mirror of
https://github.com/intel/intel-device-plugins-for-kubernetes.git
synced 2025-06-03 03:59:37 +00:00
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
# Copyright 2018 The TensorFlow Authors.
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# Copyright 2023 Intel Corporation. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# original code from:
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# https://github.com/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l02c01_celsius_to_fahrenheit.ipynb
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# this is slightly modified to run explicitly with XPU devices
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import tensorflow as tf
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import intel_extension_for_tensorflow as itex
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import numpy as np
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print("BACKENDS: ", str(itex.get_backend()))
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devs = tf.config.list_physical_devices('XPU')
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print(devs)
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if not devs:
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raise Exception("No devices found")
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with tf.device("/xpu:0"):
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celsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
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fahrenheit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(units=1, input_shape=[1])
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])
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model.compile(loss='mean_squared_error',
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optimizer=tf.keras.optimizers.Adam(0.1))
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history = model.fit(celsius_q, fahrenheit_a, epochs=500, verbose=False)
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print("model trained")
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test = [100.0]
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p = model.predict(test)
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if len(p) != 1:
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raise Exception("invalid result obj")
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prediction = p[0]
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if prediction >= 211 and prediction <= 213:
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print("inference ok: %f" % prediction)
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else:
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raise Exception("bad prediction %f" % prediction)
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print("SUCCESS")
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