tensorflow-upstream VS tensorflow

I tried to train another model using InceptionResNetV2 and the same issues happens. Also, this happens even using the model.predict() method if using the GPU. Probably this is an issue related to the AMD Radeon RX 6700 XT or some mine misconfiguration. System Inormation: ArchLinux 6.1.32-1-lts - AMD Radeon RX 6700 XT - gfx1031 Opened issues: - https://github.com/RadeonOpenCompute/ROCm/issues/2250 - https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/issues/2125

AMD has had their ROCM Tensorflow port for quite a while now: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream and it works pretty well on my RX 6800; unfortunately it's only compatible with Linux as the ROCm stack is built on top of the Linux kernel I believe

I mean, tensorflow has a fork with ROCm support which is maintained by AMD https://github.com/ROCmSoftwarePlatform/tensorflow-upstream although I'm not entirely sure what you're AI workloads are specifically, I'm just throwing out tensorflow because it's popular. On the enterprise side they also have radeon instinct MI, although I assume you're probably not using enterprise HW but I wanted to throw it out there anyway.

It might work, but will likely require building pytorch/tensorflow from source. I compiled both with ROCm 5.0 today, Pytorch is painless but TF required using this repo maintained by AMD instead

I saw this https://github.com/ROCmSoftwarePlatform/tensorflow-upstream and I thought it could be a way. Do you think it is not stable and ready to be used ?

The repo exists: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream

https://github.com/ROCmSoftwarePlatform/tensorflow-upstream#tensorflow-rocm-port

I tried some case ROCm & Tensorflow-rocm as is on this page( tensorflow-upstream/tensorflow-rocm-release.md at develop-upstream · ROCmSoftwarePlatform/tensorflow-upstream · GitHub ), but I failed to run a simple CNN model with fashion-mnist datasets.

# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")

[3]: https://github.com/tensorflow/tensorflow/graphs/contributors

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Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

Exactly what language do you think TensorFlow is written in? :)