Checkout this answer for listing available GPUs.

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

You can also use CUDA to list the current device and, if necessary, set the device.

import torch

print(torch.cuda.is_available())
print(torch.cuda.current_device())

Hi @Gabriel-2005
Welcome to Microsoft Q&A platform and thanks for posting your query here.

Databricks is utilizing the specified instance type, but it's important to note that the g4dn.2xlarge instance offers 32 GiB of system memory, while its GPU only provides 16 GiB. A portion of this GPU memory is reserved for the operating system, which explains why your error shows an available memory of 14.76 GiB.

To resolve this issue, I suggest choosing a different instance type that satisfies the minimum GPU memory needs for your workload. Since AWS frequently introduces new instances and updates existing ones, I won't suggest a specific type. However, you can use https://instances.vantage.sh/ to filter and find suitable instances based on your requirements.

For reference: https://learn.microsoft.com/en-us/azure/databricks/machine-learning/train-model/huggingface/fine-tune-model#troubleshoot-common-cuda-errors

Hope this helps. Do let us know if you any further queries.

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