Solve ImportError: Could not find cudart64_90.dll. TensorFlow requires that this DLL be installed in

Table of Contents

Solve ImportError: Could not find cudart64_90.dll. TensorFlow requires that this DLL be installed in

Step 1: Check the installation of CUDA Toolkit

Step 2: Check the configuration of CUDA environment variables

Step 3: Check the installation of cuDNN

Step 4: Reinstall TensorFlow

in conclusion

Solving ImportError: Could not find cudart64_90.dll. TensorFlow requires that this DLL be installed in

When we use TensorFlow to train a deep learning model, we may encounter an error named ??ImportError: Could not find cudart64_90.dll??. This error usually occurs when the file ??cudart64_90.dll?? is missing from our system. ??cudart64_90.dll?? is part of the CUDA Toolkit and is required by TensorFlow to communicate with the GPU. In this article, we will explain how to solve this problem and ensure that our TensorFlow runs smoothly.

Step 1: Check the installation of CUDA Toolkit

First, we need to check whether the CUDA Toolkit is already installed in our system. CUDA Toolkit is a development toolkit for GPU acceleration provided by NVIDIA. We can check the installation of CUDA Toolkit by following these steps:

  1. Open a Command Prompt or Terminal window.
  2. Run the following command:
plaintextCopy codenvcc --version

If the CUDA Toolkit is installed, the CUDA version number is displayed. If it is not installed or the version is lower, please follow the instructions in NVIDIA official documentation to download and install the CUDA Toolkit version suitable for your system.

Step 2: Check the configuration of CUDA environment variables

After installing the CUDA Toolkit, we also need to ensure that the system has correctly configured the CUDA environment variables. Here are the steps to configure CUDA environment variables:

  1. Open the system’s environment variable configuration page.
  • On Windows, you can do this by right-clicking the “Computer” (or “This PC”) icon, selecting “Properties”, then clicking “Advanced System Settings”, and then clicking the “Environment Variables” button.
  • On Linux or macOS, you can edit the environment variable configuration file by running the sudo nano /etc/environment command in the terminal.
  1. Add the following variables to the system environment variables:
  • ??CUDA_PATH??: Points to the installation path of CUDA Toolkit. For example, ??C:\Program Files\\
    VIDIA GPU Computing Toolkit\CUDA\v10.0?    ?.
  • ??CUDA_HOME??: The same value as the ??CUDA_PATH?? variable.
  • ??PATH??: Add the CUDA Toolkit’s ??bin?? directory to the PATH variable. For example, on Windows, you can add ??%CUDA_PATH%\bin?? to the end of the PATH variable.
  1. Save and close the environment variable configuration file.
  • On Windows, click the “OK” button.
  • On Linux or macOS, press the Ctrl + X keys, then the Y keys to save changes.
  1. Restart the computer for the environment variables to take effect.

Step 3: Check the installation of cuDNN

cuDNN is a GPU acceleration library for deep neural networks, and TensorFlow relies on cuDNN to achieve GPU acceleration. We need to ensure that the cuDNN version corresponding to the CUDA version is correctly installed in the system. Here are the steps to check cuDNN installation:

  1. Log in to the NVIDIA developer website (https://developer.nvidia.com/) and download the cuDNN corresponding to your CUDA version.
  2. Unzip the downloaded cuDNN compressed package.
  3. Copy the cuDNN library files (including ??cudnn64_7.dll?? and ??cudnn.h??, etc.) to the corresponding folder in the installation directory of CUDA Toolkit middle. For example, copy ??cudnn64_7.dll?? to ??C:\Program Files\\
    VIDIA GPU Computing Toolkit\CUDA\v10.0\bin?    ?.

Step 4: Reinstall TensorFlow

After completing the above steps, we can try to reinstall TensorFlow to ensure that the problem is resolved.

  1. Open a Command Prompt or Terminal window.
  2. Create a new Python virtual environment (optional but recommended).
  3. Run the following command in the virtual environment to reinstall TensorFlow:
plaintextCopy codepip install --upgrade tensorflow

This will install the latest version of TensorFlow.

Conclusion

By following the above steps, we should be able to resolve the ??ImportError: Could not find cudart64_90.dll?? error and ensure that TensorFlow can run properly. Please note that it is important to ensure that the version of CUDA Toolkit and cuDNN is compatible with the TensorFlow version to avoid additional compatibility issues. I hope this article can help you solve this problem and successfully train the deep learning model. If you have any questions or encounter other issues, please feel free to leave a comment below.

After solving the ??ImportError: Could not find cudart64_90.dll?? error, we can use TensorFlow to train the deep learning model. Below is a simple example code that demonstrates how to use TensorFlow to train a basic image classification model. First, we need to import the necessary libraries and modules:

pythonCopy codeimport tensorflow as tf
from tensorflow.keras import datasets, layers, models

Next, we need to load the training dataset. In this example, we will use the MNIST handwritten digits dataset:

pythonCopy code(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

Then, we need to preprocess the data. In this example, we will normalize the image and convert the labels to one-hot encoding:

pythonCopy codetrain_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))
train_images, test_images = train_images / 255.0, test_images / 255.0
train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=10)
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=10)

Next, we can build a simple convolutional neural network model:

pythonCopy codemodel = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

Then we need to compile the model and define the optimizer and loss function:

pythonCopy codemodel.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

Next, we can train the model using the training dataset:

pythonCopy codemodel.fit(train_images, train_labels, epochs=5, batch_size=64)

Finally, we can evaluate the model’s performance using the test dataset:

pythonCopy codetest_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)

This is a simple example code for training an image classification model using TensorFlow. You can modify and expand according to actual needs, such as adding more convolutional layers or fully connected layers, adjusting hyperparameters, etc., to achieve better performance and accuracy.

??cudart64_90.dll?? is a dynamic link library file of NVIDIA CUDA Runtime Library. CUDA is a parallel computing platform and programming model provided by NVIDIA, allowing developers to perform parallel computing on NVIDIA GPUs. And ??cudart64_90.dll?? is the runtime library file corresponding to CUDA 9.0 version. The CUDA runtime library contains a series of functions and tools for managing and executing parallel computing tasks on the GPU. It provides functions such as GPU memory management, thread scheduling, and parallel computing operations, allowing developers to write parallel computing programs more conveniently. When using CUDA to develop a GPU-accelerated application, you need to link the CUDA runtime library with the application. And ??cudart64_90.dll?? is one of the library files, used to support the runtime environment of CUDA version 9.0. When running a program that requires CUDA support, the system will automatically load and call the function provided by ??cudart64_90.dll?? to realize the parallel computing function of the GPU. It should be noted that ??cudart64_90.dll?? is a CUDA runtime library file for 64-bit operating systems. For 32-bit operating systems, the corresponding library file names may be different. When using deep learning frameworks such as TensorFlow, if you encounter the ??ImportError: Could not find 'cudart64_90.dll'?? error, it is usually because the CUDA runtime library file is not configured correctly or is not in the in the system path. The solution to this problem is usually to ensure that the CUDA runtime library files exist in the correct path and that the path in the system environment variables is configured correctly.

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