Some fixes

hyperparam_opt.py

@@ -1,7 +1,7 @@
 from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
 from clearml.automation import HyperParameterOptimizer
 from clearml.automation.optuna import OptimizerOptuna
-
+from optuna.pruners import HyperbandPruner, MedianPruner
 from clearml import Task
 
 task = Task.init(
@@ -17,19 +17,19 @@ optimizer = HyperParameterOptimizer(
       base_task_id="4504e0b3ec6745249d3d4c94d3d40652",  
       # setting the hyperparameters to optimize
       hyper_parameters=[
-        # epochs
-        UniformIntegerParameterRange('Args/epochs', 200, 800),
+        # epochs:
+        DiscreteParameterRange('Args/epochs', [200]),
         # learning rate
-        UniformParameterRange('Args/lr', 0.000001, 0.1),
+        UniformParameterRange('Args/lr', 0.000001, 0.01),
         # optimizer
         DiscreteParameterRange('Args/optimizer', ['ADAM', 'SGD']),
         # vector length
         UniformIntegerParameterRange('Args/vector_length', 10, 100),
           ],
       # setting the objective metric we want to maximize/minimize
-      objective_metric_title='silhouette_coefficient',
-      objective_metric_series='val',
-      objective_metric_sign='max',  
+      objective_metric_title='train_loss',
+      objective_metric_series='loss',
+      objective_metric_sign='min',  
 
       # setting optimizer  
       optimizer_class=OptimizerOptuna,
@@ -40,7 +40,34 @@ optimizer = HyperParameterOptimizer(
       compute_time_limit=480, 
       total_max_jobs=20,
       min_iteration_per_job=0,
-      max_iteration_per_job=150000,
+      max_iteration_per_job=150000, 
+      pool_period_min=0.1,
+
+      save_top_k_tasks_only=3,
+      optuna_pruner=MedianPruner(),
+
       )
 
-task.execute_remotely(queue_name='default', exit_process=True)
+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))
+
+task.execute_remotely(queue_name='hypertuning', exit_process=True)
+
+optimizer.set_report_period(0.3)
+
+optimizer.start(job_complete_callback=job_complete_callback)
+
+optimizer.wait()
+
+top_exp = optimizer.get_top_experiments(top_k=3)
+print([t.id for t in top_exp])
+
+optimizer.stop()