Basic Support for Endpoints, EndpointConfigs and TrainingJobs (#3142)

* Basic upport for Endpoints, EndpointConfigs and TrainingJobs

* Dropped extraneous pass statement.

Co-authored-by: Joseph Weitekamp <jweite@amazon.com>

tests/test_sagemaker/test_sagemaker_training.py

@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+import boto3
+import datetime
+import sure  # noqa
+
+from moto import mock_sagemaker
+from moto.sts.models import ACCOUNT_ID
+from nose.tools import assert_true, assert_equal, assert_raises, assert_regexp_matches
+
+FAKE_ROLE_ARN = "arn:aws:iam::{}:role/FakeRole".format(ACCOUNT_ID)
+TEST_REGION_NAME = "us-east-1"
+
+
+@mock_sagemaker
+def test_create_training_job():
+    sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
+
+    training_job_name = "MyTrainingJob"
+    container = "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1"
+    bucket = "my-bucket"
+    prefix = "sagemaker/DEMO-breast-cancer-prediction/"
+
+    params = {
+        "RoleArn": FAKE_ROLE_ARN,
+        "TrainingJobName": training_job_name,
+        "AlgorithmSpecification": {
+            "TrainingImage": container,
+            "TrainingInputMode": "File",
+        },
+        "ResourceConfig": {
+            "InstanceCount": 1,
+            "InstanceType": "ml.c4.2xlarge",
+            "VolumeSizeInGB": 10,
+        },
+        "InputDataConfig": [
+            {
+                "ChannelName": "train",
+                "DataSource": {
+                    "S3DataSource": {
+                        "S3DataType": "S3Prefix",
+                        "S3Uri": "s3://{}/{}/train/".format(bucket, prefix),
+                        "S3DataDistributionType": "ShardedByS3Key",
+                    }
+                },
+                "CompressionType": "None",
+                "RecordWrapperType": "None",
+            },
+            {
+                "ChannelName": "validation",
+                "DataSource": {
+                    "S3DataSource": {
+                        "S3DataType": "S3Prefix",
+                        "S3Uri": "s3://{}/{}/validation/".format(bucket, prefix),
+                        "S3DataDistributionType": "FullyReplicated",
+                    }
+                },
+                "CompressionType": "None",
+                "RecordWrapperType": "None",
+            },
+        ],
+        "OutputDataConfig": {"S3OutputPath": "s3://{}/{}/".format(bucket, prefix)},
+        "HyperParameters": {
+            "feature_dim": "30",
+            "mini_batch_size": "100",
+            "predictor_type": "regressor",
+            "epochs": "10",
+            "num_models": "32",
+            "loss": "absolute_loss",
+        },
+        "StoppingCondition": {"MaxRuntimeInSeconds": 60 * 60},
+    }
+
+    resp = sagemaker.create_training_job(**params)
+    resp["TrainingJobArn"].should.match(
+        r"^arn:aws:sagemaker:.*:.*:training-job/{}$".format(training_job_name)
+    )
+
+    resp = sagemaker.describe_training_job(TrainingJobName=training_job_name)
+    resp["TrainingJobName"].should.equal(training_job_name)
+    resp["TrainingJobArn"].should.match(
+        r"^arn:aws:sagemaker:.*:.*:training-job/{}$".format(training_job_name)
+    )
+    assert_true(
+        resp["ModelArtifacts"]["S3ModelArtifacts"].startswith(
+            params["OutputDataConfig"]["S3OutputPath"]
+        )
+    )
+    assert_true(training_job_name in (resp["ModelArtifacts"]["S3ModelArtifacts"]))
+    assert_true(
+        resp["ModelArtifacts"]["S3ModelArtifacts"].endswith("output/model.tar.gz")
+    )
+    assert_equal(resp["TrainingJobStatus"], "Completed")
+    assert_equal(resp["SecondaryStatus"], "Completed")
+    assert_equal(resp["HyperParameters"], params["HyperParameters"])
+    assert_equal(
+        resp["AlgorithmSpecification"]["TrainingImage"],
+        params["AlgorithmSpecification"]["TrainingImage"],
+    )
+    assert_equal(
+        resp["AlgorithmSpecification"]["TrainingInputMode"],
+        params["AlgorithmSpecification"]["TrainingInputMode"],
+    )
+    assert_true("MetricDefinitions" in resp["AlgorithmSpecification"])
+    assert_true("Name" in resp["AlgorithmSpecification"]["MetricDefinitions"][0])
+    assert_true("Regex" in resp["AlgorithmSpecification"]["MetricDefinitions"][0])
+    assert_equal(resp["RoleArn"], FAKE_ROLE_ARN)
+    assert_equal(resp["InputDataConfig"], params["InputDataConfig"])
+    assert_equal(resp["OutputDataConfig"], params["OutputDataConfig"])
+    assert_equal(resp["ResourceConfig"], params["ResourceConfig"])
+    assert_equal(resp["StoppingCondition"], params["StoppingCondition"])
+    assert_true(isinstance(resp["CreationTime"], datetime.datetime))
+    assert_true(isinstance(resp["TrainingStartTime"], datetime.datetime))
+    assert_true(isinstance(resp["TrainingEndTime"], datetime.datetime))
+    assert_true(isinstance(resp["LastModifiedTime"], datetime.datetime))
+    assert_true("SecondaryStatusTransitions" in resp)
+    assert_true("Status" in resp["SecondaryStatusTransitions"][0])
+    assert_true("StartTime" in resp["SecondaryStatusTransitions"][0])
+    assert_true("EndTime" in resp["SecondaryStatusTransitions"][0])
+    assert_true("StatusMessage" in resp["SecondaryStatusTransitions"][0])
+    assert_true("FinalMetricDataList" in resp)
+    assert_true("MetricName" in resp["FinalMetricDataList"][0])
+    assert_true("Value" in resp["FinalMetricDataList"][0])
+    assert_true("Timestamp" in resp["FinalMetricDataList"][0])
+
+    pass