Fingerspelling embedding + ClearML

hyperparam_opt.py

@@ -0,0 +1,73 @@
+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(
+    project_name='SpoterEmbedding',
+    task_name='Automatic Hyper-Parameter Optimization',
+    task_type=Task.TaskTypes.optimizer,
+    reuse_last_task_id=False
+)
+
+
+optimizer = HyperParameterOptimizer(
+      # specifying the task to be optimized, task must be in system already so it can be cloned
+      base_task_id="4504e0b3ec6745249d3d4c94d3d40652",  
+      # setting the hyperparameters to optimize
+      hyper_parameters=[
+        # epochs:
+        DiscreteParameterRange('Args/epochs', [200]),
+        # learning rate
+        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='train_loss',
+      objective_metric_series='loss',
+      objective_metric_sign='min',  
+
+      # setting optimizer  
+      optimizer_class=OptimizerOptuna,
+  
+      # configuring optimization parameters
+      execution_queue='default',  
+      optimization_time_limit=360, 
+      compute_time_limit=480, 
+      total_max_jobs=20,
+      min_iteration_per_job=0,
+      max_iteration_per_job=150000, 
+      pool_period_min=0.1,
+
+      save_top_k_tasks_only=3,
+      optuna_pruner=MedianPruner(),
+
+      )
+
+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()