There is no published method for installing Tensorflow, the leading ML API, on a Macbook Pro M1 that actually works without breaking something else. Why is this question and its responses marked read only? This given that it is such an important topic affecting the adoption of Macbook Pro M1's. Normalize_img, num_parallel_calls=tf.)ĭs_train = ds_train.shuffle(ds_examples)ĭs_train = ds_train.prefetch(tf.)ĭs_test = ds_test.prefetch(tf.) Return tf.cast(image, tf.float32) / 255., label """Normalizes images: uint8 -> float32.""" (ds_train, ds_test), ds_info = tfds.load( Print("Num GPUs Available: ", len(tf._physical_devices('GPU')))įrom import disable_eager_execution I would appreciate very much any help from Apple support or the developers community. We have more than 50 data scientists in our company and I am leading a research on CoreML and the adoption of the new MacBook Pro as a standard platform to our developers. As a remedy I am now running the same code on Anaconda (Rosetta) and it is taking 50% more time. I have formatted the MacBook several times, followed the instructions on and the problem persists. I'd been successfully running M1 native Python code on a MacBook Pro (13-inch, M1, 2020) using Jupyter Notebook, but since the notebook kernel dies as soon as the M1 CPU is used intensively. Please, I need help to run M1 native Python again!
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