def _deserialize_output(self, task):
"""
Deserialize the output from a task.
Parameters
----------
Task definition of interest.
Returns
-------
The output of the run-time task associated with the task definition.
"""
filepath = self._task_output_paths[task]
non_hdfs_file_path = filepath
# Unpickler has no support for passing in additional HADOOP_CONF_DIR
# so we download HDFS folder first before calling to unpickler
if _file_util.is_hdfs_path(filepath):
non_hdfs_file_path = _make_temp_directory("job_output_")
_file_util.download_from_hdfs(filepath, non_hdfs_file_path,
hadoop_conf_dir=self.environment.hadoop_conf_dir, is_dir = True)
unpickler = gl_pickle.GLUnpickler(non_hdfs_file_path)
# We cannot delete this temporary file path becaue SFrame lazily load
# the content from disk. But the temporary folder will be removed
# eventually when the python session goes away
return unpickler.load()
def prepare_job_files(environment, job):
'''
Prepare all files needed to run a job in EC2 cluster
'''
# Update job working directory
s3_job_path_root = Ec2ExecutionEnvironment.create_job_home_dir(
environment, job.name)
_job.Ec2Job._update_exec_dir(job, s3_job_path_root)
__LOGGER__.info('Job working directory: %s' % s3_job_path_root)
# Prepare all files locally and then upload to S3
temp_local_folder = _make_temp_directory(prefix='ec2_job_')
try:
ExecutionEnvironment.prepare_job_exec_dir(job, temp_local_folder)
_file_util.upload_to_s3(
temp_local_folder,
s3_job_path_root,
is_dir=True,
aws_credentials=environment.ec2_config.get_credentials(),
silent=True)
finally:
_shutil.rmtree(temp_local_folder)
def _deserialize_output(self, task):
"""
Deserialize the output from a task.
Parameters
----------
Task definition of interest.
Returns
-------
The output of the run-time task associated with the task definition.
"""
filepath = self._task_output_paths[task]
non_hdfs_file_path = filepath
# Unpickler has no support for passing in additional HADOOP_CONF_DIR
# so we download HDFS folder first before calling to unpickler
if _file_util.is_hdfs_path(filepath):
non_hdfs_file_path = _make_temp_directory("job_output_")
_file_util.download_from_hdfs(filepath, non_hdfs_file_path,
hadoop_conf_dir=self.environment.hadoop_conf_dir, is_dir = True)
unpickler = gl_pickle.GLUnpickler(non_hdfs_file_path)
# We cannot delete this temporary file path becaue SFrame lazily load
# the content from disk. But the temporary folder will be removed
# eventually when the python session goes away
return unpickler.load()
def _get_dependent_file_path(cls):
'''We put all dependent_files under a fixed path so that newer file may
overwrite older files to guarantee the modules can be reloaded correctly
'''
if PredictiveObject._depedent_file_path is None:
PredictiveObject._depedent_file_path = _make_temp_directory(prefix='predictive_object_files')
sys.path.insert(1, PredictiveObject._depedent_file_path)
return PredictiveObject._depedent_file_path
def submit_yarn_application(environment,
job_working_dir,
silent=False,
native_conda=False):
'''
Submit a yarn application
'''
# Create temporary directory to write tmp files in for the job
temp_local_directory = _make_temp_directory(prefix='hadoop_job_')
req_file_path = None
if hasattr(environment, 'additional_packages'):
req_file_path = ExecutionEnvironment.write_required_packages(
temp_local_directory, environment.additional_packages)
# build actual hadoop command
hadoop_cmd = HadoopExecutionEnvironment._build_hadoop_cmd(
environment,
job_working_dir,
req_file_path,
native_conda=native_conda)
# call hadoop command
if not silent:
__LOGGER__.info(
"Submitting job to Hadoop cluster using command= \n%s" %
hadoop_cmd)
proc = _subprocess.Popen(hadoop_cmd,
shell=True,
stderr=_subprocess.STDOUT,
stdout=_subprocess.PIPE)
app_info = HadoopExecutionEnvironment._parse_hadoop_cmd_output(
proc, silent)
if 'app_id' not in app_info or not app_info['app_id']:
__LOGGER__.info(
"Submitting job to Hadoop cluster using command= \n%s" %
hadoop_cmd)
raise RuntimeError(
"Error submitting application or determining application id. Please confirm that you"
" can correctly access the Hadoop environment specified (try running the above"
" command from a terminal to see more diagnostic output).")
else:
_shutil.rmtree(temp_local_directory)
return app_info
def submit_yarn_application(environment, job_working_dir, silent = False,
native_conda = False):
'''
Submit a yarn application
'''
# Create temporary directory to write tmp files in for the job
temp_local_directory = _make_temp_directory(prefix='hadoop_job_')
req_file_path = None
if hasattr(environment, 'additional_packages'):
req_file_path = ExecutionEnvironment.write_required_packages(
temp_local_directory, environment.additional_packages)
# build actual hadoop command
hadoop_cmd = HadoopExecutionEnvironment._build_hadoop_cmd(
environment,
job_working_dir,
req_file_path,
native_conda = native_conda)
# call hadoop command
if not silent:
__LOGGER__.info("Submitting job to Hadoop cluster using command= \n%s" % hadoop_cmd)
proc = _subprocess.Popen(hadoop_cmd, shell=True,
stderr=_subprocess.STDOUT,
stdout=_subprocess.PIPE)
app_info = HadoopExecutionEnvironment._parse_hadoop_cmd_output(proc, silent)
if 'app_id' not in app_info or not app_info['app_id']:
__LOGGER__.info("Submitting job to Hadoop cluster using command= \n%s" % hadoop_cmd)
raise RuntimeError("Error submitting application or determining application id. Please confirm that you"
" can correctly access the Hadoop environment specified (try running the above"
" command from a terminal to see more diagnostic output).")
else:
_shutil.rmtree(temp_local_directory)
return app_info
def prepare_job_files(environment, job):
'''
Upload all job related information to HDFS so that it can be executed
remotely
'''
exec_dir = HadoopExecutionEnvironment.create_job_home_dir(environment, job.name)
_job.HadoopJob._update_exec_dir(job, exec_dir)
logging.info("Job working directory: %s" % job._exec_dir)
temp_job_folder = _make_temp_directory(prefix='hadoop_job_')
try:
ExecutionEnvironment.prepare_job_exec_dir(job, temp_job_folder)
# Move everything to HDFS
_file_util.upload_folder_to_hdfs(
temp_job_folder,
exec_dir,
hadoop_conf_dir = environment.hadoop_conf_dir)
finally:
_shutil.rmtree(temp_job_folder)
def prepare_job_files(environment, job):
'''
Upload all job related information to HDFS so that it can be executed
remotely
'''
exec_dir = HadoopExecutionEnvironment.create_job_home_dir(
environment, job.name)
_job.HadoopJob._update_exec_dir(job, exec_dir)
logging.info("Job working directory: %s" % job._exec_dir)
temp_job_folder = _make_temp_directory(prefix='hadoop_job_')
try:
ExecutionEnvironment.prepare_job_exec_dir(job, temp_job_folder)
# Move everything to HDFS
_file_util.upload_folder_to_hdfs(
temp_job_folder,
exec_dir,
hadoop_conf_dir=environment.hadoop_conf_dir)
finally:
_shutil.rmtree(temp_job_folder)
def prepare_job_files(environment, job):
'''
Prepare all files needed to run a job in EC2 cluster
'''
# Update job working directory
s3_job_path_root = Ec2ExecutionEnvironment.create_job_home_dir(environment, job.name)
_job.Ec2Job._update_exec_dir(job, s3_job_path_root)
__LOGGER__.info('Job working directory: %s' % s3_job_path_root)
# Prepare all files locally and then upload to S3
temp_local_folder = _make_temp_directory(prefix='ec2_job_')
try:
ExecutionEnvironment.prepare_job_exec_dir(job, temp_local_folder)
_file_util.upload_to_s3(
temp_local_folder,
s3_job_path_root,
is_dir = True,
aws_credentials = environment.ec2_config.get_credentials(),
silent = True)
finally:
_shutil.rmtree(temp_local_folder)
