I consistently receive corrupted results from tf.signal.fft3d() when it is within a function that has a @tf.function decorator. The results are all zero (0.) for entries after a certain x index (see image). Surprisingly, the issue depends on the matrix size. For example, (1023, 1023, 287) works but (1023, 1023, 575) does not. The issue is problematic because it occurs silently and not for all matrix sizes, i.e. can easily slip through tests. The error occurs only when tensorflow-metal is installed. The Tensorflow version is 2.16.1. My hardware is a Macbook Pro M3 Max with 40 GPU cores, 128 GB RAM running MacOS Sonoma version 14.5 (23F79). A Python environment to reproduce the bug can be created as follows: conda create --name tfmetalbug python=3.11.9 conda activate tfmetalbug pip install tensorflow tensorflow-metal conda install matplotlib The following code reproduces the issue: import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Wrap fft3d with tf.function @tf.function def fft3d_wrapper_function(x): return tf.signal.fft3d(x) # Generate a 3D image img = tf.random.normal(shape=(1023, 1023, 575), stddev=1., dtype=float) # generate random 3d image img = tf.dtypes.cast(img, tf.complex64) # convert to complex values # Compute the 3D FFT img_fft = fft3d_wrapper_function(img) # Visualize the 3D FFT plt.imshow(np.real(img_fft)[:, img_fft.shape[1]//2+10, :], cmap="gray", vmin=-0.001, vmax=0.001) plt.savefig("fft3d_wrapper_function.png") For me, removing the @tf.function decorator has resolved the issue.

I'm trying to convert a TensorFlow model that I didn't create and know approximately nothing about to CoreML so that I can use it in some functional tests. I can't tell you much about the model, but you can read about it on the blog from the team that created it: https://research.google/blog/improving-mobile-app-accessibility-with-icon-detection/ I can't convert this model to a TensorFlow Lite model because it uses a few full TensorFlow operations (which I could work around) and it exceeds the 4-tensor output limit (which I can't, AFAIK). So instead, I'm trying to convert the model to CoreML so that I can run it on-device. The issue I'm running into is that every approach fails in different ways. If I load the model with tf.saved_model.load and pass that as the first parameter to the convert call, it says NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <tensorflow.python.trackable.autotrackable.AutoTrackable object at 0x30d90c250> If I pass model.signatures['serving_default'] as the first parameter to convert, I get NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got ConcreteFunction [...a page or two of info about the function here...] If I try to wrap it in a Keras layer using the instructions provided in the converter, it fails because a sequential model can't have multiple outputs. If I try to use a tf.keras.layers.TFSMLayer to load the model, it fails because there are multiple tags, and there's no way to specify tags when constructing the layer. (It tells me that I need to add 'tags' to load the model, but if I do that, it tells me that tags isn't a valid parameter to the call.) If I load the model with tf.saved_model.load and specify a single tag, then re-save it in a different location with tf.saved_model.save to generate a new model with only a single tag, then do input_layer = tf.keras.Input(shape=(768, 768, 3), dtype="int8") layer = tf.keras.layers.TFSMLayer("./serve_model", call_endpoint='serving_default') outputs = layer(input_layer) model = tf.keras.Model(input_layer, outputs) I get AttributeError: 'Functional' object has no attribute '_get_save_spec' At one point, I also tried this: class LayerFromSavedModel(tf.keras.layers.Layer): def __init__(self): super(LayerFromSavedModel, self).__init__() self.vars = legacy_model.variables def call(self, inputs): return legacy_model.signatures['serving_default'](inputs) input = tf.keras.Input(shape=(3000, 3000, 3)) model = tf.keras.Model(input, LayerFromSavedModel()(input)) and saw a similar failure. I've run out of ideas here. Is there simply no support whatsoever in the converter for importing a TensorFlow 2 SavedModel into CoreML, or am I missing something fundamental?

followed instruction in https://developer.apple.com/metal/jax/ I got Successfully installed importlib-metadata-7.1.0 jax-0.4.28 jax-metal-0.0.7 jaxlib-0.4.28 opt-einsum-3.3.0 scipy-1.13.0 six-1.16.0 zipp-3.18.2 but the test failed python -c 'import jax; print(jax.numpy.arange(10))' Traceback (most recent call last): File "", line 1, in File "/Users/erivas/jax-metal/lib/python3.9/site-packages/jax/init.py", line 37, in import jax.core as _core File "/Users/erivas/jax-metal/lib/python3.9/site-packages/jax/core.py", line 18, in from jax._src.core import ( File "/Users/erivas/jax-metal/lib/python3.9/site-packages/jax/_src/core.py", line 39, in from jax._src import dtypes File "/Users/erivas/jax-metal/lib/python3.9/site-packages/jax/_src/dtypes.py", line 33, in from jax._src import config File "/Users/erivas/jax-metal/lib/python3.9/site-packages/jax/_src/config.py", line 27, in from jax._src import lib File "/Users/erivas/jax-metal/lib/python3.9/site-packages/jax/_src/lib/init.py", line 84, in cpu_feature_guard.check_cpu_features() RuntimeError: This version of jaxlib was built using AVX instructions, which your CPU and/or operating system do not support. You may be able work around this issue by building jaxlib from source.

Cannot assign a device for operation encoder/down1/downs_0/conv1/weight/Initializer/random_uniform/RandomUniform: Could not satisfy explicit device specification '' because the node {{colocation_node encoder/down1/downs_0/conv1/weight/Initializer/random_uniform/RandomUniform}} was colocated with a group of nodes that required incompatible device '/device:GPU:0'. All available devices [/job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0]. Colocation Debug Info: Colocation group had the following types and supported devices: Root Member(assigned_device_name_index_=-1 requested_device_name_='/device:GPU:0' assigned_device_name_='' resource_device_name_='/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[] Identity: GPU CPU Mul: GPU CPU AddV2: GPU CPU Sub: GPU CPU RandomUniform: GPU CPU Assign: CPU VariableV2: GPU CPU Const: GPU CPU

Regardless of the installation version combinations of tensorflow & metal (2.14, 2.15, 2.16), I find a metal/non-metal incompatibility for some layer types. For the GRU layer, for example, metal-trained weights (model.save_weights()/load_weights()) are not compatible with inference using the CPU. That is, train a model using metal, run inference using metal, then run inference again after uninstalling metal, and the results differ -- sometimes a night and day difference. This essentially eliminates the usefulness of tensorflow-metal for me. From my limited testing, models using other, simple combinations of layer types including Dense and LSTM do not show this problem. Just the GRU. And by "testing" I mean really simple models, like one GRU layer. Apple Framework Metal Team: You are doing very useful work, and I kindly ask, please address this bug :)

I noticed from the system requirements, TensorFlow only seems to support Python. Are there any plans to add JavaScript as TensorFlow has JS support? Thank you for your time...

I using a Macbook pro with an m2 pro chip. I was trying to work with TensorFlow but I encountered an illegal hardware instruction error. To resolve it I initiated the installation of a metal plugin which is throwing the following error. or semicolon (after version specifier) awscli>=1.16.100boto3>=1.9.100 ~~~~~~~~~~~^ Unable to locate awscli [end of output]

When fitting a CNN model, every second Epoch takes zero seconds and with OUT_OF_RANGE warnings. Im using structured folders of categorical images for training and validation. Here is the warning message that occurs after every second Epoch. The fitting looks like this... 37/37 ━━━━━━━━━━━━━━━━━━━━ 14s 337ms/step - accuracy: 0.5255 - loss: 1.0819 - val_accuracy: 0.2578 - val_loss: 2.4472 Epoch 4/20 37/37 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.5312 - loss: 1.1106 - val_accuracy: 0.1250 - val_loss: 3.0711 Epoch 5/20 2024-04-19 09:22:51.673909: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence [[{{node IteratorGetNext}}]] 2024-04-19 09:22:51.673928: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence [[{{node IteratorGetNext}}]] [[IteratorGetNext/_59]] 2024-04-19 09:22:51.673940: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 10431687783238222105 2024-04-19 09:22:51.673944: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 17360824274615977051 2024-04-19 09:22:51.673955: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 10732905483452597729 My setup is.. Tensor Flow Version: 2.16.1 Python 3.9.19 (main, Mar 21 2024, 12:07:41) [Clang 14.0.6 ] Pandas 2.2.2 Scikit-Learn 1.4.2 GPU is available My generator is.. train_generator = datagen.flow_from_directory( scalp_dir_train, # directory target_size=(256, 256),# all images found will be resized batch_size=32, class_mode='categorical' #subset='training' # Specify the subset as training ) n_samples = train_generator.samples # gets the number of samples validation_generator = datagen.flow_from_directory( scalp_dir_test, # directory path target_size=(256, 256), batch_size=32, class_mode='categorical' #subset='validation' # Specifying the subset as validation Here is my model. early_stopping_monitor = EarlyStopping(patience = 10,restore_best_weights=True) from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers import SGD optimizer = Adam(learning_rate=0.01) model = Sequential() model.add(Conv2D(128, (3, 3), activation='relu',padding='same', input_shape=(256, 256, 3))) model.add(BatchNormalization()) model.add(MaxPooling2D((2, 2))) model.add(Dropout(0.3)) model.add(Conv2D(64, (3, 3),padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D((2, 2))) model.add(Dropout(0.3)) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Dense(256, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.3)) model.add(Dense(4, activation='softmax')) # Defined by the number of classes model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) Here is the fit... history=model.fit( train_generator, steps_per_epoch=37, epochs=20, validation_data=validation_generator, validation_steps=12, callbacks=[early_stopping_monitor] #verbose=2 )

Hi, I just noticed that using the jax.numpy.insert() function returns an incorrect result (zero-padding the array) when compiled with jax.jit. When not jitted, the results are correct Config: M1 Pro Macbook Pro 2021 python 3.12.3 ; jax-metal 0.0.6 ; jax 0.4.26 ; jaxlib 0.4.23 MWE: import jax import jax.numpy as jnp x = jnp.arange(20).reshape(5, 4) print(f"{x=}\n") def return_arr_with_ins(arr, ins): return jnp.insert(arr, 2, ins, axis=1) x2 = return_arr_with_ins(x, 99) print(f"{x2=}\n") return_arr_with_ins_jit = jax.jit(return_arr_with_ins) x3 = return_arr_with_ins_jit(x, 99) print(f"{x3=}\n") Output: x2 (computed with the non-jitted function) is correct; x3 just has zero-padding instead of a column of 99 x=Array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]], dtype=int32) x2=Array([[ 0, 1, 99, 2, 3], [ 4, 5, 99, 6, 7], [ 8, 9, 99, 10, 11], [12, 13, 99, 14, 15], [16, 17, 99, 18, 19]], dtype=int32) x3=Array([[ 0, 1, 2, 3, 0], [ 4, 5, 6, 7, 0], [ 8, 9, 10, 11, 0], [12, 13, 14, 15, 0], [16, 17, 18, 19, 0]], dtype=int32) The same code run on a non-metal machine gives the correct results: x=Array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]], dtype=int32) x2=Array([[ 0, 1, 99, 2, 3], [ 4, 5, 99, 6, 7], [ 8, 9, 99, 10, 11], [12, 13, 99, 14, 15], [16, 17, 99, 18, 19]], dtype=int32) x3=Array([[ 0, 1, 99, 2, 3], [ 4, 5, 99, 6, 7], [ 8, 9, 99, 10, 11], [12, 13, 99, 14, 15], [16, 17, 99, 18, 19]], dtype=int32) Not sure if this is the correct channel for bug reports, please feel free to let me know if there's a more appropriate place!

Hello, We all face issues with the latest tensorflow gpu. Incorrect result, errors etc... We all agreed to pay extra for the M1/2/3 so we could work on a professional grade computer but in the end we must use CPU. When will apple actually comment on that and provide updates. I totally understand these issues aren't fixed overnight and take some time, but i've never seen any apple dev answer saying that they understand and they're working on a fix. I've basically bought a Mac M3 Pro to be able to run on GPU some stuff without having to purchase a server and it's now useless. It's really frustrating.

(Copied from https://github.com/google/jax/issues/20835) I am attempting to use JAX on Metal (on a M1 Pro chip) to model discrete (count) data. I've installed the latest version jax-metal 0.0.6 using pip. The installation seems to have worked overall as I can perform basic Jax array operations on GPU. However, when I try to compute the (log-)PMFs/PDFs of random variables which are defined in terms of the (log-)Gamma function I get errors like the one below which seems to indicate that the lax.lgamma function is not supported under the hood on M1 metal. This is essential functionality for a wide class of probabilistic machine learning models. Note that following functions (among others) are broken as a result: jax.scipy.stats.binom.logpmf jax.scipy.stats.nbinom.logpmf jax.scipy.stats.poisson.logpmf jax.scipy.stats.dirichlet.logpdf jax.scipy.stats.beta.logpdf jax.scipy.stats.gamma.logpdf ... >>> jax.scipy.stats.binom.logpmf(1, n=2, p=0.5) jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/scipy/stats/binom.py", line 31, in logpmf gammaln(n + 1), File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/scipy/special.py", line 44, in gammaln return lax.lgamma(x) File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/lax/special.py", line 46, in lgamma return lgamma_p.bind(x) File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/core.py", line 422, in bind return self.bind_with_trace(find_top_trace(args), args, params) File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/core.py", line 425, in bind_with_trace out = trace.process_primitive(self, map(trace.full_raise, args), params) File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/core.py", line 913, in process_primitive return primitive.impl(*tracers, **params) File "/Users/ljb80/.virtualenvs/jax-metal/lib/python3.10/site-packages/jax/_src/dispatch.py", line 87, in apply_primitive outs = fun(*args) jaxlib.xla_extension.XlaRuntimeError: UNKNOWN: <stdin>:1:0: error: failed to legalize operation 'chlo.lgamma' <stdin>:1:0: note: see current operation: %0 = "chlo.lgamma"(%arg0) : (tensor<f32>) -> tensor<f32> System info (python version, jaxlib version, accelerator, etc.) jax: 0.4.26 jaxlib: 0.4.23 numpy: 1.26.4 python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:38:29) [Clang 13.0.1 ] jax.devices (1 total, 1 local): [METAL(id=0)] process_count: 1 platform: uname_result(system='Darwin', node='PHS027794', release='23.4.0', version='Darwin Kernel Version 23.4.0: Fri Mar 15 00:10:42 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T6000', machine='arm64')

Copying from https://github.com/google/jax/issues/20750: import jax import jax.numpy as jnp def test_func(x, y): return x, y def main(): # Print available JAX devices print("JAX devices:", jax.devices()) # Create two random matrices a = jnp.array([[1.0, 2.0], [3.0, 4.0]]) b = jnp.array([[5.0, 6.0], [7.0, 8.0]]) # Perform matrix multiplication c = jnp.dot(a, b) # Print the result print("Result of matrix multiplication:") print(c) # Compute the gradient of sum of c with respect to a grad_a = jax.grad(lambda a: jnp.sum(jnp.dot(a, b)))(a) print("Gradient with respect to a:") print(grad_a) rng = jax.random.PRNGKey(0) test_input = jax.random.normal(key=rng, shape=(5,5,5)) initial_state = jax.numpy.array(0.0) x, y = jax.lax.scan(test_func, initial_state, test_input) if __name__ == "__main__": main() Gets: Platform 'METAL' is experimental and not all JAX functionality may be correctly supported! 2024-04-15 18:22:28.994752: W pjrt_plugin/src/mps_client.cc:563] WARNING: JAX Apple GPU support is experimental and not all JAX functionality is correctly supported! Metal device set to: Apple M2 Pro systemMemory: 16.00 GB maxCacheSize: 5.33 GB JAX devices: [METAL(id=0)] Result of matrix multiplication: [[19. 22.] [43. 50.]] Gradient with respect to a: [[11. 15.] [11. 15.]] zsh: segmentation fault python JAXTest.py With more info from the debugger: Current thread 0x00000001fdd3bac0 (most recent call first): File "/Users/.../anaconda3/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py", line 1213 in __call__ My configuration is: jax-metal : 0.0.6 jax: 0.4.26 jaxlib: 0.4.23 numpy: 1.24.3 python: 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:49:36) [Clang 16.0.6 ] jax.devices (1 total, 1 local): [METAL(id=0)] process_count: 1 platform: uname_result(system='Darwin', root:xnu-10063.101.17~1/RELEASE_ARM64_T6020', machine='arm64') macOS 14.4.1 (23E224) Before in 3.9+0.0.3 etc it wasn't happening.

Hi, I have encountered to a segfault error when I called something via jax.lax.scan. A minimum failing example is pasted below: $ ipython Python 3.9.6 (default, Feb 3 2024, 15:58:27) Type 'copyright', 'credits' or 'license' for more information IPython 8.18.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: import jax In [2]: jax.__version__ Out[2]: '0.4.22' In [3]: import jaxlib In [4]: jaxlib.__version__ Out[4]: '0.4.22' In [6]: import jax.numpy as jnp In [7]: def f(carry, x): ...: return carry + x * x, x * x ...: ...: jax.lax.scan(f, jnp.zeros((), dtype=jnp.float32), jnp.arange(3, dtype=jnp.float32)) Platform 'METAL' is experimental and not all JAX functionality may be correctly supported! 2024-04-16 01:03:52.483015: W pjrt_plugin/src/mps_client.cc:563] WARNING: JAX Apple GPU support is experimental and not all JAX functionality is correctly supported! Metal device set to: Apple M3 Max systemMemory: 36.00 GB maxCacheSize: 13.50 GB zsh: segmentation fault ipython This might be related to the thread below: https://developer.apple.com/forums/thread/749080 Strangely, when we call it jax.lax.scan is a very important building block, so I would greatly appreciate if this can be resolved soon.

Hardware: 16" 2023 MBP M3 Pro OS: 14.4.1 Memory: 36 GB python version: 3.8.16 TF-Metal version: tensorflow-metal 1.0.1 installed via pip TF version: 2.13.0 Tensorflow-Metal starts pretty slow, approximately 10s/iteration and over the course of 36 iteration progressively slows down to over 120s/iteration. Info log prints out that TFLite is using XNNPack. Can't share the TFLite model but it is relatively shallow, small, and simple. Uninstalled TF-Metal, and installed tensorflow. Inference speed picks right up and is rock solid at 0.78s/iteration. What is going on??? **TLDR, TFLite inference speed: TF Metal = 120s/iteration TF = 0.78s/iteration**

Tried various how-tos on youtube and github. Have conda. Third step fails. conda install -c apple tensorflow-deps pip install tensorflow-macos pip install tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal I see a lot of fixes for Intel-based Mac. None for M3. HELP!?

I tried running inference with the 2B model from https://github.com/google-deepmind/gemma on my M2 MacBook Pro, but it segfaults during sampling: https://pastebin.com/KECyz60T Note: out of the box it will try to load bfloat16 weights, which will fail. To avoid this, I patched line 30 in gemma/params.py to explicitly cast to float32: param_state = jax.tree_util.tree_map(lambda p: jnp.array(p, jnp.float32), params)

Tensorflow metal was working on my Power Mac Mac M3 until yesterday. Then my code started freezing. I ran the test script from https://developer.apple.com/metal/tensorflow-plugin/ and it now crashes - this used to work fine, but all of a sudden it does not. The results are shown below. Has anyone seen anything like this? Could this be a hardware problem? MacBook-Pro-3: carl$ python mac_tensorflow_test.py Epoch 1/5 1/782 [..............................] - ETA: 51:53 - loss: 6.0044 - accuracy: 0.0312Error: command buffer exited with error status. The Metal Performance Shaders operations encoded on it may not have completed. Error: (null) Ignored (for causing prior/excessive GPU errors) (00000004:kIOGPUCommandBufferCallbackErrorSubmissionsIgnored) <AGXG15XFamilyCommandBuffer: 0x1172515e0> label = <none> device = <AGXG15SDevice: 0x1588e6000> name = Apple M3 Pro commandQueue = <AGXG15XFamilyCommandQueue: 0x17427e400> label = <none> device = <AGXG15SDevice: 0x1588e6000> name = Apple M3 Pro retainedReferences = 1 Error: command buffer exited with error status. The Metal Performance Shaders operations encoded on it may not have completed. Error: (null) Ignored (for causing prior/excessive GPU errors) (00000004:kIOGPUCommandBufferCallbackErrorSubmissionsIgnored) <AGXG15XFamilyCommandBuffer: 0x117257b40> label = <none> device = <AGXG15SDevice: 0x1588e6000> name = Apple M3 Pro commandQueue = <AGXG15XFamilyCommandQueue: 0x17427e400> label = <none> device = <AGXG15SDevice: 0x1588e6000> name = Apple M3 Pro retainedReferences = 1 Many more rows of similar printouts follow.

Hi i am trying to set up tensorflow-metal as instructed by https://developer.apple.com/metal/tensorflow-plugin/ when running line (python -m pip install tensorflow-metal) I get the following error: ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal According to the troubleshooting section: "Check that the Python version used in the environment is supported (Python 3.8, Python 3.9, Python 3.10)." My current version is Python 3.9.12. Any insight would be great!

Hi, I am looking for a routine to perform complex-valued linear algebra on the GPU in python for scientific programming, in particular quantum physics simulations. At the moment I am looking for a routine for complex-valued matrix multiplication. I found MLX has a routine for float matrix multiplication, but it does not directly work for complex-valued matrices. I figured a work-around by splitting the complex valued matrix into real and imaginary part and working with the pair, but it makes it cumbersome to integrate with the remainder of the code. I was hoping for a library-based implementation similar to cupy. I also tried out using the tensorflow linear algebra routines, but I couldn't get them to run on the GPU by now. Specifically, a testfile with a tensorflow.keras.applications.ResNet50 routine runs on the GPU, but the routines from tensorflow.linalg and tensorflow.math that I tested (matmul, expm, eigh) were not running on the GPU. Any advice on how to make linear algebra calculations on mac GPUs work is highly appreciated! For my application the unified memory might be especially beneficial. Thank you!

InvalidArgumentError: Cannot assign a device for operation don_nn/model_2/branch_hidden0/MatMul/ReadVariableOp: Could not satisfy explicit device specification '' because the node {{colocation_node don_nn/model_2/branch_hidden0/MatMul/ReadVariableOp}} was colocated with a group of nodes that required incompatible device '/job:localhost/replica:0/task:0/device:GPU:0'. All available devices [/job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0].