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.
- Go to a project that is using the ML Runtimes NVIDIA GPU edition and click Open Workbench.
- 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==1.4.0
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())