15
15
16
16
import logging
17
17
18
- from sagemaker import fw_utils
19
-
20
18
import sagemaker
21
19
from sagemaker .fw_utils import model_code_key_prefix , python_deprecation_warning
22
20
from sagemaker .fw_registry import default_framework_uri
@@ -118,16 +116,16 @@ def __init__(
118
116
self .framework_version = framework_version
119
117
self .model_server_workers = model_server_workers
120
118
121
- def prepare_container_def (self , instance_type , accelerator_type = None ):
119
+ def prepare_container_def (self , instance_type = None , accelerator_type = None ):
122
120
"""Return a container definition with framework configuration set in
123
121
model environment variables.
124
122
125
123
Args:
126
124
instance_type (str): The EC2 instance type to deploy this Model to.
127
- For example, 'ml.p2.xlarge' .
125
+ This parameter is unused because Scikit-learn supports only CPU .
128
126
accelerator_type (str): The Elastic Inference accelerator type to
129
127
deploy to the instance for loading and making inferences to the
130
- model. For example, 'ml.eia1.medium'. Note: accelerator types
128
+ model. This parameter is unused because accelerator types
131
129
are not supported by SKLearnModel.
132
130
133
131
Returns:
@@ -139,9 +137,8 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
139
137
140
138
deploy_image = self .image
141
139
if not deploy_image :
142
- image_tag = "{}-{}-{}" .format (self .framework_version , "cpu" , self .py_version )
143
- deploy_image = default_framework_uri (
144
- self .__framework_name__ , self .sagemaker_session .boto_region_name , image_tag
140
+ deploy_image = self .serving_image_uri (
141
+ self .sagemaker_session .boto_region_name , instance_type
145
142
)
146
143
147
144
deploy_key_prefix = model_code_key_prefix (self .key_prefix , self .name , deploy_image )
@@ -156,22 +153,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
156
153
)
157
154
return sagemaker .container_def (deploy_image , model_data_uri , deploy_env )
158
155
159
- def serving_image_uri (self , region_name , instance_type ):
156
+ def serving_image_uri (self , region_name , instance_type ): # pylint: disable=unused-argument
160
157
"""Create a URI for the serving image.
161
158
162
159
Args:
163
160
region_name (str): AWS region where the image is uploaded.
164
- instance_type (str): SageMaker instance type. Used to determine device type
165
- (cpu/gpu/family-specific optimized) .
161
+ instance_type (str): SageMaker instance type. This parameter is unused because
162
+ Scikit-learn supports only CPU .
166
163
167
164
Returns:
168
165
str: The appropriate image URI based on the given parameters.
169
166
170
167
"""
171
- return fw_utils .create_image_uri (
172
- region_name ,
173
- self .__framework_name__ ,
174
- instance_type ,
175
- self .framework_version ,
176
- self .py_version ,
177
- )
168
+ image_tag = "{}-{}-{}" .format (self .framework_version , "cpu" , self .py_version )
169
+ return default_framework_uri (self .__framework_name__ , region_name , image_tag )
0 commit comments