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Tensorboard Related Documentation (#187)
* Made changes related cmf init commands * Added tesorflow related document * Update mkdocs.yaml Added for testing of TensorFlow document purpose * Update document * Added image * Added image * Update tensorflow_guide.md Made chanages inside image path * kept cmf related old code inside old_archive_code directory * Removing unnecessary files * Made cmf server url as optional parameter * Made changes inside cmf client command file * Update tensorboard document * Added OSDF related parameter and Resolved Ann's Comment * Modified changes related to cmf-server page * Made changes inside README file * Made content bold * Added a disclosure widget * Updated cmf-server.md docker run commands * Updating cmf-server.md * Updating cmf-server.md * Updating ENV PATH variable as new Dockerfile guidelines * Update cmf-server.md --------- Co-authored-by: Varkha Sharma <[email protected]>
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| # How to Use TensorBoard with CMF | ||
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| 1. Copy the contents of the 'example-get-started' directory from `cmf/examples/example-get-started` into a separate directory outside cmf repository. | ||
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| 2. Execute the following command to install the TensorFlow library in the current directory: | ||
| ```bash | ||
| pip install tensorflow | ||
| ``` | ||
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| 3. Create a new Python file (e.g., `tensorflow_log.py`) and copy the following code: | ||
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| ``` | ||
| import datetime | ||
| import tensorflow as tf | ||
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| 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 | ||
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| def create_model(): | ||
| return tf.keras.models.Sequential([ | ||
| tf.keras.layers.Flatten(input_shape=(28, 28), name='layers_flatten'), | ||
| tf.keras.layers.Dense(512, activation='relu', name='layers_dense'), | ||
| tf.keras.layers.Dropout(0.2, name='layers_dropout'), | ||
| tf.keras.layers.Dense(10, activation='softmax', name='layers_dense_2') | ||
| ]) | ||
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| model = create_model() | ||
| model.compile(optimizer='adam', | ||
| loss='sparse_categorical_crossentropy', | ||
| metrics=['accuracy']) | ||
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| log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") | ||
| tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1) | ||
| model.fit(x=x_train,y=y_train,epochs=5,validation_data=(x_test, y_test),callbacks=[tensorboard_callback]) | ||
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| train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) | ||
| test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)) | ||
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| train_dataset = train_dataset.shuffle(60000).batch(64) | ||
| test_dataset = test_dataset.batch(64) | ||
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| loss_object = tf.keras.losses.SparseCategoricalCrossentropy() | ||
| optimizer = tf.keras.optimizers.Adam() | ||
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| # Define our metrics | ||
| train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32) | ||
| train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('train_accuracy') | ||
| test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32) | ||
| test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('test_accuracy') | ||
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| def train_step(model, optimizer, x_train, y_train): | ||
| with tf.GradientTape() as tape: | ||
| predictions = model(x_train, training=True) | ||
| loss = loss_object(y_train, predictions) | ||
| grads = tape.gradient(loss, model.trainable_variables) | ||
| optimizer.apply_gradients(zip(grads, model.trainable_variables)) | ||
| train_loss(loss) | ||
| train_accuracy(y_train, predictions) | ||
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| def test_step(model, x_test, y_test): | ||
| predictions = model(x_test) | ||
| loss = loss_object(y_test, predictions) | ||
| test_loss(loss) | ||
| test_accuracy(y_test, predictions) | ||
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| current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") | ||
| train_log_dir = 'logs/gradient_tape/' + current_time + '/train' | ||
| test_log_dir = 'logs/gradient_tape/' + current_time + '/test' | ||
| train_summary_writer = tf.summary.create_file_writer(train_log_dir) | ||
| test_summary_writer = tf.summary.create_file_writer(test_log_dir) | ||
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| model = create_model() # reset our model | ||
| EPOCHS = 5 | ||
| for epoch in range(EPOCHS): | ||
| for (x_train, y_train) in train_dataset: | ||
| train_step(model, optimizer, x_train, y_train) | ||
| with train_summary_writer.as_default(): | ||
| tf.summary.scalar('loss', train_loss.result(), step=epoch) | ||
| tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch) | ||
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| for (x_test, y_test) in test_dataset: | ||
| test_step(model, x_test, y_test) | ||
| with test_summary_writer.as_default(): | ||
| tf.summary.scalar('loss', test_loss.result(), step=epoch) | ||
| tf.summary.scalar('accuracy', test_accuracy.result(), step=epoch) | ||
| template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}' | ||
| print (template.format(epoch+1, | ||
| train_loss.result(), | ||
| train_accuracy.result()*100, | ||
| test_loss.result(), | ||
| test_accuracy.result()*100)) | ||
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| ``` | ||
| For more detailed information, check out the [TensorBoard documentation](https://www.tensorflow.org/tensorboard/get_started). | ||
| 5. Execute the TensorFlow log script using the following command: | ||
| ```bash | ||
| python3 tensorflow_log.py | ||
| ``` | ||
| 6. The above script will automatically create a `logs` directory inside your current directory. | ||
| 7. Start the CMF server and configure the [CMF client](step-by-step.md). | ||
| 8. Use the following command to run the test script, which will generate the MLMD file: | ||
| ```bash | ||
| sh test_script.sh | ||
| ``` | ||
| 9. Use the following command to push the generated MLMD and TensorFlow log files to the CMF server: | ||
| ```bash | ||
| cmf metadata push -p 'pipeline-name' -t 'tensorboard-log-file-name' | ||
| ``` | ||
| 10. Go to the CMF server and navigate to the TensorBoard tab. You will see an interface similar to the following image. | ||
|  | ||
| --- |
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