How to run azure custom vision model on a local development machine with gpu capability

Bassi, Ankur 0 Reputation points
2023-03-13T16:57:51.5333333+00:00

I exported the compact model and successfully ran it on my local environment. Although I enabled GPU environment by downloading the necessary libraries and I'm receiving a message confirming it, I have not observed any noticeable improvement in performance. My GPU is Quadro P1000 and the FPS remains at around 1, both before and after enabling the GPU. Any suggestion will be of great help. For more information please see the log below. Thanks

`
2023-03-13 12:55:36.556930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0

2023-03-13 12:55:36.558002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:

2023-03-13 12:55:36.559061: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0

2023-03-13 12:55:36.559860: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N

2023-03-13 12:55:36.560814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2983 MB memory) -> physical GPU (device: 0, name: Quadro P1000, pci bus id: 0000:01:00.0, compute capability: 6.1)

2023-03-13 12:55:36.562112: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
`

Azure AI Custom Vision
Azure AI Custom Vision
An Azure artificial intelligence service and end-to-end platform for applying computer vision to specific domains.
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