Test the CUDA Runtime
You can use the following simple examples to test whether the new CUDA ML Runtime is able to leverage GPUs as expected.
- Go to a project that is using the CUDA ML Runtime and click New Session.
- Launch a new session with GPUs.
-
Run the following command in the workbench command prompt to verify that the driver
was installed correctly:
! /usr/bin/nvidia-smi
-
Use any of the following code samples to confirm that the new engine works with
common deep learning libraries.
- PyTorch
-
!pip3 install torch from torch import cuda assert cuda.is_available() assert cuda.device_count() > 0 print(cuda.get_device_name(cuda.current_device()))
- Tensorflow
-
!pip3 install tensorflow-gpu==2.1.0 from tensorflow.python.client import device_lib assert 'GPU' in str(device_lib.list_local_devices()) device_lib.list_local_devices()
- Keras
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!pip3 install keras from keras import backend assert len(backend.tensorflow_backend._get_available_gpus()) > 0 print(backend.tensorflow_backend._get_available_gpus())