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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
import json
import uuid
from typing import Any, Dict, List, Tuple
import boto3
import pandas as pd
import os
from botocore.config import Config
from tests.integ.sagemaker.jumpstart.constants import (
TEST_ASSETS_SPECS,
TMP_DIRECTORY_PATH,
TRAINING_DATASET_MODEL_DICT,
ContentType,
)
from sagemaker.jumpstart.constants import JUMPSTART_DEFAULT_REGION_NAME
from sagemaker.jumpstart.utils import get_jumpstart_content_bucket
from sagemaker.session import Session
def get_test_artifact_bucket() -> str:
bucket_name = get_sm_session().default_bucket()
return bucket_name
def get_test_suite_id() -> str:
return str(uuid.uuid4())
def get_sm_session() -> Session:
return Session(boto_session=boto3.Session(region_name=JUMPSTART_DEFAULT_REGION_NAME))
def get_training_dataset_for_model_and_version(model_id: str, version: str) -> dict:
return TRAINING_DATASET_MODEL_DICT[(model_id, version)]
def download_inference_assets():
if not os.path.exists(TMP_DIRECTORY_PATH):
os.makedirs(TMP_DIRECTORY_PATH)
for asset, s3_key in TEST_ASSETS_SPECS.items():
file_path = os.path.join(TMP_DIRECTORY_PATH, str(asset.value))
if not os.path.exists(file_path):
download_file(
file_path,
get_jumpstart_content_bucket(JUMPSTART_DEFAULT_REGION_NAME),
s3_key,
boto3.client("s3"),
)
def get_tabular_data(data_filename: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
asset_file_path = os.path.join(TMP_DIRECTORY_PATH, data_filename)
test_data = pd.read_csv(asset_file_path, header=None)
label, features = test_data.iloc[:, :1], test_data.iloc[:, 1:]
return label, features
def download_file(local_download_path, s3_bucket, s3_key, s3_client) -> None:
s3_client.download_file(s3_bucket, s3_key, local_download_path)
class EndpointInvoker:
def __init__(
self,
endpoint_name: str,
region: str = JUMPSTART_DEFAULT_REGION_NAME,
boto_config: Config = Config(retries={"max_attempts": 10, "mode": "standard"}),
) -> None:
self.endpoint_name = endpoint_name
self.region = region
self.config = boto_config
self.sagemaker_runtime_client = self.get_sagemaker_runtime_client()
def _invoke_endpoint(
self,
body: Any,
content_type: ContentType,
) -> Dict[str, Any]:
response = self.sagemaker_runtime_client.invoke_endpoint(
EndpointName=self.endpoint_name, ContentType=content_type.value, Body=body
)
return json.loads(response["Body"].read())
def invoke_tabular_endpoint(self, data: pd.DataFrame) -> Dict[str, Any]:
return self._invoke_endpoint(
body=data.to_csv(header=False, index=False).encode("utf-8"),
content_type=ContentType.TEXT_CSV,
)
def invoke_spc_endpoint(self, text: List[str]) -> Dict[str, Any]:
return self._invoke_endpoint(
body=json.dumps(text).encode("utf-8"),
content_type=ContentType.LIST_TEXT,
)
def get_sagemaker_runtime_client(self) -> boto3.client:
return boto3.client(
service_name="runtime.sagemaker", config=self.config, region_name=self.region
)
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