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Accelerate training and validation of neural networks using the CPU and GPUs.

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Tensorflow on M1 Macbook Pro, error when model fit executes

It doesn't matter if I install miniforge or mamba, directly or through brew, when I try to fit the sample model from https://developer.apple.com/metal/tensorflow-plugin/, even with a simple sequential model, I always get this error. Is there any workaround on this? I'll appreciate any help, thanks! 2022-12-10 11:18:19.941623: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-12-10 11:18:20.427283: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled. 2022-12-10 11:18:21.222950: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x28edf1f90 2022-12-10 11:18:21.223003: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x28edf1f90 2022-12-10 11:18:21.363366: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x28edf1f90 2022-12-10 11:18:21.364757: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x28edf1f90 2022-12-10 11:18:21.388739: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x28edf1f90 2022-12-10 11:18:21.388757: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x28edf1f90 NotFoundError Traceback (most recent call last) Cell In[25], line 2 1 model = create_model() ----> 2 history = model.fit(Xf_train, yf_train, epochs=3, batch_size=64); File /opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File /opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 50 try: 51 ctx.ensure_initialized() ---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, 53 inputs, attrs, num_outputs) 54 except core._NotOkStatusException as e: 55 if name is not None: NotFoundError: Graph execution error: Detected at node 'StatefulPartitionedCall_4' defined at (most recent call last): File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/ipykernel_launcher.py", line 17, in app.launch_new_instance() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/traitlets/config/application.py", line 992, in launch_instance app.start() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/ipykernel/kernelapp.py", line 711, in start self.io_loop.start() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/tornado/platform/asyncio.py", line 215, in start self.asyncio_loop.run_forever() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/asyncio/base_events.py", line 603, in run_forever self._run_once() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/asyncio/base_events.py", line 1899, in _run_once handle._run() ... File "/var/folders/f9/bp40pn0d401d974fy48dxm8h0000gn/T/ipykernel_63636/3393788193.py", line 2, in <module> history = model.fit(Xf_train, yf_train, epochs=3, batch_size=64); File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1650, in fit tmp_logs = self.train_function(iterator) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1249, in train_function return step_function(self, iterator) ...... File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1222, in run_step outputs = model.train_step(data) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1027, in train_step self.optimizer.minimize(loss, self.trainable_variables, tape=tape) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 527, in minimize self.apply_gradients(grads_and_vars) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1140, in apply_gradients return super().apply_gradients(grads_and_vars, name=name) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 634, in apply_gradients iteration = self._internal_apply_gradients(grads_and_vars) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1166, in _internal_apply_gradients return tf.__internal__.distribute.interim.maybe_merge_call( File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1216, in _distributed_apply_gradients_fn distribution.extended.update( File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1211, in apply_grad_to_update_var return self._update_step_xla(grad, var, id(self._var_key(var))) Node: 'StatefulPartitionedCall_4' could not find registered platform with id: 0x28edf1f90 [[{{node StatefulPartitionedCall_4}}]] [Op:__inference_train_function_1241]
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by ppobar.
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CoreML encrypt exceptions in App Store

I have an app in the App Store that has been running fine. Starting yesterday it began throwing an exception during initialization in the App Store and during development: "[0x000...***] during in one-time initialization function for my_model_name at /ViewController.swift:84" Thread 2: EXC_BREAKPOINT (code=1, =***) [coreml] Could not create persistent key blob for ----*** : error=Error Domain=com.apple.CoreML Code=8 "Fetching decryption key from server failed." UserInfo=NSLocalizedDescription=Fetching decryption key from server failed., NSUnderlyingError=0x2822c6a60 Error Domain=CKErrorDomain Code=6 "CKInternalErrorDomain: 2022" UserInfo=NSDebugDescription=CKInternalErrorDomain: 2022, RequestUID=----, NSLocalizedDescription=Request failed with http status code 503, CKErrorDescription=Request failed with http status code 503, CKRetryAfter=21, NSUnderlyingError=0x2822c6ac0 Error Domain=CKInternalErrorDomain Code=2022 "Request failed with http status code 503" UserInfo=CKRetryAfter=21, CKHTTPStatus=503, CKErrorDescription=Request failed with http status code 503, RequestUID=----, NSLocalizedDescription=Request failed with http status code 503, CKHTTPStatus=503 ... NSLocalizedDescription=Request failed with http status code 503, CKHTTPStatus=503 ... Help! Anyone know what is going on? ... Also, when I try to generate a new key. I get this: I'm the only admin developer on my account. I tried following its directions by signing out and back in with my Apple ID in preferences. Restarted XCode and still can't generate a new key, but still received the same error.
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by Glenn007.
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Create ML

Hey! I have a question. I try to add some pictures in Create ML and it tells me that I'm missing something. What do I need to add? (I mention that in that folder there are pictures + a .json with all the pictures) If you have any recommendations.
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by Fooorest.
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ML ANE Model unloaded on first call to predict

Hi, I am trying to take advantage of my device ANE. I have created a model from torch using coremltools and adapted it until xcode model performance preview indicates it will run on my device ANE. But when i profile my integration into my app, i can see from the com.apple.ane logs the model has been loaded on device: Timestamp Type Process Category Message 00:00.905.087 Debug MLBench (11135) client doLoadModel:options:qos:error:: model[0x2804500c0] : success=1 : progamHandle=10 000 241 581 886: intermediateBufferHandle=10 000 242 143 532 : queueDepth=32 :err= but when i call predict on my model, the ANE is unloaded and the prediction run on CPU: Timestamp Type Process Category Message 00:00.996.015 Debug MLBench (11135) client doUnloadModel:options:qos:error:: model[0x2804500c0]=_ANEModel: { modelURL=file:///var/mobile/Containers/Data/Application/0A9F356B-B8C7-4B86-90A5-6812EF48CC94/tmp/math_custom_trans_decoder_seg_0DB63A47-E84E-4887-A606-BC9986B2C662.mlmodelc/ : key={"isegment":0,"inputs":{"extras":{"shape":[2,1,1,1,1]},"memory":{"shape":[128,5,1,1,1]},"proj_key_seg_in":{"shape":[128,39,1,1,1]},"state_in_k":{"shape":[32,1,1,20,2]},"tgt":{"shape":[5,1,1,1,1]},"state_in_v":{"shape":[32,1,1,20,2]},"pos_enc":{"shape":[128,1,1,1,1]}},"outputs":{"attn_seg":{"shape":[1,5,1,4,1]},"state_out_v":{"shape":[32,2,1,20,2]},"output":{"shape":[292,5,1,1,1]},"state_out_k":{"shape":[32,2,1,20,2]},"extras_tmp":{"shape":[2,1,1,1,1]},"proj_key_seg_in_tmp":{"shape":[128,39,1,1,1]},"attn":{"shape":[1,1,1,5,2]},"proj_key_seg":{"shape":[128,1,1,20,1]}}} : string_id=0x70ac000000015257 : program=_ANEProgramForEvaluation: { programHandle=10000241581886 : intermediateBufferHandle=10000242143532 : queueDepth=32 } : state=3 : programHandle=10000241581886 : intermediateBufferHandle=10000242143532 : queueDepth=32 : attr=... : perfStatsMask=0}  i dont see any obvious error messages in com.apple.ane, com.apple.coreml or com.apple.espresso. where/what should i look for to understand what is going on? and in particular why the ANE model was unloaded? Thank you
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by gsigms.
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Dynamic coreml model inference is significantly slower than static model

devices: iphone 11 config: configuration.computeUnits = .all let myModel = try! myCoremlModel(configuration: configuration).model Set the Range for Each Dimension: input_shape= ct.Shape(shape=(1,3,ct.RangeDim(lower_bound=128, upper_bound=384, default=256),ct.RangeDim(lower_bound=128, upper_bound=384, default=256))) inference time as table(average of 100 runs) The default size inference for dynamic models is the same as for static models, but 128128 and 384384 hundreds of times slow than fixed-size models. Is this normal? Is there any good solution? model init time is too long load model time about 2 minutes, Is there a way to speed it up? For example, load from the cache? Can converted mlparkage speed up the loading time?
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by Cel-b.
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Example code for MPS graph not working

I'm trying to run sample code for MPS graph, which I got here: https://developer.apple.com/documentation/metalperformanceshadersgraph/adding_custom_functions_to_a_shader_graph And it's not working. Builds successfully, but after you press train (play button), program fails right after first training iteration with errors like these: -[MTLDebugCommandBuffer lockPurgeableObjects]:2103: failed assertion `MTLResource 0x600001693940 (label: (null)), referenced in cmd buffer 0x124015800 (label: (null)) is in volatile or empty purgeable state at commit' -[MTLDebugCommandBuffer lockPurgeableObjects]:2103: failed assertion `MTLResource 0x600001693940 (label: (null)), referenced in cmd buffer 0x124015800 (label: (null)) is in volatile or empty purgeable state at commit' It is failing on commandBuffer.commit() in runTrainingIterationBatch() method. Its like something already committed operation (I've checked and yeah, command buffer is already commited). But why such thing in EXAMPLE CODE? I've tried to wrap commit operation with command buffer status check and it is helping to not fail, but program works wrong overall and not calculating loss well. Everything is getting worse because documentation for MPS Graph is empty! It's contains only class and method names without any description D; My env: Xcode 13.4.1 (13F100) macOS 12.4 MacBook Pro (m1 pro) 14' 2021 16gb Tried to build on iPhone 12 Pro Max / iOS 15.5 and to Mac catalyst application. Got same error everywhere
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by abesmon.
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PyTorch Training on iOS Devices

Is there any way to train PyTorch models directly on-device on an iPhone via the GPU? PyTorch Mobile docs seems to be completely focused on inference only, as do the iOS app examples (https://github.com/pytorch/ios-demo-app). I did find this article about using MPS backend on Macs (https://developer.apple.com/metal/pytorch/), but not sure if this is at all viable for iOS devices. There's also this prototype article about using iOS GPU for PyTorch mobile (https://pytorch.org/tutorials/prototype/ios_gpu_workflow.html), but it too seems to be focused on inference only. We are attempting to train a large language model on the iPhone 14 and we would like to a) discard intermediate activations and recompute them, and b) manage memory directly to write some intermediate activations to the filesystem and later read them back. We suspect that converting a PyTorch model to CoreML format and using CoreML for training would prevent us from making these low-level modifications, but PyTorch might have the APIs necessary for this. If there's any examples/pointers that anyone can link to that would be great.
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by anish-shanbhag.
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ValueError: Shape of the RGB/BGR image output, 'colorOutput', must be of kind (1, 3, H, W), i.e., first two dimensions must be (1, 3), instead they are: (1, 2)

I specifically give the model the shape of (1, 3, 1024, 1024), but for some reason, CoreML thinks it's 2 channels instead of 3. The pytorch model is based on this - LINK The "local.pth" model to be specific. My CoreML conversion code is attached below. #from networks.drn_seg import DRNSeg import coremltools as ct import coremltools.proto.FeatureTypes_pb2 as ft import io from PIL import Image from torchvision import transforms import math import torch import torch.nn as nn from networks.drn import drn_c_26 import torchvision from torchvision.io import read_image def fill_up_weights(up): w = up.weight.data f = math.ceil(w.size(2) / 2) c = (2 * f - 1 - f % 2) / (2. * f) for i in range(w.size(2)): for j in range(w.size(3)): w[0, 0, i, j] = \ (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) for c in range(1, w.size(0)): w[c, 0, :, :] = w[0, 0, :, :] class DRNSeg(nn.Module): def __init__(self): super(DRNSeg, self).__init__() classes=2 pretrained_drn=None pretrained_model=None use_torch_up=False model = drn_c_26(pretrained=pretrained_drn) self.base = nn.Sequential(*list(model.children())[:-2]) if pretrained_model: self.load_pretrained(pretrained_model) self.seg = nn.Conv2d(model.out_dim, classes, kernel_size=1, bias=True) m = self.seg n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) m.bias.data.zero_() if use_torch_up: self.up = nn.UpsamplingBilinear2d(scale_factor=8) else: up = nn.ConvTranspose2d(classes, classes, 16, stride=8, padding=4, output_padding=0, groups=classes, bias=False) fill_up_weights(up) up.weight.requires_grad = False self.up = up def forward(self, x): x = self.base(x) x = self.seg(x) y = self.up(x) return y def optim_parameters(self, memo=None): for param in self.base.parameters(): yield param for param in self.seg.parameters(): yield param def load_pretrained(self, pretrained_model): print("loading the pretrained drn model from %s" % pretrained_model) state_dict = torch.load(pretrained_model, map_location='cpu') if hasattr(state_dict, '_metadata'): del state_dict._metadata # filter out unnecessary keys pretrained_dict = state_dict['model'] pretrained_dict = {k[5:]: v for k, v in pretrained_dict.items() if k.split('.')[0] == 'base'} # load the pretrained state dict self.base.load_state_dict(pretrained_dict) #img = read_image('test.jpg') model_path = ("modelphotoshop.pth") device = 'cpu' model_flow = DRNSeg() model_flow.load_state_dict(torch.load(model_path, map_location=device)) model_flow.to(device) model_flow.eval() example_input = torch.randn([1,3,1024,1024]) traced_model = torch.jit.trace(model_flow, example_input) traced_model.save("modelphotoshop.pt") shape = (1,3, 1024, 1024) mlmodel = ct.convert(traced_model, convert_to="mlprogram", inputs=[ct.ImageType(name="colorImage", shape=shape, color_layout=ct.colorlayout.RGB,)], outputs=[ct.ImageType(name="colorOutput", color_layout=ct.colorlayout.RGB,)], ) mlmodel.save("Model.mlmodel")
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by lugookeke.
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Make program use Neural Engine?

Hello, Is there any way to make a program run on the Neural Engine? I have a compiled program (not Python/tensorflow/etc) that I would like to speed up; right now, it runs on the GPU but I was told by the developer it doesn’t use the neural engine. I’m running predictions, not training models - I have read that the ANE is especially good for this purpose. Is there a way to speed up my predictions here with the ANE? Noah
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by nlittman1.
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Connect NVIDIA GPU to Macbook M1 Pro

I have a 16-inch M1 pro with 32 GB ram and an Nvidia A6000 GPU. What is the best way to connect the GPU so I can use it with VS code? Thanks.
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by Zohim.
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Object detection for dice not working

Hi from France ! I'm trying to create a model for dice detection. I've take about 100 photos of dice on the same side (1 point). Are-my bounding boxes good ? should I take the whole dice ? I launched the trainning, it seems to work well : Then in the Evaluation tab, the values seems not great but not bad : I/U 84% Varied I/U 44% The validation scope is very low : In the preview tab, no matter what image I give to it, I have no detection What am I missing ? What should I improve ?
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by Cyril42.
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Issues converting tensorflow model with coremltools

First off, I'm mainly a wet-lab biologist, and Python is not my strong suit, so sorry if I seem a little clueless here. Anyways, I am having trouble converting a pretrained keras model (.h5 format) with coremltools. My code to try and convert the model: DL_model = (path to .h5 file) model_converted = ct.convert(DL_model, source = "tensorflow") and it throws an error: ValueError: Unknown metric function: binary_recall. Please ensure this object is passed to the custom_objects argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details. I assume I need to pass some sort of custom_objects = {"custom_obj": custom_obj} argument in ct.convert, but I don't know how. I tried: custom_objects = {"binary_recall": binary_recall}) but that caused NameError: name 'binary_recall' is not defined Can anyone give me some help here? It would be nice to speed up this model by converting it to Apple's format; I work with huge files and cutting down data processing time is important for me. Thanks!! Noah
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by nlittman1.
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Why Python native on M1 Max is greatly slower than Python on old Intel i5?

I just got my new MacBook Pro with M1 Max chip and am setting up Python. I've tried several combinational settings to test speed - now I'm quite confused. First put my questions here: Why python run natively on M1 Max is greatly (~100%) slower than on my old MacBook Pro 2016 with Intel i5? On M1 Max, why there isn't significant speed difference between native run (by miniforge) and run via Rosetta (by anaconda) - which is supposed to be slower ~20%? On M1 Max and native run, why there isn't significant speed difference between conda installed Numpy and TensorFlow installed Numpy - which is supposed to be faster? On M1 Max, why run in PyCharm IDE is constantly slower ~20% than run from terminal, which doesn't happen on my old Intel Mac. Evidence supporting my questions is as follows: Here are the settings I've tried: 1. Python installed by Miniforge-arm64, so that python is natively run on M1 Max Chip. (Check from Activity Monitor, Kind of python process is Apple). Anaconda.: Then python is run via Rosseta. (Check from Activity Monitor, Kind of python process is Intel). 2. Numpy installed by conda install numpy: numpy from original conda-forge channel, or pre-installed with anaconda. Apple-TensorFlow: with python installed by miniforge, I directly install tensorflow, and numpy will also be installed. It's said that, numpy installed in this way is optimized for Apple M1 and will be faster. Here is the installation commands: conda install -c apple tensorflow-deps python -m pip install tensorflow-macos python -m pip install tensorflow-metal 3. Run from Terminal. PyCharm (Apple Silicon version). Here is the test code: import time import numpy as np np.random.seed(42) a = np.random.uniform(size=(300, 300)) runtimes = 10 timecosts = [] for _ in range(runtimes): s_time = time.time() for i in range(100): a += 1 np.linalg.svd(a) timecosts.append(time.time() - s_time) print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s') and here are the results: +-----------------------------------+-----------------------+--------------------+ | Python installed by (run on)→ | Miniforge (native M1) | Anaconda (Rosseta) | +----------------------+------------+------------+----------+----------+---------+ | Numpy installed by ↓ | Run from → | Terminal | PyCharm | Terminal | PyCharm | +----------------------+------------+------------+----------+----------+---------+ | Apple Tensorflow | 4.19151 | 4.86248 | / | / | +-----------------------------------+------------+----------+----------+---------+ | conda install numpy | 4.29386 | 4.98370 | 4.10029 | 4.99271 | +-----------------------------------+------------+----------+----------+---------+ This is quite slow. For comparison, run the same code on my old MacBook Pro 2016 with i5 chip - it costs 2.39917s. another post reports that run with M1 chip (not Pro or Max), miniforge+conda_installed_numpy is 2.53214s, and miniforge+apple_tensorflow_numpy is 1.00613s. you may also try on it your own. Here is the CPU information details: My old i5: $ sysctl -a | grep -e brand_string -e cpu.core_count machdep.cpu.brand_string: Intel(R) Core(TM) i5-6360U CPU @ 2.00GHz machdep.cpu.core_count: 2 My new M1 Max: % sysctl -a | grep -e brand_string -e cpu.core_count machdep.cpu.brand_string: Apple M1 Max machdep.cpu.core_count: 10 I follow instructions strictly from tutorials - but why would all these happen? Is it because of my installation flaws, or because of M1 Max chip? Since my work relies heavily on local runs, local speed is very important to me. Any suggestions to possible solution, or any data points on your own device would be greatly appreciated :)
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by graphitump.
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Error when trying to use Tensorflow mixed-precision on M1 Pro

I tried training my model on my M1 Pro using Tensorflow's mixed-precision, hoping it will boost the performance, but I got an error: .../mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm:289:0: error: 'mps.select' op failed to verify that all of {true_value, false_value, result} have same element type .../mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm:289:0: note: see current operation: %5 = "mps.select"(%4, %3, %2) : (tensor<1xi1>, tensor<1xf16>, tensor<1xf32>) -> tensor<1xf16>
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by naor-navina.
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Xcode Coreml

The data format of MLMultiArray content is float32. How to convert the data format of content to int
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by LIttt.
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Mac Studio Machine Learning script failing to use the GPU

I'm using tensorflow in python and I'm exploring the hyper parameters of a machine learning models for my dataset. My workflow involves two python scripts. Script 1 iterates through a suite of ML hyper parameters calling a second python script that fits resulting ML model to the data. Script 2 is command line implementation of the ML model setup and fitting to the data. On my Macbook pro (M1 chip) everything works fine. On my new Mac Studio, script 2 hangs after a number of calls because it fails to run on the GPU. I cannot avoid this behaviour. If I kill the hung job the first script continues to call script 2 until it hangs again after another 10ish calls of script 2. What going on? Problem with the GPU of Mac studio? Any suggestion to test would be appreciated. R
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by rmatear.
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