Hello， I'm currently facing an issue while working with MetalPerformanceShaders in testing a Python project. Code: https://github.com/Thinklab- SJTU/Crossformer Error Description: /AppleInternal/Library/BuildRoots/4e1473ee-9f66-11ee-8daf-cedaeb4cabe2/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Types/MPSNDArray.mm:126: failed assertion `[MPSNDArrayDescriptor sliceDimension:withSubrange:] error: the range subRange.start + subRange.length does not fit in dimension[2] (15)' I've tried updating macOS to the latest version. Also, I've attempted running the code with MPS_NO_DEVICE_CHECK=1 to bypass device checks. But errors happened again. I'm seeking insights or solutions to this problem and If anyone has encountered a similar issue or has suggestions on how to troubleshoot and resolve this assertion failure, I would greatly appreciate your help. Project Details: Language: Python Environment: PyCharm CE Relevant Technologies: MetalPerformanceShaders, MPSNDArray Thank you for your time and assistance!

it appears that the Metal Debugging interface does not support this method, at least the function hashing algorithm does not have a pattern for it in the symbol dictionary as presented. Where do we get updated C- libraries and functions that sync with the things that are presented in the Demo Kits and Samples that Apple puts in the user domain? Why does this stuff get out into the wild insufficiently tested? It seems thet the demo kits made available to users should be included in the test domain used to verify new code releases. I came from a development environment where the 6 month release cycle involved automated execution of the test suite before it went beta or anywhere else.

Hi there, I've met a problem that in my working project build settings. There is no Metal Compiler Build Setting, but it works well in my demo project. And I'm certain I've move the shader files(.metal) into the bundle. How could I resolve this problem? XCode Version 15.0 13.6.1 (22G313)

Hello, I have been following the excellent/informative "Metal for Machine Learning" from WWDC19 to learn how to do on device training (I have a specific use case for this) and it is all working really well using the MPSNNGraph. However, I would like to call my own metal compute/render function/pipeline to transform the inference result before calculating the loss, does anyone know if this possible and what would this look like in code? Please see my current code below, at the comment I need to call an intermediate compute/render function to transform the inference result image before passing to the MPSNNForwardLossNode. let rgbImageNode = MPSNNImageNode(handle: nil) let inferGraph = makeInferenceGraph() let reshape = MPSNNReshapeNode(source: inferGraph.resultImage, resultWidth: 64, resultHeight: 64, resultFeatureChannels: 4) //Need to call render or compute pipeline to post process in the inference result image let rgbLoss = MPSNNForwardLossNode(source:reshape.resultImage, labels:rgbImageNode, lossDescriptor:lossDescriptor) let initGrad = MPSNNInitialGradientNode(source:rgbLoss.resultImage) let gradNodes = initGrad.trainingGraph(withSourceGradient:nil, nodeHandler:nil) guard let trainGraph = MPSNNGraph(device: device, resultImage: gradNodes![0].resultImage, resultImageIsNeeded: true) else{ fatalError("Unable to get training graph.") } Thanks

I'm brand new to Metal. I've googled, but can't get the right answer to come up. (Thanks, unhelpful ChatGPT generated answers polluting everything, but I digress...) Ultimately, I'm trying to figure out how to use Metal to render 3D DICOM data on iOS specifically. If you're not familiar with DICOM, let's just say I've got a whole stack of CT image slices. Or to get really simple, I've got a cube of voxel values with differing values at each voxel coordinate. Where do I even start in Metal to render something like this? (I was trying to get the VTK toolkit compiled for iOS, which uses OpenGL, but that appears to be a dead end. And besides, Metal is supposed to be so much better.) Thanks for any tips/leads/suggestions/general pointers.

What best to pass to the options parameter of: MTLDevice.makeBuffer(length:options:) MTLDevice.makeBuffer(bytes:length:options:) MTLDevice.makeBuffer(bytesNoCopy:length:options:deallocator:) Basically I'm looking for a "plain English" explanation of MTLResourceOptions doc page.