Ok false alarm. While I had background app refresh enabled at the app level, it wasn't enabled at the global level. As soon as I did that, it's all working as expected. whew.

Some more information on when I see the error being triggered. It happens pretty consistently after the second fragment interval time has elapsed. So if I set the fragment interval time to be 60 seconds, it almost always happens after the 2 minute mark. So it seems like something is going wrong when the second fragment is being written to the movie file. Hope this provides some insights. Happy to file a radar if its a bug but want to confirm that it is in the first place and that I'm not doing something wrong with my setup (see code above). Thanks

Here's the console output referenced above. Selected video format: <AVCaptureDeviceFormat: 0x283edddd0 'vide'/'420f' 1280x 720, { 1- 60 fps}, photo dims:{1280x720,4224x2376}, fov:64.717, supports vis (max strength:Low), max zoom:123.75 (upscales @3.00), AF System:2, ISO:18.0-1728.0, SS:0.000024-1.000000, supports wide color, supports depth> Selected depth format: Optional('dpth'/'hdep' 320x 180, { 1- 30 fps}, photo dims:{}, fov:64.717) no depth data at time 4660461719679 received depth data 4660461721647 no depth data at time 4660461721931 received depth data 4660461722090 no depth data at time 4660461722241 received depth data 4660461722574 no depth data at time 4660461733834 received depth data 4660461754095 no depth data at time 4660484422372 received depth data 4660484427828 no depth data at time 4660484551473 no depth data at time 4660484557872 received depth data 4660484560137 received depth data 4660484587264 received depth data 4660484625637 received depth data 4660484723590 received depth data 4660484890588 received depth data 4660484894113 received depth data 4660485028919 received depth data 4660485065300 received depth data 4660485108980 received depth data 4660485117934 received depth data 4660485311522 received depth data 4660485374234 received depth data 4660485409137 received depth data 4660485587031 received depth data 4660485717468 received depth data 4660485774764 received depth data 4660485965976 received depth data 4660486050028 received depth data 4660486050960 received depth data 4660486141976 no depth data at time 4660486148485 received depth data 4660486255161 received depth data 4660486255353 received depth data 4660486255451 received depth data 4660486255518 received depth data 4660486255590 received depth data 4660486255661 received depth data 4660486323853 received depth data 4660486343897 received depth data 4660486346269 received depth data 4660486586520 received depth data 4660486739514 received depth data 4660486845554 received depth data 4660486925583 received depth data 4660487127930 received depth data 4660487324394 received depth data 4660487377842 received depth data 4660487413297 received depth data 4660487533840 received depth data 4660487539837 received depth data 4660487539986 received depth data 4660487606854 received depth data 4660487730023 received depth data 4660487731258 received depth data 4660487731424 received depth data 4660487943556 received depth data 4660487945158 received depth data 4660488042570 received depth data 4660488089371 received depth data 4660488089543 received depth data 4660488161973 no depth data at time 4660488378041 no video data at time 4660488378151 no depth data at time 4660488795023 no video data at time 4660488795134 no depth data at time 4660488925324 no video data at time 4660488925465 no depth data at time 4660488931173 no video data at time 4660488931281 no depth data at time 4660489036864 no video data at time 4660489036974 no depth data at time 4660489098026 no video data at time 4660489098133 no depth data at time 4660489177999 no video data at time 4660489178109 no depth data at time 4660489181387 no video data at time 4660489181546 no depth data at time 4660489239588 no video data at time 4660489239690 no depth data at time 4660489455661 no video data at time 4660489455773 no depth data at time 4660489565211 no video data at time 4660489565315 no depth data at time 4660489683372 no video data at time 4660489683486 no depth data at time 4660489912688 no video data at time 4660489912809 no depth data at time 4660489925852 no video data at time 4660489926002 no depth data at time 4660490016836 no video data at time 4660490016941 no depth data at time 4660490017016 no video data at time 4660490017061 ...

One learning - my project is very similar to Apple's WWDC sample available for download here: https://developer.apple.com/documentation/avfoundation/additional_data_capture/capturing_depth_using_the_lidar_camera. Only difference is that instead of using a metal texture, I'm using a AVCaptureVideoPreviewLayer to show the camera view. After much comparison/experimentation, I'm finding that not adding my preview layer is allowing the video and depth data to flow reliably. When I add it, it flows correctly about 50% of the time. This appears to be the most likely issue - any clues as to why this can happen?

Thanks @Engineer for taking a look. As suggested we used a FLOAT32 compute_precision and it didn't help meaning that we still saw jitteriness on device inference versus Pytorch inference on a server. ** One interesting fact ** - we took the Core ML model (that was originally converted from PyTorch using coremltools) and ran it in Python using Core ML tools' libraries and didn't encounter the jitteriness! So our best guess at this stage is that it has something to do with running the CoreML model on the device itself (tested on iPhone 14/iOS 17 and M2 Mac OS Sonoma 14.5 as an iPad app). Do you think the above noted warning copied below has something to do with it? Validation failure: Invalid Pool kernel width (9), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (13), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (9), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (13), must be [1-8] or 20. This neural network model does not have a parameter for requested key 'precisionRecallCurves'. Note: only updatable neural network models can provide parameter values and these values are only accessible in the context of an MLUpdateTask completion or progress handler.