Comments (2)
HI @windvibra, sorry for my late response. Can you check if TensorFlow is built using CUDA? You can verify if TF is using GPU via:
import tensorflow as tf
print(tf.test.is_gpu_available()) # Should print True if TF can detect GPU
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) # Should print the number of GPUs available
Additionally, when you trained the model, did you check if the GPU memory was allocated to the model?
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I may have an idea. TensorFlow has stopped GPU support from TF 2.11 on native-Windows platform. More details can be found here.
You'd have to either use Linux or WSL 2 on Windows to use this repo and TF with GPU support.
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Related Issues (13)
- Predict wrong class HOT 5
- 'SGD' object has no attribute 'get_updates' HOT 4
- Nucleus HOT 2
- Error when i try to train the model HOT 1
- SGD optimizer HOT 1
- keras.optimizers.legacy.SGD() not working HOT 1
- Run with GPU HOT 4
- Getting weird predictions. HOT 4
- Using other optimizers HOT 1
- GOOGLE COLAB PYTHON VERSION HOT 5
- Train Fail HOT 1
- train False HOT 1
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from mask-rcnn_tf2.14.0.