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

!pip3 install keras
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0
print(backend.tensorflow_backend._get_available_gpus())