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I'm experiencing extremely long loading times when using the MLModel API to load a converted Core ML model. The loading process hangs indefinitely. When changing compute_units to ct.ComputeUnit.CPU_AND_GPU, the model loading works and is fast.
Stack Trace
If applicable, please paste the complete stack trace.
To Reproduce
I built a simple super-resolution model in PyTorch, traced it using torch.jit.trace and then converted to mlpackage. There is no fancy operations, and all operations are common in deep learning.
The loading works only when setting compute_units=ct.ComputeUnit.CPU_AND_GPU
System environment (please complete the following information):
coremltools version: 7.2
OS: Sonoma 14.5
Any other relevant version information: Pytorch==2.0.1
Additional context
I also tried running a performance report in Xcode. The profiling takes forever if setting Compute Unit to ALL with real physical devices: macOS 14.5 (m1 pro chip), iOS 17.5 (iPhone 13 Pro) and iOS 18.0 (iPhone 16 Pro). Running performance report works only when setting compute unit to either 'CPU only' or 'CPU and GPU'.
The text was updated successfully, but these errors were encountered:
🐞Describing the bug
I'm experiencing extremely long loading times when using the MLModel API to load a converted Core ML model. The loading process hangs indefinitely. When changing compute_units to ct.ComputeUnit.CPU_AND_GPU, the model loading works and is fast.
Stack Trace
To Reproduce
torch.jit.trace
and then converted tomlpackage
. There is no fancy operations, and all operations are common in deep learning.The loading works only when setting
compute_units=ct.ComputeUnit.CPU_AND_GPU
System environment (please complete the following information):
Pytorch==2.0.1
Additional context
ALL
with real physical devices: macOS 14.5 (m1 pro chip), iOS 17.5 (iPhone 13 Pro) and iOS 18.0 (iPhone 16 Pro). Running performance report works only when setting compute unit to either 'CPU only' or 'CPU and GPU'.The text was updated successfully, but these errors were encountered: