QBoard » Artificial Intelligence & ML » AI and ML - Tensorflow » Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation
User Dashboard

Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation

Back To Topics
Tags : tensorflow python keras python-3.x tensorflow2.0
  • 67 3
    I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:

    W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found​

    Is this bad? How do I fix the error?
      September 7, 2020 3:18 PM IST
    0
  • 461
    Tensorflow 2.1+
    What's going on?
    With the new Tensorflow 2.1 release, the default tensorflow pip package contains both CPU and GPU versions of TF. In previous TF versions, not finding the CUDA libraries would emit an error and raise an exception, while now the library dynamically searches for the correct CUDA version and, if it doesn't find it, emits the warning (The W in the beginning stands for warnings, errors have an E (or F for fatal errors) and falls back to CPU-only mode. In fact, this is also written in the log as an info message right after the warning (do note that if you have a higher minimum log level that the default, you might not see info messages). The full log is (emphasis mine):
    2020-09-07 03:24:44.554767: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found

2020-09-07 03:24:44.554964: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.​
    Should I worry? How do I fix it?
    If you don't have a CUDA-enabled GPU on your machine, or if you don't care about not having GPU acceleration, no need to worry. If, on the other hand, you installed tensorflow and wanted GPU acceleration, check your CUDA installation (TF 2.1 requires CUDA 10.1, not 10.2 or 10.0).
    If you just want to get rid of the warning, you can adapt TF's logging level to suppress warnings, but that might be overkill, as it will silence all warnings.
    Tensorflow 1.X or 2.0:
    Your CUDA setup is broken, ensure you have the correct version installed.
    This post was edited by Viaan Prakash at September 7, 2020 3:26 PM IST
      September 7, 2020 3:25 PM IST
    0
  • 319 1

    To install the prerequisites for GPU support in TensorFlow 2.1:

    1. Install your latest GPU drivers.
    2. Install CUDA 10.1.
      • If the CUDA installer reports "you are installing an older driver version", you may wish to choose a custom installation and deselect some components. Indeed, note that software bundled with CUDA including GeForce Experience, PhysX, a Display Driver, and Visual Studio integration are not required by TensorFlow.
      • Also note that TensorFlow requires a specific version of the CUDA Toolkit unless you build from source; for TensorFlow 2.1 and 2.2, this is currently version 10.1.
    3. Install cuDNN.
      1. Download cuDNN v7.6.4 for CUDA 10.1. This will require you to sign up to the NVIDIA Developer Program.
      2. Unzip to a suitable location and add the bin directory to your PATH.
    4. Install tensorflow by pip install tensorflow.
    5. You may need to restart your PC.
     
      September 7, 2020 3:28 PM IST
    0
  • 32 1

    I solved this another way. First of all I installed cuda 10.1 toolkit from this link

    Where i selected installer type(exe(local)) and installed 10.1 in custom mode means (without visual studio integration, NVIDIA PhysX because previously I installed CUDA 10.2 so required dependencies were installed automatically)

    After installation, From the Following Path (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin) , in my case, I copied 'cudart64_101.dll' file and pasted in (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin).

    Then importing Tensorflow worked smoothly.

      September 7, 2020 3:28 PM IST
    0
  • 129 8

    In conda environment, this is what solved my problem (I was missing cudart64-100.dll:

    1. Downloaded it from dll-files.com/CUDART64_100.DLL

    2. Put it in my conda environment at C:\Users\<user>\Anaconda3\envs\<env name>\Library\bin

    That's all it took! You can double check if it's working

    import tensorflow as tf
tf.config.experimental.list_physical_devices('GPU')
    This post was edited by Rakesh Racharla at September 7, 2020 3:35 PM IST
      September 7, 2020 3:31 PM IST
    0