Generate unique SageMaker training job name based on pipeline and ste…

src/zenml/integrations/aws/step_operators/sagemaker_step_operator.py

@@ -13,6 +13,8 @@
 #  permissions and limitations under the License.
 """Implementation of the Sagemaker Step Operator."""
 
+import random
+import string
 from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type, cast
 
 import sagemaker
@@ -179,9 +181,14 @@ def launch(
             image_name, self.config.role, **estimator_args
         )
 
+        # SageMaker allows 63 characters at maximum for job name - ZenML uses 60 for safety margin.
+        step_name = Client().get_run_step(info.step_run_id).name
+        training_job_name = f"{info.pipeline.name}-{step_name}"[:55]
+        suffix = "".join(random.choices(string.ascii_lowercase, k=4))
+        unique_training_job_name = f"{training_job_name}-{suffix}"
+
         # Sagemaker doesn't allow any underscores in job/experiment/trial names
-        job_name = f"{info.run_name}-{info.pipeline_step_name}"
-        job_name = job_name.replace("_", "-")
+        sanitized_training_job_name = unique_training_job_name.replace("_", "-")
 
         # Construct training input object, if necessary
         inputs = None
@@ -201,12 +208,12 @@ def launch(
         if settings.experiment_name:
             experiment_config = {
                 "ExperimentName": settings.experiment_name,
-                "TrialName": job_name,
+                "TrialName": sanitized_training_job_name,
             }
 
         estimator.fit(
             wait=True,
             inputs=inputs,
             experiment_config=experiment_config,
-            job_name=job_name,
+            job_name=sanitized_training_job_name,
         )