diff --git a/hyperparam_opt.py b/hyperparam_opt.py
new file mode 100644
index 0000000..830748e
--- /dev/null
+++ b/hyperparam_opt.py
@@ -0,0 +1,46 @@
+from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
+from clearml.automation import HyperParameterOptimizer
+from clearml.automation.optuna import OptimizerOptuna
+
+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="b0acd4bca5d447a28a882a65cdf3be3e",  
+      # setting the hyperparameters to optimize
+      hyper_parameters=[
+        # epochs
+        UniformIntegerParameterRange('Args/epochs', 200, 800),
+        # learning rate
+        UniformParameterRange('Args/lr', 0.000001, 0.1),
+        # 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',  
+
+      # 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,
+      )
+
+task.execute_remotely(queue_name='default', exit_process=True)
diff --git a/requirements.txt b/requirements.txt
index a7d1285..f339074 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,3 +12,4 @@ clearml==1.10.3
 torch
 torchvision
 tqdm==4.54.1
+optuna==3.1.1
\ No newline at end of file