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Don't use GPU for training

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This commit is contained in:
Nicolas Mowen 2025-10-31 12:26:18 -06:00
parent 057d44d6a9
commit b4ba2ce7c4
3 changed files with 0 additions and 3 deletions
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1
docker/tensorrt/requirements-amd64.txt
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@ -13,7 +13,6 @@ nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64' nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64' nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64' nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
tensorflow==2.19.*; platform_machine == 'x86_64'
onnx==1.16.*; platform_machine == 'x86_64' onnx==1.16.*; platform_machine == 'x86_64'
onnxruntime-gpu==1.22.*; platform_machine == 'x86_64' onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
protobuf==3.20.3; platform_machine == 'x86_64' protobuf==3.20.3; platform_machine == 'x86_64'

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docs/docs/configuration/custom_classification/object_classification.md
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@ -10,7 +10,6 @@ Object classification allows you to train a custom MobileNetV2 classification mo
Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate. Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer. Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
## Classes ## Classes

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docs/docs/configuration/custom_classification/state_classification.md
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@ -10,7 +10,6 @@ State classification allows you to train a custom MobileNetV2 classification mod
State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate. State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer. Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
## Classes ## Classes