I've been trying to implement picture-in-picture support with the SwiftUI VideoPlayer view that AVKit offers. By default, it seems, there's no option for picture-in-picture with the system playback controls, so I tried building a custom control as an overlay. Unfortunately, I couldn't get anything working. While AVPictureInPictureController.isPictureInPictureSupported() consistently returns true, the .isPictureInPicturePossible property always returns false, even though I've enabled the required background capability and configured the shared audio session properly. As a result, the .startPictureInPicture() method doesn't actually do anything. Strangely, none of the AVPictureInPictureControllerDelegate methods are ever called, even though my custom delegate instance is indeed properly configured. How can I implement picture-in-picture support with the SwiftUI VideoPlayer view? I'd prefer not to have to use AVPlayerViewController.

Hello! I’m having an issue with retrieving the trained weights from MLCLSTMLayer in ML Compute when training on a GPU. I maintain references to the input-weights, hidden-weights, and biases tensors and use the following code to extract the data post-training: extension MLCTensor { func dataArray<Scalar>(as _: Scalar.Type) throws -> [Scalar] where Scalar: Numeric { let count = self.descriptor.shape.reduce(into: 1) { (result, value) in result *= value } var array = [Scalar](repeating: 0, count: count) self.synchronizeData() // This *should* copy the latest data from the GPU to memory that’s accessible by the CPU _ = try array.withUnsafeMutableBytes { (pointer) in guard let data = self.data else { throw DataError.uninitialized // A custom error that I declare elsewhere } data.copyBytes(to: pointer) } return array } } The issue is that when I call dataArray(as:) on a weights or biases tensor for an LSTM layer that has been trained on a GPU, the values that it retrieves are the same as they were before training began. For instance, if I initialize the biases all to 0 and then train the LSTM layer on a GPU, the biases values seemingly remain 0 post-training, even though the reported loss values decrease as you would expect. This issue does not occur when training an LSTM layer on a CPU, and it also does not occur when training a fully-connected layer on a GPU. Since both types of layers work properly on a CPU but only MLCFullyConnectedLayer works properly on a GPU, it seems that the issue is a bug in ML Compute’s GPU implementation of MLCLSTMLayer specifically. For reference, I’m testing my code on M1 Max. Am I doing something wrong, or is this an actual bug that I should report in Feedback Assistant?