Testing ML Runtime GPU Setup

You can use the following simple examples to test whether the new ML Runtime is able to leverage GPUs as expected.

  1. Go to a project that is using the ML Runtimes NVIDIA GPU edition and click Open Workbench.
  2. Launch a new session with GPUs.
  3. Run the following command in the workbench command prompt to verify that the driver was installed correctly:
    ! /usr/bin/nvidia-smi
  4. Use any of the following code samples to confirm that the new engine works with common deep learning libraries.


!pip3 install torch==1.4.0
from torch import cuda
assert cuda.is_available()
assert cuda.device_count() > 0


!pip3 install tensorflow-gpu==2.1.0
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())


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