change: enable logging-format-interpolation pylint check (aws#904)

Author: knakad

.pylintrc

@@ -83,7 +83,6 @@ disable=
     too-many-instance-attributes,
     line-too-long, # We let Flake8 take care of this # TODO: Fix these and stop relying on flake8
     len-as-condition, # TODO: Enable this check once pylint 2.4.0 is released and consumed due to the fix in https://github.com/PyCQA/pylint/issues/2684
-    logging-format-interpolation, # TODO: Fix logging so as to remove this.
     import-error, # TODO: Fix import errors
     logging-not-lazy, # TODO: Fix logging
     attribute-defined-outside-init, # TODO: Fix scope

src/sagemaker/amazon/amazon_estimator.py

@@ -195,11 +195,11 @@ def record_set(self, train, labels=None, channel="train", encrypt=False):
         bucket, key_prefix = parsed_s3_url.netloc, parsed_s3_url.path
         key_prefix = key_prefix + "{}-{}/".format(type(self).__name__, sagemaker_timestamp())
         key_prefix = key_prefix.lstrip("/")
-        logger.debug("Uploading to bucket {} and key_prefix {}".format(bucket, key_prefix))
+        logger.debug("Uploading to bucket %s and key_prefix %s", bucket, key_prefix)
         manifest_s3_file = upload_numpy_to_s3_shards(
             self.train_instance_count, s3, bucket, key_prefix, train, labels, encrypt
         )
-        logger.debug("Created manifest file {}".format(manifest_s3_file))
+        logger.debug("Created manifest file %s", manifest_s3_file)
         return RecordSet(
             manifest_s3_file,
             num_records=train.shape[0],
@@ -279,7 +279,7 @@ def upload_numpy_to_s3_shards(
                 shard_index_string = str(shard_index).zfill(len(str(len(shards))))
                 file_name = "matrix_{}.pbr".format(shard_index_string)
                 key = key_prefix + file_name
-                logger.debug("Creating object {} in bucket {}".format(key, bucket))
+                logger.debug("Creating object %s in bucket %s", key, bucket)
                 s3.Object(bucket, key).put(Body=file, **extra_put_kwargs)
                 uploaded_files.append(file_name)
         manifest_key = key_prefix + ".amazon.manifest"

src/sagemaker/cli/common.py

@@ -108,9 +108,9 @@ def start(self):
         data_url = self.upload_training_data()
         estimator = self.create_estimator()
         estimator.fit(data_url)
-        logger.debug("code location: {}".format(estimator.uploaded_code.s3_prefix))
+        logger.debug("code location: %s", estimator.uploaded_code.s3_prefix)
         logger.debug(
-            "model location: {}{}/output/model.tar.gz".format(
-                estimator.output_path, estimator._current_job_name
-            )
+            "model location: %s%s/output/model.tar.gz",
+            estimator.output_path,
+            estimator._current_job_name,
         )

src/sagemaker/cli/main.py

@@ -134,7 +134,7 @@ def configure_logging(args):
 def main():
     args = parse_arguments(sys.argv[1:])
     configure_logging(args)
-    logger.debug("args: {}".format(args))
+    logger.debug("args: %s", args)
     args.func(args)
 
 

src/sagemaker/estimator.py

@@ -687,9 +687,8 @@ def start_new(cls, estimator, inputs):
         if isinstance(inputs, s3_input):
             if "InputMode" in inputs.config:
                 logging.debug(
-                    "Selecting s3_input's input_mode ({}) for TrainingInputMode.".format(
-                        inputs.config["InputMode"]
-                    )
+                    "Selecting s3_input's input_mode (%s) for TrainingInputMode.",
+                    inputs.config["InputMode"],
                 )
                 train_args["input_mode"] = inputs.config["InputMode"]
 

src/sagemaker/local/image.py

@@ -172,7 +172,7 @@ def serve(self, model_dir, environment):
         logger.info("serving")
 
         self.container_root = self._create_tmp_folder()
-        logger.info("creating hosting dir in {}".format(self.container_root))
+        logger.info("creating hosting dir in %s", self.container_root)
 
         volumes = self._prepare_serving_volumes(model_dir)
 
@@ -424,7 +424,7 @@ def _generate_compose_file(self, command, additional_volumes=None, additional_en
 
         docker_compose_path = os.path.join(self.container_root, DOCKER_COMPOSE_FILENAME)
         yaml_content = yaml.dump(content, default_flow_style=False)
-        logger.info("docker compose file: \n{}".format(yaml_content))
+        logger.info("docker compose file: \n%s", yaml_content)
         with open(docker_compose_path, "w") as f:
             f.write(yaml_content)
 
@@ -445,7 +445,7 @@ def _compose(self, detached=False):
         if detached:
             command.append("-d")
 
-        logger.info("docker command: {}".format(" ".join(command)))
+        logger.info("docker command: %s", " ".join(command))
         return command
 
     def _create_docker_host(self, host, environment, optml_subdirs, command, volumes):
@@ -739,7 +739,7 @@ def _ecr_login_if_needed(boto_session, image):
 
 def _pull_image(image):
     pull_image_command = ("docker pull %s" % image).strip()
-    logger.info("docker command: {}".format(pull_image_command))
+    logger.info("docker command: %s", pull_image_command)
 
     subprocess.check_output(pull_image_command, shell=True)
-    logger.info("image pulled: {}".format(image))
+    logger.info("image pulled: %s", image)

src/sagemaker/model.py

@@ -298,8 +298,9 @@ def compile(
             self._is_compiled_model = True
         else:
             LOGGER.warning(
-                "The intance type {} is not supported to deploy via SageMaker,"
-                "please deploy the model on the device by yourself.".format(target_instance_family)
+                "The instance type %s is not supported to deploy via SageMaker,"
+                "please deploy the model manually.",
+                target_instance_family,
             )
         return self
 

src/sagemaker/rl/estimator.py

@@ -363,9 +363,8 @@ def _validate_images_args(cls, toolkit, toolkit_version, framework, image_name):
             if found_args:
                 logger.warning(
                     "Parameter `image_name` is specified, "
-                    "`{}` are going to be ignored when choosing the image.".format(
-                        "`, `".join(found_args)
-                    )
+                    "`%s` are going to be ignored when choosing the image.",
+                    "`, `".join(found_args),
                 )
 
     @classmethod

src/sagemaker/session.py

@@ -206,7 +206,7 @@ def default_bucket(self):
                     Bucket=default_bucket, CreateBucketConfiguration={"LocationConstraint": region}
                 )
 
-            LOGGER.info("Created S3 bucket: {}".format(default_bucket))
+            LOGGER.info("Created S3 bucket: %s", default_bucket)
         except ClientError as e:
             error_code = e.response["Error"]["Code"]
             message = e.response["Error"]["Message"]
@@ -343,8 +343,8 @@ def train(  # noqa: C901
         if encrypt_inter_container_traffic:
             train_request["EnableInterContainerTrafficEncryption"] = encrypt_inter_container_traffic
 
-        LOGGER.info("Creating training-job with name: {}".format(job_name))
-        LOGGER.debug("train request: {}".format(json.dumps(train_request, indent=4)))
+        LOGGER.info("Creating training-job with name: %s", job_name)
+        LOGGER.debug("train request: %s", json.dumps(train_request, indent=4))
         self.sagemaker_client.create_training_job(**train_request)
 
     def compile_model(
@@ -379,7 +379,7 @@ def compile_model(
         if tags is not None:
             compilation_job_request["Tags"] = tags
 
-        LOGGER.info("Creating compilation-job with name: {}".format(job_name))
+        LOGGER.info("Creating compilation-job with name: %s", job_name)
         self.sagemaker_client.create_compilation_job(**compilation_job_request)
 
     def tune(
@@ -521,8 +521,8 @@ def tune(
         if encrypt_inter_container_traffic:
             tune_request["TrainingJobDefinition"]["EnableInterContainerTrafficEncryption"] = True
 
-        LOGGER.info("Creating hyperparameter tuning job with name: {}".format(job_name))
-        LOGGER.debug("tune request: {}".format(json.dumps(tune_request, indent=4)))
+        LOGGER.info("Creating hyperparameter tuning job with name: %s", job_name)
+        LOGGER.debug("tune request: %s", json.dumps(tune_request, indent=4))
         self.sagemaker_client.create_hyper_parameter_tuning_job(**tune_request)
 
     def stop_tuning_job(self, name):
@@ -535,18 +535,17 @@ def stop_tuning_job(self, name):
             ClientError: If an error occurs while trying to stop the hyperparameter tuning job.
         """
         try:
-            LOGGER.info("Stopping tuning job: {}".format(name))
+            LOGGER.info("Stopping tuning job: %s", name)
             self.sagemaker_client.stop_hyper_parameter_tuning_job(HyperParameterTuningJobName=name)
         except ClientError as e:
             error_code = e.response["Error"]["Code"]
             # allow to pass if the job already stopped
             if error_code == "ValidationException":
-                LOGGER.info("Tuning job: {} is already stopped or not running.".format(name))
+                LOGGER.info("Tuning job: %s is already stopped or not running.", name)
             else:
                 LOGGER.error(
-                    "Error occurred while attempting to stop tuning job: {}. Please try again.".format(
-                        name
-                    )
+                    "Error occurred while attempting to stop tuning job: %s. Please try again.",
+                    name,
                 )
                 raise
 
@@ -608,8 +607,8 @@ def transform(
         if data_processing is not None:
             transform_request["DataProcessing"] = data_processing
 
-        LOGGER.info("Creating transform job with name: {}".format(job_name))
-        LOGGER.debug("Transform request: {}".format(json.dumps(transform_request, indent=4)))
+        LOGGER.info("Creating transform job with name: %s", job_name)
+        LOGGER.debug("Transform request: %s", json.dumps(transform_request, indent=4))
         self.sagemaker_client.create_transform_job(**transform_request)
 
     def create_model(
@@ -681,8 +680,8 @@ def create_model(
         if enable_network_isolation:
             create_model_request["EnableNetworkIsolation"] = True
 
-        LOGGER.info("Creating model with name: {}".format(name))
-        LOGGER.debug("CreateModel request: {}".format(json.dumps(create_model_request, indent=4)))
+        LOGGER.info("Creating model with name: %s", name)
+        LOGGER.debug("CreateModel request: %s", json.dumps(create_model_request, indent=4))
 
         try:
             self.sagemaker_client.create_model(**create_model_request)
@@ -694,7 +693,7 @@ def create_model(
                 error_code == "ValidationException"
                 and "Cannot create already existing model" in message
             ):
-                LOGGER.warning("Using already existing model: {}".format(name))
+                LOGGER.warning("Using already existing model: %s", name)
             else:
                 raise
 
@@ -765,14 +764,14 @@ def create_model_package_from_algorithm(self, name, description, algorithm_arn,
             },
         }
         try:
-            LOGGER.info("Creating model package with name: {}".format(name))
+            LOGGER.info("Creating model package with name: %s", name)
             self.sagemaker_client.create_model_package(**request)
         except ClientError as e:
             error_code = e.response["Error"]["Code"]
             message = e.response["Error"]["Message"]
 
             if error_code == "ValidationException" and "ModelPackage already exists" in message:
-                LOGGER.warning("Using already existing model package: {}".format(name))
+                LOGGER.warning("Using already existing model package: %s", name)
             else:
                 raise
 
@@ -833,7 +832,7 @@ def create_endpoint_config(
         Returns:
             str: Name of the endpoint point configuration created.
         """
-        LOGGER.info("Creating endpoint-config with name {}".format(name))
+        LOGGER.info("Creating endpoint-config with name %s", name)
 
         tags = tags or []
 
@@ -872,7 +871,7 @@ def create_endpoint(self, endpoint_name, config_name, tags=None, wait=True):
         Returns:
             str: Name of the Amazon SageMaker ``Endpoint`` created.
         """
-        LOGGER.info("Creating endpoint with name {}".format(endpoint_name))
+        LOGGER.info("Creating endpoint with name %s", endpoint_name)
 
         tags = tags or []
 
@@ -915,7 +914,7 @@ def delete_endpoint(self, endpoint_name):
         Args:
             endpoint_name (str): Name of the Amazon SageMaker ``Endpoint`` to delete.
         """
-        LOGGER.info("Deleting endpoint with name: {}".format(endpoint_name))
+        LOGGER.info("Deleting endpoint with name: %s", endpoint_name)
         self.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
 
     def delete_endpoint_config(self, endpoint_config_name):
@@ -924,7 +923,7 @@ def delete_endpoint_config(self, endpoint_config_name):
         Args:
             endpoint_config_name (str): Name of the Amazon SageMaker endpoint configuration to delete.
         """
-        LOGGER.info("Deleting endpoint configuration with name: {}".format(endpoint_config_name))
+        LOGGER.info("Deleting endpoint configuration with name: %s", endpoint_config_name)
         self.sagemaker_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
 
     def delete_model(self, model_name):
@@ -934,7 +933,7 @@ def delete_model(self, model_name):
             model_name (str): Name of the Amazon SageMaker model to delete.
 
         """
-        LOGGER.info("Deleting model with name: {}".format(model_name))
+        LOGGER.info("Deleting model with name: %s", model_name)
         self.sagemaker_client.delete_model(ModelName=model_name)
 
     def wait_for_job(self, job, poll=5):
@@ -1258,9 +1257,8 @@ def get_caller_identity_arn(self):
             role = self.boto_session.client("iam").get_role(RoleName=role_name)["Role"]["Arn"]
         except ClientError:
             LOGGER.warning(
-                "Couldn't call 'get_role' to get Role ARN from role name {} to get Role path.".format(
-                    role_name
-                )
+                "Couldn't call 'get_role' to get Role ARN from role name %s to get Role path.",
+                role_name,
             )
 
         return role

src/sagemaker/tensorflow/estimator.py

@@ -46,7 +46,7 @@
 )
 _SCRIPT_MODE_TENSORBOARD_WARNING = (
     "Tensorboard is not supported with script mode. You can run the following "
-    "command: tensorboard --logdir {} --host localhost --port 6006 This can be "
+    "command: tensorboard --logdir %s --host localhost --port 6006 This can be "
     "run from anywhere with access to the S3 URI used as the logdir."
 )
 
@@ -173,7 +173,7 @@ def run(self):
         """Run TensorBoard process."""
         port, tensorboard_process = self.create_tensorboard_process()
 
-        logger.info("TensorBoard 0.1.7 at http://localhost:{}".format(port))
+        logger.info("TensorBoard 0.1.7 at http://localhost:%s", port)
         while not self.estimator.checkpoint_path:
             self.event.wait(1)
         with self._temporary_directory() as aws_sync_dir:
@@ -388,7 +388,7 @@ def fit_super():
             raise ValueError("Tensorboard is not supported with async fit")
 
         if self._script_mode_enabled() and run_tensorboard_locally:
-            logger.warning(_SCRIPT_MODE_TENSORBOARD_WARNING.format(self.model_dir))
+            logger.warning(_SCRIPT_MODE_TENSORBOARD_WARNING, self.model_dir)
             fit_super()
         elif run_tensorboard_locally:
             tensorboard = Tensorboard(self)

src/sagemaker/tuner.py

@@ -734,9 +734,8 @@ def start_new(cls, tuner, inputs):
         if isinstance(inputs, s3_input):
             if "InputMode" in inputs.config:
                 logging.debug(
-                    "Selecting s3_input's input_mode ({}) for TrainingInputMode.".format(
-                        inputs.config["InputMode"]
-                    )
+                    "Selecting s3_input's input_mode (%s) for TrainingInputMode.",
+                    inputs.config["InputMode"],
                 )
                 tuner_args["input_mode"] = inputs.config["InputMode"]