorFlow.) Prior to installing DLC, I installed: CUDA 10.1 (cuda_10.1.105_418.96_win10.exe) Anaconda cudnn (conda install cudnn) T...
  • LOADING ir FILES - Intel Community LOADING ir FILES - Intel Community | I am using unet provided by intel and successfully used it for BRATS but when ever i change the dataset this error occurs at the stage where i am running inference using IR files Data batch channel count (4) does not match filter input channel count (1).       (decathlon) E:\code3\unet-master\2D>pyt...

Were You Able to Follow the Instructions?

😍Love it!
😊Yes
😐Meh-gical
😞No
🤮Clickbait