Add automation examples

examples/automation/base_template_keras_simple.py

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+# TRAINS - Keras with Tensorboard example code, automatic logging model and Tensorboard outputs
+#
+# Train a simple deep NN on the MNIST dataset.
+# Gets to 98.40% test accuracy after 20 epochs
+# (there is *a lot* of margin for parameter tuning).
+# 2 seconds per epoch on a K520 GPU.
+from __future__ import print_function
+
+import tempfile
+import os
+
+from keras.callbacks import TensorBoard, ModelCheckpoint
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.optimizers import RMSprop
+from keras.utils import np_utils
+import tensorflow as tf
+
+from trains import Task, Logger
+
+
+# Connecting TRAINS
+task = Task.init(project_name='examples', task_name='Keras HP optimization base')
+
+
+# the data, shuffled and split between train and test sets
+nb_classes = 10
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+X_train = X_train.reshape(60000, 784).astype('float32')/255.
+X_test = X_test.reshape(10000, 784).astype('float32')/255.
+print(X_train.shape[0], 'train samples')
+print(X_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+Y_train = np_utils.to_categorical(y_train, nb_classes)
+Y_test = np_utils.to_categorical(y_test, nb_classes)
+
+args = {'batch_size': 128,
+        'epochs': 6,
+        'layer_1': 512,
+        'layer_2': 512,
+        'layer_3': 10,
+        'layer_4': 512,
+        }
+args = task.connect(args)
+
+model = Sequential()
+model.add(Dense(args['layer_1'], input_shape=(784,)))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(args['layer_2']))
+model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+model.add(Dense(args['layer_3']))
+model.add(Activation('softmax'))
+
+model2 = Sequential()
+model2.add(Dense(args['layer_4'], input_shape=(784,)))
+model2.add(Activation('relu'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=RMSprop(),
+              metrics=['accuracy'])
+
+# Advanced: setting model class enumeration
+labels = dict(('digit_%d' % i, i) for i in range(10))
+task.set_model_label_enumeration(labels)
+
+output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')
+
+board = TensorBoard(log_dir=output_folder, write_images=False)
+model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.hdf5'))
+
+history = model.fit(X_train, Y_train,
+                    batch_size=args['batch_size'], epochs=args['epochs'],
+                    callbacks=[board, model_store],
+                    validation_data=(X_test, Y_test))
+score = model.evaluate(X_test, Y_test, verbose=0)
+print('Test score:', score[0])
+print('Test accuracy:', score[1])
+Logger.current_logger().report_scalar(title='evaluate', series='score', value=score[0], iteration=args['epochs'])
+Logger.current_logger().report_scalar(title='evaluate', series='accuracy', value=score[1], iteration=args['epochs'])

examples/automation/hyper_parameter_optimizer.py

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+import logging
+
+from trains.automation import UniformParameterRange, DiscreteParameterRange, UniformIntegerParameterRange, ParameterSet
+from trains.automation import GridSearch, RandomSearch, HyperParameterOptimizer
+from trains import Task
+
+try:
+    from trains.automation.hpbandster import OptimizerBOHB
+    Our_SearchStrategy = OptimizerBOHB
+except ValueError:
+    logging.getLogger().warning(
+        'Apologies, it seems you do not have \'hpbandster\' installed, '
+        'we will be using RandomSearch strategy instead\n'
+        'If you like to try ' '{{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},\n'
+        'run: pip install hpbandster')
+    Our_SearchStrategy = RandomSearch
+
+
+def job_complete_callback(
+    job_id,                 # type: str
+    objective_value,        # type: float
+    objective_iteration,    # type: int
+    job_parameters,         # type: dict
+    top_performance_job_id  # type: str
+):
+    print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
+    if job_id == top_performance_job_id:
+        print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))
+
+
+# Connecting TRAINS
+task = Task.init(project_name='Hyper-Parameter Optimization',
+                 task_name='Automatic Hyper-Parameter Optimization',
+                 reuse_last_task_id=False)
+
+# experiment template to optimize in the hyper-parameter optimization
+args = {
+    'template_task_id': None,
+}
+args = task.connect(args)
+
+# Get the template task experiment that we want to optimize
+if not args['template_task_id']:
+    args['template_task_id'] = Task.get_task(
+        project_name='examples', task_name='Keras HP optimization base').id
+
+# Example use case:
+an_optimizer = HyperParameterOptimizer(
+    # This is the experiment we want to optimize
+    base_task_id=args['template_task_id'],
+    # here we define the hyper-parameters to optimize
+    hyper_parameters=[
+        UniformIntegerParameterRange('layer_1', min_value=128, max_value=512, step_size=128),
+        UniformIntegerParameterRange('layer_2', min_value=128, max_value=512, step_size=128),
+        DiscreteParameterRange('batch_size', values=[96, 128, 160]),
+        DiscreteParameterRange('epochs', values=[30]),
+    ],
+    # this is the objective metric we want to maximize/minimize
+    objective_metric_title='val_acc',
+    objective_metric_series='val_acc',
+    # now we decide if we want to maximize it or minimize it (accuracy we maximize)
+    objective_metric_sign='max',
+    # let us limit the number of concurrent experiments,
+    # this in turn will make sure we do dont bombard the scheduler with experiments.
+    # if we have an auto-scaler connected, this, by proxy, will limit the number of machine
+    max_number_of_concurrent_tasks=2,
+    # this is the optimizer class (actually doing the optimization)
+    # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
+    # more are coming soon...
+    optimizer_class=Our_SearchStrategy,
+    # Select an execution queue to schedule the experiments for execution
+    execution_queue='default',
+    # Limit the execution time of a single experiment
+    # (this is optional, and if using  OptimizerBOHB, it is ignored)
+    max_job_execution_minutes=10.,
+    # Check the experiments every 6 seconds is way too often, we should probably set it to 5 min,
+    # assuming a single experiment is usually hours...
+    pool_period_min=0.1,
+    # set the maximum number of jobs to launch for the optimization, default (None) unlimited
+    # If OptimizerBOHB is used, it defined the maximum budget in terms of full jobs
+    # basically the cumulative number of iterations will not exceed total_max_jobs * max_iteration_per_job
+    total_max_jobs=10,
+    # This is only applicable for OptimizerBOHB and ignore by the rest
+    # set the minimum number of iterations for an experiment, before early stopping
+    min_iteration_per_job=10,
+    # This is only applicable for OptimizerBOHB and ignore by the rest
+    # set the maximum number of iterations for an experiment to execute
+    max_iteration_per_job=30,
+)
+
+# report every 12 seconds, this is way too often, but we are testing here J
+an_optimizer.set_report_period(0.2)
+# start the optimization process, callback function to be called every time an experiment is completed
+# this function returns immediately
+an_optimizer.start(job_complete_callback=job_complete_callback)
+# set the time limit for the optimization process (2 hours)
+an_optimizer.set_time_limit(in_minutes=120.0)
+# wait until process is done (notice we are controlling the optimization process in the background)
+an_optimizer.wait()
+# optimization is completed, print the top performing experiments id
+top_exp = an_optimizer.get_top_experiments(top_k=3)
+print([t.id for t in top_exp])
+# make sure background optimization stopped
+an_optimizer.stop()
+
+print('We are done, good bye')

examples/automation/random_param_search_example.py

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+from random import random, sample
+from trains import Task
+
+# Connecting TRAINS
+task = Task.init(project_name='examples', task_name='Random Hyper-Parameter Search Example')
+
+# Create a hyper-parameter dictionary for the task
+params = dict()
+
+# track my parameters dictionary
+params = task.connect(params)
+
+# define random search space,
+params['batch_size'] = [64, 96, 128, 160, 192]
+params['layer_1'] = [128, 512, 32]
+params['layer_2'] = [128, 512, 32]
+
+# This is a simple random search
+# (can be integrated with 'bayesian-optimization' 'hpbandster' etc.)
+space = {
+    'batch_size': lambda: sample(params['batch_size'], 1)[0],
+    'layer_1': lambda: sample(range(*params['layer_1']), 1)[0],
+    'layer_2': lambda: sample(range(*params['layer_2']), 1)[0],
+}
+
+# number of random samples to test from 'space'
+params['total_number_of_experiments'] = 3
+
+# execution queue to add experiments to
+params['execution_queue_name'] = 'default'
+
+# experiment template to optimize with random parameter search
+params['experiment_template_name'] = 'Keras HP optimization base'
+
+# Select base template task
+# Notice we can be more imaginative and use task_id which will eliminate the need to use project name
+template_task = Task.get_task(project_name='examples', task_name=params['experiment_template_name'])
+
+for i in range(params['total_number_of_experiments']):
+    # clone the template task into a new write enabled task (where we can change parameters)
+    cloned_task = Task.clone(source_task=template_task,
+                             name=template_task.name+' {}'.format(i), parent=template_task.id)
+
+    # get the original template parameters
+    cloned_task_parameters = cloned_task.get_parameters()
+
+    # override with random samples form grid
+    for k in space.keys():
+        cloned_task_parameters[k] = space[k]()
+
+    # put back into the new cloned task
+    cloned_task.set_parameters(cloned_task_parameters)
+    print('Experiment {} set with parameters {}'.format(i, cloned_task_parameters))
+
+    # enqueue the task for execution
+    Task.enqueue(cloned_task.id, queue_name=params['execution_queue_name'])
+    print('Experiment id={} enqueue for execution'.format(cloned_task.id))
+
+# we are done, the next step is to watch the experiments graphs
+print('Done')

examples/automation/task_piping_example.py

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+from trains import Task
+from time import sleep
+
+# Initialize the Task Pipe's first Task used to start the Task Pipe
+task = Task.init('examples', 'Simple Controller Task')
+
+# Create a hyper-parameter dictionary for the task
+param = dict()
+# Connect the hyper-parameter dictionary to the task
+param = task.connect(param)
+
+# In this example we pass next task's name as a parameter
+param['next_task_name'] = 'Toy Base Task'
+# This is a parameter name in the next task we want to change
+param['param_name'] = 'Example_Param'
+# This is the parameter value in the next task we want to change
+param['param_name_new_value'] = 3
+# The queue where we want the template task (clone) to be sent to
+param['execution_queue_name'] = 'default'
+
+# Simulate the work of a Task
+print('Processing....')
+sleep(2.0)
+print('Done processing :)')
+
+# Get a reference to the task to pipe to.
+next_task = Task.get_task(project_name=task.get_project_name(), task_name=param['next_task_name'])
+
+# Clone the task to pipe to. This creates a task with status Draft whose parameters can be modified.
+cloned_task = Task.clone(source_task=next_task, name='Auto generated cloned task')
+
+# Get the original parameters of the Task, modify the value of one parameter,
+#   and set the parameters in the next Task
+cloned_task_parameters = cloned_task.get_parameters()
+cloned_task_parameters[param['param_name']] = param['param_name_new_value']
+cloned_task.set_parameters(cloned_task_parameters)
+
+# Enqueue the Task for execution. The enqueued Task must already exist in the trains platform
+print('Enqueue next step in pipeline to queue: {}'.format(param['execution_queue_name']))
+Task.enqueue(cloned_task.id, queue_name=param['execution_queue_name'])
+
+# We are done. The next step in the pipe line is in charge of the pipeline now.
+print('Done')

examples/automation/toy_base_task.py

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+# This Task is the base task that we will be executing as a second step (see task_piping.py)
+# In order to make sure this experiment is registered in the platform, you must execute it once.
+
+from trains import Task
+
+# Initialize the task pipe's first task used to start the task pipe
+task = Task.init('examples', 'Toy Base Task')
+
+# Create a dictionary for hyper-parameters
+params = dict()
+
+# Add a parameter and value to the dictionary
+params['Example_Param'] = 1
+
+# Connect the hyper-parameter dictionary to the task
+task.connect(params)
+
+# Print the value to demonstrate it is the value is set by the initiating task.
+print("Example_Param is {}".format(params['Example_Param']))