add dtk24.04 code

docs/source/tutorials/hpo_quickstart_tensorflow/model.py

+"""
+Port TensorFlow Quickstart to NNI
+=================================
+This is a modified version of `TensorFlow quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 3 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `(Optional) Report intermediate results`_ part, it reports per-epoch accuracy metrics.
+3. In `Report final result`_ part, it reports final accuracy.
+
+.. _TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+"""
+
+# %%
+import nni
+import tensorflow as tf
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned later.
+params = {
+    'dense_units': 128,
+    'activation_type': 'relu',
+    'dropout_rate': 0.2,
+    'learning_rate': 0.001,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+model = tf.keras.models.Sequential([
+    tf.keras.layers.Flatten(input_shape=(28, 28)),
+    tf.keras.layers.Dense(params['dense_units'], activation=params['activation_type']),
+    tf.keras.layers.Dropout(params['dropout_rate']),
+    tf.keras.layers.Dense(10)
+])
+
+adam = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])
+loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+model.compile(optimizer=adam, loss=loss_fn, metrics=['accuracy'])
+
+# %%
+# (Optional) Report intermediate results
+# --------------------------------------
+# The callback reports per-epoch accuracy to show learning curve in the web portal.
+# You can also leverage the metrics for early stopping with :doc:`NNI assessors </hpo/assessors>`.
+#
+# This part can be safely skipped and the experiment will work fine.
+callback = tf.keras.callbacks.LambdaCallback(
+    on_epoch_end = lambda epoch, logs: nni.report_intermediate_result(logs['accuracy'])
+)
+
+# %%
+# Train and evluate the model
+# ---------------------------
+model.fit(x_train, y_train, epochs=5, verbose=2, callbacks=[callback])
+loss, accuracy = model.evaluate(x_test, y_test, verbose=2)
+
+# %%
+# Report final result
+# -------------------
+# Report final accuracy to NNI so the tuning algorithm can suggest better hyperparameters.
+nni.report_final_result(accuracy)

docs/source/tutorials/hpo_quickstart_tensorflow/model.py.md5

+d0c869d9d7c7f9208abc10357de52056
\ No newline at end of file

docs/source/tutorials/hpo_quickstart_tensorflow/model.rst

+:orphan:
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_tensorflow/model.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_tensorflow_model.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_tensorflow_model.py:
+
+
+Port TensorFlow Quickstart to NNI
+=================================
+This is a modified version of `TensorFlow quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 3 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `(Optional) Report intermediate results`_ part, it reports per-epoch accuracy metrics.
+3. In `Report final result`_ part, it reports final accuracy.
+
+.. _TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-25
+
+.. code-block:: default
+
+    import nni
+    import tensorflow as tf
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-29
+
+Hyperparameters to be tuned
+---------------------------
+These are the hyperparameters that will be tuned later.
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-36
+
+.. code-block:: default
+
+    params = {
+        'dense_units': 128,
+        'activation_type': 'relu',
+        'dropout_rate': 0.2,
+        'learning_rate': 0.001,
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 37-41
+
+Get optimized hyperparameters
+-----------------------------
+If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-45
+
+.. code-block:: default
+
+    optimized_params = nni.get_next_parameter()
+    params.update(optimized_params)
+    print(params)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'dense_units': 128, 'activation_type': 'relu', 'dropout_rate': 0.2, 'learning_rate': 0.001}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-48
+
+Load dataset
+------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-53
+
+.. code-block:: default
+
+    mnist = tf.keras.datasets.mnist
+
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 54-56
+
+Build model with hyperparameters
+--------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 56-67
+
+.. code-block:: default
+
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Flatten(input_shape=(28, 28)),
+        tf.keras.layers.Dense(params['dense_units'], activation=params['activation_type']),
+        tf.keras.layers.Dropout(params['dropout_rate']),
+        tf.keras.layers.Dense(10)
+    ])
+
+    adam = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])
+    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+    model.compile(optimizer=adam, loss=loss_fn, metrics=['accuracy'])
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-74
+
+(Optional) Report intermediate results
+--------------------------------------
+The callback reports per-epoch accuracy to show learning curve in the web portal.
+You can also leverage the metrics for early stopping with :doc:`NNI assessors </hpo/assessors>`.
+
+This part can be safely skipped and the experiment will work fine.
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-78
+
+.. code-block:: default
+
+    callback = tf.keras.callbacks.LambdaCallback(
+        on_epoch_end = lambda epoch, logs: nni.report_intermediate_result(logs['accuracy'])
+    )
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 79-81
+
+Train and evluate the model
+---------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-84
+
+.. code-block:: default
+
+    model.fit(x_train, y_train, epochs=5, verbose=2, callbacks=[callback])
+    loss, accuracy = model.evaluate(x_test, y_test, verbose=2)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Epoch 1/5
+    [2022-03-21 01:25:00] INFO (nni/MainThread) Intermediate result: 0.9153500199317932  (Index 0)
+    1875/1875 - 17s - loss: 0.2914 - accuracy: 0.9154 - 17s/epoch - 9ms/step
+    Epoch 2/5
+    [2022-03-21 01:25:18] INFO (nni/MainThread) Intermediate result: 0.9588666558265686  (Index 1)
+    1875/1875 - 18s - loss: 0.1387 - accuracy: 0.9589 - 18s/epoch - 10ms/step
+    Epoch 3/5
+    [2022-03-21 01:25:38] INFO (nni/MainThread) Intermediate result: 0.9677000045776367  (Index 2)
+    1875/1875 - 20s - loss: 0.1073 - accuracy: 0.9677 - 20s/epoch - 11ms/step
+    Epoch 4/5
+    [2022-03-21 01:25:56] INFO (nni/MainThread) Intermediate result: 0.9738666415214539  (Index 3)
+    1875/1875 - 18s - loss: 0.0866 - accuracy: 0.9739 - 18s/epoch - 10ms/step
+    Epoch 5/5
+    [2022-03-21 01:26:16] INFO (nni/MainThread) Intermediate result: 0.977483332157135  (Index 4)
+    1875/1875 - 21s - loss: 0.0728 - accuracy: 0.9775 - 21s/epoch - 11ms/step
+    313/313 - 2s - loss: 0.0702 - accuracy: 0.9776 - 2s/epoch - 6ms/step
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-88
+
+Report final result
+-------------------
+Report final accuracy to NNI so the tuning algorithm can suggest better hyperparameters.
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
+
+.. code-block:: default
+
+    nni.report_final_result(accuracy)
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-03-21 01:27:08] INFO (nni/MainThread) Final result: 0.9775999784469604
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 2 minutes  27.156 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_tensorflow_model.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: model.py <model.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/source/tutorials/hpo_quickstart_tensorflow/sg_execution_times.rst

+
+:orphan:
+
+.. _sphx_glr_tutorials_hpo_quickstart_tensorflow_sg_execution_times:
+
+Computation times
+=================
+**01:24.384** total execution time for **tutorials_hpo_quickstart_tensorflow** files:
+
++-----------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_main.py` (``main.py``)   | 01:24.384 | 0.0 MB |
++-----------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_model.py` (``model.py``) | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------+-----------+--------+