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From GitBox <...@apache.org>
Subject [GitHub] yangaws commented on a change in pull request #4091: [AIRFLOW-2524] Update SageMaker hook, operator and sensor for training, tuning and transform
Date Wed, 31 Oct 2018 23:06:02 GMT
yangaws commented on a change in pull request #4091: [AIRFLOW-2524] Update SageMaker hook,
operator and sensor for training, tuning and transform
URL: https://github.com/apache/incubator-airflow/pull/4091#discussion_r229896905
 
 

 ##########
 File path: airflow/contrib/hooks/sagemaker_hook.py
 ##########
 @@ -16,299 +16,793 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-import copy
+import tarfile
+import tempfile
 import time
+import os
+import collections
+import functools
+from datetime import datetime
+
+import botocore.config
 from botocore.exceptions import ClientError
 
 from airflow.exceptions import AirflowException
 from airflow.contrib.hooks.aws_hook import AwsHook
 from airflow.hooks.S3_hook import S3Hook
 
 
+class LogState(object):
+    STARTING = 1
+    WAIT_IN_PROGRESS = 2
+    TAILING = 3
+    JOB_COMPLETE = 4
+    COMPLETE = 5
+
+
+# Position is a tuple that includes the last read timestamp and the number of items that
were read
+# at that time. This is used to figure out which event to start with on the next read.
+Position = collections.namedtuple('Position', ['timestamp', 'skip'])
+
+
+def argmin(arr, f):
+    """Return the index, i, in arr that minimizes f(arr[i])"""
+    m = None
+    i = None
+    for idx, item in enumerate(arr):
+        if item is not None:
+            if m is None or f(item) < m:
+                m = f(item)
+                i = idx
+    return i
+
+
+def some(arr):
+    """Return True iff there is an element, a, of arr such that a is not None"""
+    return functools.reduce(lambda x, y: x or (y is not None), arr, False)
+
+
+def secondary_training_status_changed(current_job_description, prev_job_description):
+    """
+    Returns true if training job's secondary status message has changed.
+
+    :param current_job_description: Current job description, returned from DescribeTrainingJob
call.
+    :type current_job_description: dict
+    :param prev_job_description: Previous job description, returned from DescribeTrainingJob
call.
+    :type prev_job_description: dict
+
+    :return: Whether the secondary status message of a training job changed or not.
+    """
+    current_secondary_status_transitions = current_job_description.get('SecondaryStatusTransitions')
+    if current_secondary_status_transitions is None or len(current_secondary_status_transitions)
== 0:
+        return False
+
+    prev_job_secondary_status_transitions = prev_job_description.get('SecondaryStatusTransitions')
\
+        if prev_job_description is not None else None
+
+    last_message = prev_job_secondary_status_transitions[-1]['StatusMessage'] \
+        if prev_job_secondary_status_transitions is not None \
+        and len(prev_job_secondary_status_transitions) > 0 else ''
+
+    message = current_job_description['SecondaryStatusTransitions'][-1]['StatusMessage']
+
+    return message != last_message
+
+
+def secondary_training_status_message(job_description, prev_description):
+    """
+    Returns a string contains start time and the secondary training job status message.
+
+    :param job_description: Returned response from DescribeTrainingJob call
+    :type job_description: dict
+    :param prev_description: Previous job description from DescribeTrainingJob call
+    :type prev_description: dict
+
+    :return: Job status string to be printed.
+    """
+
+    if job_description is None or job_description.get('SecondaryStatusTransitions') is None\
+            or len(job_description.get('SecondaryStatusTransitions')) == 0:
+        return ''
+
+    prev_description_secondary_transitions = prev_description.get('SecondaryStatusTransitions')\
+        if prev_description is not None else None
+    prev_transitions_num = len(prev_description['SecondaryStatusTransitions'])\
+        if prev_description_secondary_transitions is not None else 0
+    current_transitions = job_description['SecondaryStatusTransitions']
+
+    transitions_to_print = current_transitions[-1:] if len(current_transitions) == prev_transitions_num
else \
+        current_transitions[prev_transitions_num - len(current_transitions):]
+
+    status_strs = []
+    for transition in transitions_to_print:
+        message = transition['StatusMessage']
+        time_str = datetime.utcfromtimestamp(
+            time.mktime(job_description['LastModifiedTime'].timetuple())).strftime('%Y-%m-%d
%H:%M:%S')
+        status_strs.append('{} {} - {}'.format(time_str, transition['Status'], message))
+
+    return '\n'.join(status_strs)
+
+
 class SageMakerHook(AwsHook):
     """
     Interact with Amazon SageMaker.
-    sagemaker_conn_id is required for using
-    the config stored in db for training/tuning
     """
-    non_terminal_states = {'InProgress', 'Stopping', 'Stopped'}
+    non_terminal_states = {'InProgress', 'Stopping'}
+    endpoint_non_terminal_states = {'Creating', 'Updating', 'SystemUpdating',
+                                    'RollingBack', 'Deleting'}
     failed_states = {'Failed'}
 
     def __init__(self,
-                 sagemaker_conn_id=None,
-                 use_db_config=False,
-                 region_name=None,
-                 check_interval=5,
-                 max_ingestion_time=None,
                  *args, **kwargs):
         super(SageMakerHook, self).__init__(*args, **kwargs)
-        self.sagemaker_conn_id = sagemaker_conn_id
-        self.use_db_config = use_db_config
-        self.region_name = region_name
-        self.check_interval = check_interval
-        self.max_ingestion_time = max_ingestion_time
-        self.conn = self.get_conn()
+        self.s3_hook = S3Hook(aws_conn_id=self.aws_conn_id)
+
+    def expand_role(self, role):
+        """
+        Expand an IAM role name to an IAM role ARN. If role is already an IAM ARN,
+        no change is made.
+
+        :param role: IAM role name or ARN
+        :return: IAM role ARN
+        """
+        if '/' in role:
+            return role
+        else:
+            return self.get_iam_conn().get_role(RoleName=role)['Role']['Arn']
+
+    def tar_and_s3_upload(self, path, key, bucket):
+        """
+        Tar the local file or directory and upload to s3
 
-    def check_for_url(self, s3url):
+        :param path: local file or directory
+        :type path: str
+        :param key: s3 key
+        :type key: str
+        :param bucket: s3 bucket
+        :type bucket: str
+        :return: None
+        """
+        with tempfile.TemporaryFile() as temp_file:
+            if os.path.isdir(path):
+                files = [os.path.join(path, name) for name in os.listdir(path)]
+            else:
+                files = [path]
+            with tarfile.open(mode='w:gz', fileobj=temp_file) as tar_file:
+                for f in files:
+                    tar_file.add(f, arcname=os.path.basename(f))
+            temp_file.seek(0)
+            self.s3_hook.load_file_obj(temp_file, key, bucket, True)
+
+    def configure_s3_resources(self, config):
+        """
+        Extract the S3 operations from the configuration and execute them.
+
+        :param config: config of SageMaker operation
+        :type config: dict
+        :return: dict
         """
-        check if the s3url exists
+        s3_operations = config.pop('S3Operations', None)
+
+        if s3_operations is not None:
+            create_bucket_ops = s3_operations.get('S3CreateBucket')
+            upload_ops = s3_operations.get('S3Upload')
+            if create_bucket_ops:
+                for op in create_bucket_ops:
+                    self.s3_hook.create_bucket(bucket_name=op['Bucket'])
+            if upload_ops:
+                for op in upload_ops:
+                    if op['Tar']:
+                        self.tar_and_s3_upload(op['Path'], op['Key'],
+                                               op['Bucket'])
+                    else:
+                        self.s3_hook.load_file(op['Path'], op['Key'],
+                                               op['Bucket'])
+
+        return config
+
+    def check_s3_url(self, s3url):
+        """
+        Check if an S3 URL exists
+
         :param s3url: S3 url
         :type s3url:str
         :return: bool
         """
         bucket, key = S3Hook.parse_s3_url(s3url)
-        s3hook = S3Hook(aws_conn_id=self.aws_conn_id)
-        if not s3hook.check_for_bucket(bucket_name=bucket):
+        if not self.s3_hook.check_for_bucket(bucket_name=bucket):
             raise AirflowException(
                 "The input S3 Bucket {} does not exist ".format(bucket))
-        if key and not s3hook.check_for_key(key=key, bucket_name=bucket)\
-           and not s3hook.check_for_prefix(
+        if key and not self.s3_hook.check_for_key(key=key, bucket_name=bucket)\
+           and not self.s3_hook.check_for_prefix(
                 prefix=key, bucket_name=bucket, delimiter='/'):
             # check if s3 key exists in the case user provides a single file
-            # or if s3 prefix exists in the case user provides a prefix for files
+            # or if s3 prefix exists in the case user provides multiple files in
+            # a prefix
             raise AirflowException("The input S3 Key "
                                    "or Prefix {} does not exist in the Bucket {}"
                                    .format(s3url, bucket))
         return True
 
-    def check_valid_training_input(self, training_config):
+    def check_training_config(self, training_config):
         """
-        Run checks before a training starts
+        Check if a training configuration is valid
+
         :param training_config: training_config
         :type training_config: dict
         :return: None
         """
         for channel in training_config['InputDataConfig']:
-            self.check_for_url(channel['DataSource']
-                               ['S3DataSource']['S3Uri'])
+            self.check_s3_url(channel['DataSource']
+                                     ['S3DataSource']['S3Uri'])
 
-    def check_valid_tuning_input(self, tuning_config):
+    def check_tuning_config(self, tuning_config):
         """
-        Run checks before a tuning job starts
+        Check if a tuning configuration is valid
+
         :param tuning_config: tuning_config
         :type tuning_config: dict
         :return: None
         """
         for channel in tuning_config['TrainingJobDefinition']['InputDataConfig']:
-            self.check_for_url(channel['DataSource']
-                               ['S3DataSource']['S3Uri'])
+            self.check_s3_url(channel['DataSource']
+                                     ['S3DataSource']['S3Uri'])
 
-    def check_status(self, non_terminal_states,
-                     failed_state, key,
-                     describe_function, *args):
-        """
-        :param non_terminal_states: the set of non_terminal states
-        :type non_terminal_states: set
-        :param failed_state: the set of failed states
-        :type failed_state: set
-        :param key: the key of the response dict
-        that points to the state
-        :type key: str
-        :param describe_function: the function used to retrieve the status
-        :type describe_function: python callable
-        :param args: the arguments for the function
-        :return: None
+    def get_conn(self):
         """
-        sec = 0
-        running = True
-
-        while running:
+        Establish an AWS connection for SageMaker
 
-            sec = sec + self.check_interval
-
-            if self.max_ingestion_time and sec > self.max_ingestion_time:
-                # ensure that the job gets killed if the max ingestion time is exceeded
-                raise AirflowException("SageMaker job took more than "
-                                       "%s seconds", self.max_ingestion_time)
-
-            time.sleep(self.check_interval)
-            try:
-                response = describe_function(*args)
-                status = response[key]
-                self.log.info("Job still running for %s seconds... "
-                              "current status is %s" % (sec, status))
-            except KeyError:
-                raise AirflowException("Could not get status of the SageMaker job")
-            except ClientError:
-                raise AirflowException("AWS request failed, check log for more info")
+        :return: a boto3 SageMaker client
+        """
+        return self.get_client_type('sagemaker')
 
-            if status in non_terminal_states:
-                running = True
-            elif status in failed_state:
-                raise AirflowException("SageMaker job failed because %s"
-                                       % response['FailureReason'])
-            else:
-                running = False
+    def get_log_conn(self):
+        """
+        Establish an AWS connection for retrieving logs during training
 
-        self.log.info('SageMaker Job Compeleted')
+        :return: a boto3 CloudWatchLog client
+        """
+        config = botocore.config.Config(retries={'max_attempts': 15})
+        return self.get_client_type('logs', config=config)
 
-    def get_conn(self):
+    def get_iam_conn(self):
         """
-        Establish an AWS connection
-        :return: a boto3 SageMaker client
+        Establish an AWS connection for retrieving IAM roles during training
+
+        :return: a boto3 IAM client
         """
-        return self.get_client_type('sagemaker', region_name=self.region_name)
+        return self.get_client_type('iam')
 
-    def list_training_job(self, name_contains=None, status_equals=None):
+    def log_stream(self, log_group, stream_name, start_time=0, skip=0):
         """
-        List the training jobs associated with the given input
-        :param name_contains: A string in the training job name
-        :type name_contains: str
-        :param status_equals: 'InProgress'|'Completed'
-        |'Failed'|'Stopping'|'Stopped'
-        :return:dict
+        A generator for log items in a single stream. This will yield all the
+        items that are available at the current moment.
+
+        :param log_group: The name of the log group.
+        :type log_group: str
+        :param stream_name: The name of the specific stream.
+        :type stream_name: str
+        :param start_time: The time stamp value to start reading the logs from (default:
0).
+        :type start_time: int
+        :param skip: The number of log entries to skip at the start (default: 0).
+            This is for when there are multiple entries at the same timestamp.
+        :type skip: int
+        :return:A CloudWatch log event with the following key-value pairs:
+            'timestamp' (int): The time of the event.
+            'message' (str): The log event data.
+            'ingestionTime' (int): The time the event was ingested.
         """
-        return self.conn.list_training_jobs(
-            NameContains=name_contains, StatusEquals=status_equals)
 
-    def list_tuning_job(self, name_contains=None, status_equals=None):
+        next_token = None
+
+        event_count = 1
+        while event_count > 0:
+            if next_token is not None:
+                token_arg = {'nextToken': next_token}
+            else:
+                token_arg = {}
+
+            response = self.get_log_conn().get_log_events(logGroupName=log_group,
+                                                          logStreamName=stream_name,
+                                                          startTime=start_time,
+                                                          startFromHead=True,
+                                                          **token_arg)
 
 Review comment:
   I talked with cloudwatch guys. They identified my use case as 'tailing the logs'. So 'get_log_events'
is what they recommend to use (nextToken will never be null in this case unlike what in 'filter_log_events').

   

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