Source code for airflow.contrib.hooks.sagemaker_hook

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import tarfile
import tempfile
import time
import os
import collections

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
from airflow.utils import timezone


[docs]class LogState(object):
[docs] STARTING = 1
[docs] WAIT_IN_PROGRESS = 2
[docs] TAILING = 3
[docs] JOB_COMPLETE = 4
[docs] 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.
[docs]Position = collections.namedtuple('Position', ['timestamp', 'skip'])
[docs]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
[docs]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
[docs]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 = timezone.convert_to_utc(job_description['LastModifiedTime']).strftime('%Y-%m-%d %H:%M:%S') status_strs.append('{} {} - {}'.format(time_str, transition['Status'], message)) return '\n'.join(status_strs)
[docs]class SageMakerHook(AwsHook): """ Interact with Amazon SageMaker. """
[docs] non_terminal_states = {'InProgress', 'Stopping'}
[docs] endpoint_non_terminal_states = {'Creating', 'Updating', 'SystemUpdating', 'RollingBack', 'Deleting'}
[docs] failed_states = {'Failed'}
def __init__(self, *args, **kwargs): super(SageMakerHook, self).__init__(*args, **kwargs) self.s3_hook = S3Hook(aws_conn_id=self.aws_conn_id)
[docs] def tar_and_s3_upload(self, path, key, bucket): """ Tar the local file or directory and upload to s3 :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, replace=True)
[docs] 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 :rtype: dict """ 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', []) for op in create_bucket_ops: self.s3_hook.create_bucket(bucket_name=op['Bucket']) 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'])
[docs] def check_s3_url(self, s3url): """ Check if an S3 URL exists :param s3url: S3 url :type s3url: str :rtype: bool """ bucket, key = S3Hook.parse_s3_url(s3url) 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 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 multiple files in # a prefix raise AirflowException("The input S3 Key " "or Prefix {} does not exist in the Bucket {}" .format(s3url, bucket)) return True
[docs] def check_training_config(self, training_config): """ 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_s3_url(channel['DataSource']['S3DataSource']['S3Uri'])
[docs] def check_tuning_config(self, tuning_config): """ 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_s3_url(channel['DataSource']['S3DataSource']['S3Uri'])
[docs] def get_conn(self): """ Establish an AWS connection for SageMaker :rtype: :py:class:`SageMaker.Client` """ return self.get_client_type('sagemaker')
[docs] def get_log_conn(self): """ Establish an AWS connection for retrieving logs during training :rtype: CloudWatchLogs.Client """ config = botocore.config.Config(retries={'max_attempts': 15}) return self.get_client_type('logs', config=config)
[docs] def log_stream(self, log_group, stream_name, start_time=0, skip=0): """ 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 :rtype: dict :return: | A CloudWatch log event with the following key-value pairs: | 'timestamp' (int): The time in milliseconds of the event. | 'message' (str): The log event data. | 'ingestionTime' (int): The time in milliseconds the event was ingested. """ 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) next_token = response['nextForwardToken'] events = response['events'] event_count = len(events) if event_count > skip: events = events[skip:] skip = 0 else: skip = skip - event_count events = [] for ev in events: yield ev
[docs] def multi_stream_iter(self, log_group, streams, positions=None): """ Iterate over the available events coming from a set of log streams in a single log group interleaving the events from each stream so they're yielded in timestamp order. :param log_group: The name of the log group. :type log_group: str :param streams: A list of the log stream names. The position of the stream in this list is the stream number. :type streams: list :param positions: A list of pairs of (timestamp, skip) which represents the last record read from each stream. :type positions: list :return: A tuple of (stream number, cloudwatch log event). """ positions = positions or {s: Position(timestamp=0, skip=0) for s in streams} event_iters = [self.log_stream(log_group, s, positions[s].timestamp, positions[s].skip) for s in streams] events = [] for s in event_iters: if not s: events.append(None) continue try: events.append(next(s)) except StopIteration: events.append(None) while any(events): i = argmin(events, lambda x: x['timestamp'] if x else 9999999999) yield (i, events[i]) try: events[i] = next(event_iters[i]) except StopIteration: events[i] = None
[docs] def create_training_job(self, config, wait_for_completion=True, print_log=True, check_interval=30, max_ingestion_time=None): """ Create a training job :param config: the config for training :type config: dict :param wait_for_completion: if the program should keep running until job finishes :type wait_for_completion: bool :param check_interval: the time interval in seconds which the operator will check the status of any SageMaker job :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :return: A response to training job creation """ self.check_training_config(config) response = self.get_conn().create_training_job(**config) if print_log: self.check_training_status_with_log(config['TrainingJobName'], self.non_terminal_states, self.failed_states, wait_for_completion, check_interval, max_ingestion_time ) elif wait_for_completion: describe_response = self.check_status(config['TrainingJobName'], 'TrainingJobStatus', self.describe_training_job, check_interval, max_ingestion_time ) billable_time = \ (describe_response['TrainingEndTime'] - describe_response['TrainingStartTime']) * \ describe_response['ResourceConfig']['InstanceCount'] self.log.info('Billable seconds:{}'.format(int(billable_time.total_seconds()) + 1)) return response
[docs] def create_tuning_job(self, config, wait_for_completion=True, check_interval=30, max_ingestion_time=None): """ Create a tuning job :param config: the config for tuning :type config: dict :param wait_for_completion: if the program should keep running until job finishes :type wait_for_completion: bool :param check_interval: the time interval in seconds which the operator will check the status of any SageMaker job :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :return: A response to tuning job creation """ self.check_tuning_config(config) response = self.get_conn().create_hyper_parameter_tuning_job(**config) if wait_for_completion: self.check_status(config['HyperParameterTuningJobName'], 'HyperParameterTuningJobStatus', self.describe_tuning_job, check_interval, max_ingestion_time ) return response
[docs] def create_transform_job(self, config, wait_for_completion=True, check_interval=30, max_ingestion_time=None): """ Create a transform job :param config: the config for transform job :type config: dict :param wait_for_completion: if the program should keep running until job finishes :type wait_for_completion: bool :param check_interval: the time interval in seconds which the operator will check the status of any SageMaker job :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :return: A response to transform job creation """ self.check_s3_url(config['TransformInput']['DataSource']['S3DataSource']['S3Uri']) response = self.get_conn().create_transform_job(**config) if wait_for_completion: self.check_status(config['TransformJobName'], 'TransformJobStatus', self.describe_transform_job, check_interval, max_ingestion_time ) return response
[docs] def create_model(self, config): """ Create a model job :param config: the config for model :type config: dict :return: A response to model creation """ return self.get_conn().create_model(**config)
[docs] def create_endpoint_config(self, config): """ Create an endpoint config :param config: the config for endpoint-config :type config: dict :return: A response to endpoint config creation """ return self.get_conn().create_endpoint_config(**config)
[docs] def create_endpoint(self, config, wait_for_completion=True, check_interval=30, max_ingestion_time=None): """ Create an endpoint :param config: the config for endpoint :type config: dict :param wait_for_completion: if the program should keep running until job finishes :type wait_for_completion: bool :param check_interval: the time interval in seconds which the operator will check the status of any SageMaker job :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :return: A response to endpoint creation """ response = self.get_conn().create_endpoint(**config) if wait_for_completion: self.check_status(config['EndpointName'], 'EndpointStatus', self.describe_endpoint, check_interval, max_ingestion_time, non_terminal_states=self.endpoint_non_terminal_states ) return response
[docs] def update_endpoint(self, config, wait_for_completion=True, check_interval=30, max_ingestion_time=None): """ Update an endpoint :param config: the config for endpoint :type config: dict :param wait_for_completion: if the program should keep running until job finishes :type wait_for_completion: bool :param check_interval: the time interval in seconds which the operator will check the status of any SageMaker job :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :return: A response to endpoint update """ response = self.get_conn().update_endpoint(**config) if wait_for_completion: self.check_status(config['EndpointName'], 'EndpointStatus', self.describe_endpoint, check_interval, max_ingestion_time, non_terminal_states=self.endpoint_non_terminal_states ) return response
[docs] def describe_training_job(self, name): """ Return the training job info associated with the name :param name: the name of the training job :type name: str :return: A dict contains all the training job info """ return self.get_conn().describe_training_job(TrainingJobName=name)
[docs] def describe_training_job_with_log(self, job_name, positions, stream_names, instance_count, state, last_description, last_describe_job_call): """ Return the training job info associated with job_name and print CloudWatch logs """ log_group = '/aws/sagemaker/TrainingJobs' if len(stream_names) < instance_count: # Log streams are created whenever a container starts writing to stdout/err, so this list # may be dynamic until we have a stream for every instance. logs_conn = self.get_log_conn() try: streams = logs_conn.describe_log_streams( logGroupName=log_group, logStreamNamePrefix=job_name + '/', orderBy='LogStreamName', limit=instance_count ) stream_names = [s['logStreamName'] for s in streams['logStreams']] positions.update([(s, Position(timestamp=0, skip=0)) for s in stream_names if s not in positions]) except logs_conn.exceptions.ResourceNotFoundException: # On the very first training job run on an account, there's no log group until # the container starts logging, so ignore any errors thrown about that pass if len(stream_names) > 0: for idx, event in self.multi_stream_iter(log_group, stream_names, positions): self.log.info(event['message']) ts, count = positions[stream_names[idx]] if event['timestamp'] == ts: positions[stream_names[idx]] = Position(timestamp=ts, skip=count + 1) else: positions[stream_names[idx]] = Position(timestamp=event['timestamp'], skip=1) if state == LogState.COMPLETE: return state, last_description, last_describe_job_call if state == LogState.JOB_COMPLETE: state = LogState.COMPLETE elif time.time() - last_describe_job_call >= 30: description = self.describe_training_job(job_name) last_describe_job_call = time.time() if secondary_training_status_changed(description, last_description): self.log.info(secondary_training_status_message(description, last_description)) last_description = description status = description['TrainingJobStatus'] if status not in self.non_terminal_states: state = LogState.JOB_COMPLETE return state, last_description, last_describe_job_call
[docs] def describe_tuning_job(self, name): """ Return the tuning job info associated with the name :param name: the name of the tuning job :type name: string :return: A dict contains all the tuning job info """ return self.get_conn().describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=name)
[docs] def describe_model(self, name): """ Return the SageMaker model info associated with the name :param name: the name of the SageMaker model :type name: string :return: A dict contains all the model info """ return self.get_conn().describe_model(ModelName=name)
[docs] def describe_transform_job(self, name): """ Return the transform job info associated with the name :param name: the name of the transform job :type name: string :return: A dict contains all the transform job info """ return self.get_conn().describe_transform_job(TransformJobName=name)
[docs] def describe_endpoint_config(self, name): """ Return the endpoint config info associated with the name :param name: the name of the endpoint config :type name: string :return: A dict contains all the endpoint config info """ return self.get_conn().describe_endpoint_config(EndpointConfigName=name)
[docs] def describe_endpoint(self, name): """ :param name: the name of the endpoint :type name: string :return: A dict contains all the endpoint info """ return self.get_conn().describe_endpoint(EndpointName=name)
[docs] def check_status(self, job_name, key, describe_function, check_interval, max_ingestion_time, non_terminal_states=None): """ Check status of a SageMaker job :param job_name: name of the job to check status :type job_name: str :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 :param check_interval: the time interval in seconds which the operator will check the status of any SageMaker job :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :param non_terminal_states: the set of nonterminal states :type non_terminal_states: set :return: response of describe call after job is done """ if not non_terminal_states: non_terminal_states = self.non_terminal_states sec = 0 running = True while running: time.sleep(check_interval) sec = sec + check_interval try: response = describe_function(job_name) 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 logs for more info') if status in non_terminal_states: running = True elif status in self.failed_states: raise AirflowException('SageMaker job failed because %s' % response['FailureReason']) else: running = False if max_ingestion_time and sec > 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', max_ingestion_time) self.log.info('SageMaker Job Compeleted') response = describe_function(job_name) return response
[docs] def check_training_status_with_log(self, job_name, non_terminal_states, failed_states, wait_for_completion, check_interval, max_ingestion_time): """ Display the logs for a given training job, optionally tailing them until the job is complete. :param job_name: name of the training job to check status and display logs for :type job_name: str :param non_terminal_states: the set of non_terminal states :type non_terminal_states: set :param failed_states: the set of failed states :type failed_states: set :param wait_for_completion: Whether to keep looking for new log entries until the job completes :type wait_for_completion: bool :param check_interval: The interval in seconds between polling for new log entries and job completion :type check_interval: int :param max_ingestion_time: the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job. :type max_ingestion_time: int :return: None """ sec = 0 description = self.describe_training_job(job_name) self.log.info(secondary_training_status_message(description, None)) instance_count = description['ResourceConfig']['InstanceCount'] status = description['TrainingJobStatus'] stream_names = [] # The list of log streams positions = {} # The current position in each stream, map of stream name -> position job_already_completed = status not in non_terminal_states state = LogState.TAILING if wait_for_completion and not job_already_completed else LogState.COMPLETE # The loop below implements a state machine that alternates between checking the job status and # reading whatever is available in the logs at this point. Note, that if we were called with # wait_for_completion == False, we never check the job status. # # If wait_for_completion == TRUE and job is not completed, the initial state is TAILING # If wait_for_completion == FALSE, the initial state is COMPLETE # (doesn't matter if the job really is complete). # # The state table: # # STATE ACTIONS CONDITION NEW STATE # ---------------- ---------------- ----------------- ---------------- # TAILING Read logs, Pause, Get status Job complete JOB_COMPLETE # Else TAILING # JOB_COMPLETE Read logs, Pause Any COMPLETE # COMPLETE Read logs, Exit N/A # # Notes: # - The JOB_COMPLETE state forces us to do an extra pause and read any items that # got to Cloudwatch after the job was marked complete. last_describe_job_call = time.time() last_description = description while True: time.sleep(check_interval) sec = sec + check_interval state, last_description, last_describe_job_call = \ self.describe_training_job_with_log(job_name, positions, stream_names, instance_count, state, last_description, last_describe_job_call) if state == LogState.COMPLETE: break if max_ingestion_time and sec > 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', max_ingestion_time) if wait_for_completion: status = last_description['TrainingJobStatus'] if status in failed_states: reason = last_description.get('FailureReason', '(No reason provided)') raise AirflowException('Error training {}: {} Reason: {}'.format(job_name, status, reason)) billable_time = (last_description['TrainingEndTime'] - last_description['TrainingStartTime']) \ * instance_count self.log.info('Billable seconds:{}'.format(int(billable_time.total_seconds()) + 1))