The tensorflow-macos repo has been archived and the last commit redirects users to the plugin page; however, this page still instructs users to install the now-archived fork. Are these instructions still up to date? Also, what is the plan long-term for Metal/M1 acceleration in Tensorflow—will the necessary changes eventually be upstreamed, if they haven't already?

Good day people! I'm currently working on my master thesis in media informatics. I'd really appreciate to discuss my topic with you guys, so I may get some interesting ideas or new information. The goal is to implement an app, specifically designed for places like museums where the envrionment isn't perfect for AR tracking. (Darkness, no network connection, maybe exhibits made out of glass...) Therefore, i'd like to develop a neuronal network for the new ipad pro that takes rgb-d data to predict a pose estimation in a scene for an object, so that it matches the real world object perfectly. This placed object will be a perfect 3d model replica of the real object. (hand modeled or scanned and revised) This should allow me to place AR Content precisely over the real world object, even in difficult lightlings and stuff. Maybe it will improve occlusion, too. I can imagine that the neuronal network may also detect structures, edges and semantic coherences better than the usual approach. My first thought was to work with CoreML, Metal, maybe Vision and ARKit. I will also try out XCode for the first time. Maybe you guys have interesting ideas for improvement or can guide me a little bit, since i fell a bit lost at the moment. Would you use rather point clouds or the raw depth buffer to train the model? Would you also train with edge filter images and stuff? Why or why not? Thanks in advance, it would mean the world to me! Kind regards, Miri :-)

Is it possible to do any of the following: Export a model created using MetalPerformanceShadersGraph to a CoreML file; Failing 1., save a trained MetalPerformanceShadersGraph model in any other way for deployment; Import a CoreML model and use it as a part of a MetalPerformanceShadersGraph model. Thanks!

Greetings. I was shopping around for an external GPU and Machine Learning + GPU solution for my Mac. Are there any suggestions? It looks like Keras 2.4 has dropped the multi backed support which is worrying. I'm trying to make sure I make a purchase that will fly with what I have. I'm using a Mac Mini (2018) with 64GB RAM. I have multiple Thunderbolt 3 ports. This is an Intel chipset machine, not an M1. Is this feasible?

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?

Is it possible to use SNAudioFileAnalyzer with live HLS(m3u8) stream? Maybe we need to extract somehow audio from it? And Can we use SNAudioFileAnalyzer with real remote url? Or we can use it only with files in file system?

Hi everyone, I found that the performance of GPU is not good as I expected (as slow as a turtle), I wanna switch from GPU to CPU. but mlcompute module cannot be found, so wired. The same code ran on colab and my computer (jupyter lab) take 156s vs 40 minutes per epoch, respectively. I only used a small dataset (a few thousands of data points), and each epoch only have 20 baches. I am so disappointing and it seems like the "powerful" GPU is a joke. I am using 12.0.1 macOS and the version of tensorflow-macos is 2.6.0 Can anyone tell me why this happens?

wwdc20-10673 briefly shows how to visualize optical flow generated by VNGenerateOpticalFlowRequest and sample code is available through the developer app. But how can we build the OpticalFlowVisualizer.ci.metallib file from the CI-kernel code provided as OpticalFlowVisualizer.cikernel?

After collecting data I wanted to create an ML model I have done this before but for some reason I was getting the error: error: Couldn't lookup symbols:  CreateML.MLDataTable.init(contentsOf: Foundation.URL, options: CreateML.MLDataTable.ParsingOptions) throws -> CreateML.MLDataTable  CreateML.MLDataTable.init(contentsOf: Foundation.URL, options: CreateML.MLDataTable.ParsingOptions) throws -> CreateML.MLDataTable So I went to test a working example that was created by apple using this link: https://developer.apple.com/documentation/createml/creating_a_model_from_tabular_data After running this test with no data changed, I still get the same error logged. I don't know if I'm doing something wrong. Any advice would be greatly appreciated.

Can I run inference on the new MacBook Pro with M1 Chips (Apple Silicon) using Keras Models (sometimes PyTorch). These would be computer vision models, some might have custom loss functions or metrics and would have been trained on lets say, Google Colab. If I can perform inference, how do I do that? Also, will the Neural Engines help while performing inference or will it boost training if I have to train on the Mac?

Hi all, I've spent some time experimenting with the BNNS (Accelerate) LSTM-related APIs lately and despite a distinct lack of documentation (even though the headers have quite a few) a got most things to a point where I think I know what's going on and I get the expected results. However, one thing I have not been able to do is to get this working if inputSize != hiddenSize. I am currently only concerned with a simple unidirectional LSTM with a single layer but none of my permutations of gate "iw_desc" matrices with various 2D layouts and reordering input-size/hidden-size made any difference, ultimately BNNSDirectApplyLSTMBatchTrainingCaching always returns -1 as an indication of error. Any help would be greatly appreciated. PS: The bnns.h framework header file claims that "When a parameter is invalid or an internal error occurs, an error message will be logged. Some combinations of parameters may not be supported. In that case, an info message will be logged.", and yet, I've not been able to find any such messages logged to NSLog() or stderr or Console. Is there a magic environment variable that I need to set to get more verbose logging?

I'm using Vision to conduct some OCR from a live camera feed. I've setup my VNRecognizeTextRequests as follows: let request = VNRecognizeTextRequest(completionHandler: recognizeTextCompletionHandler) request.recognitionLevel = .accurate request.usesLanguageCorrection = false And I handle the results as follows: guard let observations = request.results as? [VNRecognizedTextObservation] else { return } for observation in observations { if let recognizedText = observation.topCandidates(1).first { guard recognizedText.confidence >= self.confidenceLimit, // set to 0.5 let foundText = validateRegexPattern(text: recognizedText.string, regexPattern: self.regexPattern), let foundDecimal = Double(foundText) else { continue } } This is actually working great and yielding very accurate results, but the confidence values I'm receiving from the results are generally either 0.5 or 1.0, and rarely 0.3. I find these to be pretty nonsensical confidence values and I'm wondering if this is the intended result or some sort of bug. Conversely, using recognitionLevel = .fast yields more realistic and varied confidence values, but much less accurate results overall (even though fast is recommended for OCR from a live camera feed, I've had significantly better results using the accurate recognition level, which is why I've been using the accurate recognition level)

as above. I found that M1 Chip has extremely bad performance on training RNNs (158s vs 6h). Could anyone know why the M1 Pro chip is so unfriendly for RNNs? How can I only use CPU to run my code as mlcompute package cannot be recognized somehow.

How to convert an existing mlmodel to mlprogram without source code? It has been verified that coremltools.convert does not support coremltools.models.MLModel as source.

I'm trying to apply TensorFlow-macOS to accelerate the fine-tune of hugging face transformer program on my Radeon Pro 5300M. However, there shows that "Cannot assign a device for operation", would it be possible to tell me why and how to solve this problem? issues: Version of TensorFlow-macOS is 2.5.0; I've tried to update into 2.6.0, and it've worked. However, it's extremely slower than the 2.5.0 one. It would be a better way to fix 2.5.0 according to the 2.6.0, but I don't know how to do.

this is the message i got as i tried to install tensorflow on my mac M1ship ERROR: tensorflow_addons_macos-0.1a2-cp38-cp38-macosx_11_0_arm64.whl is not a supported wheel on this platform. i have followed all the instruction as presented on the website can somebody help please.?

how to fix this????

I just created my custom hand poses classification ml model and I don't know how to implement it in any app..So where can I get the demo app that they showed us in the video?

Hi, I have seen this video: https://developer.apple.com/videos/play/wwdc2021/10041/ and in my project i am trying to draw detected barcodes. I am using Vision framework and i have the barcode position in boundingBox parameter, but i dont understand cgrect of that parameter. I am programming in objective c and i don't see resources, and for more complication i have not an image, i am capturing barcodes from video camera sesion. for parts: 1-how can i draw detected barcode like in the video (from an image). 2-how can i draw detected barcode in capturesession. I have used VNImageRectForNormalizedRect to pass from normalized to pixel, but the result is not correct. thank you very much.

In a section of my app I would like to recommend restaurants to users based on certain parameters. Some parameters have a higher weighting than others. In this WWDC Video a very similar app is made. Here if a user likes a dish a value of 1.0 is assigned to those specific keywords (that apple to the dish) with a value of -1.0 to all other keywords that don't apply to that dish. For my app if the user has ordered I then apply a value of 1.0 (to the keywords that apply) but if a user has just expressed interest (but not ordered) then can I apply a value of 0.5 (and -0.5) instead, would the model adapt to this?