def _create_job(config, internal_storage, executor_id, job_id, func, data, runtime_meta,
runtime_memory=None, extra_env=None, invoke_pool_threads=128, include_modules=[],
exclude_modules=[], original_func_name=None, remote_invocation=False,
original_total_tasks=None, execution_timeout=EXECUTION_TIMEOUT):
"""
:param func: the function to map over the data
:param iterdata: An iterable of input data
:param extra_env: Additional environment variables for CF environment. Default None.
:param extra_meta: Additional metadata to pass to CF. Default None.
:param remote_invocation: Enable remote invocation. Default False.
:param invoke_pool_threads: Number of threads to use to invoke.
:param data_all_as_one: upload the data as a single object. Default True
:param overwrite_invoke_args: Overwrite other args. Mainly used for testing.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:param original_func_name: Name of the function to invoke.
:return: A list with size `len(iterdata)` of futures for each job
:rtype: list of futures.
"""
log_level = os.getenv('CB_LOG_LEVEL')
runtime_name = config['pywren']['runtime']
if runtime_memory is None:
runtime_memory = config['pywren']['runtime_memory']
if original_func_name:
func_name = original_func_name
else:
func_name = func.__name__
if extra_env is not None:
extra_env = utils.convert_bools_to_string(extra_env)
if not data:
return []
host_job_meta = {}
job_description = {}
job_description['runtime_name'] = runtime_name
job_description['runtime_memory'] = int(runtime_memory)
job_description['execution_timeout'] = execution_timeout
job_description['func_name'] = func_name
job_description['extra_env'] = extra_env
job_description['total_calls'] = len(data)
job_description['invoke_pool_threads'] = invoke_pool_threads
job_description['job_id'] = job_id
job_description['remote_invocation'] = remote_invocation
job_description['original_total_calls'] = original_total_tasks
log_msg = 'ExecutorID {} | JobID {} - Serializing function and data'.format(executor_id, job_id)
logger.debug(log_msg)
# pickle func and all data (to capture module dependencies)
exclude_modules.extend(config['pywren'].get('exclude_modules', []))
include_modules_cfg = config['pywren'].get('include_modules', [])
if include_modules is not None and include_modules_cfg is not None:
include_modules.extend(include_modules_cfg)
serializer = SerializeIndependent(runtime_meta['preinstalls'])
func_and_data_ser, mod_paths = serializer([func] + data, include_modules, exclude_modules)
func_str = func_and_data_ser[0]
data_strs = func_and_data_ser[1:]
data_size_bytes = sum(len(x) for x in data_strs)
host_job_meta['agg_data'] = False
host_job_meta['data_size_bytes'] = data_size_bytes
log_msg = 'ExecutorID {} | JobID {} - Uploading function and data'.format(executor_id, job_id)
logger.info(log_msg)
if not log_level:
print(log_msg, end=' ')
if data_size_bytes < MAX_AGG_DATA_SIZE:
agg_data_key = create_agg_data_key(internal_storage.prefix, executor_id, job_id)
job_description['data_key'] = agg_data_key
agg_data_bytes, agg_data_ranges = _agg_data(data_strs)
job_description['data_ranges'] = agg_data_ranges
agg_upload_time = time.time()
internal_storage.put_data(agg_data_key, agg_data_bytes)
host_job_meta['agg_data'] = True
host_job_meta['data_upload_time'] = time.time() - agg_upload_time
host_job_meta['data_upload_timestamp'] = time.time()
else:
log_msg = ('ExecutorID {} | JobID {} - Total data exceeded '
'maximum size of {} bytes'.format(executor_id, job_id, MAX_AGG_DATA_SIZE))
raise Exception(log_msg)
module_data = create_module_data(mod_paths)
# Create func and upload
host_job_meta['func_name'] = func_name
func_module_str = pickle.dumps({'func': func_str, 'module_data': module_data}, -1)
host_job_meta['func_module_bytes'] = len(func_module_str)
func_upload_time = time.time()
func_key = create_func_key(internal_storage.prefix, executor_id, job_id)
job_description['func_key'] = func_key
internal_storage.put_func(func_key, func_module_str)
host_job_meta['func_upload_time'] = time.time() - func_upload_time
host_job_meta['func_upload_timestamp'] = time.time()
if not log_level:
func_and_data_size = utils.sizeof_fmt(host_job_meta['func_module_bytes']+host_job_meta['data_size_bytes'])
log_msg = '- Total: {}'.format(func_and_data_size)
print(log_msg)
job_description['host_job_meta'] = host_job_meta
return job_description
def _map(self, func, iterdata, extra_env=None, extra_meta=None, invoke_pool_threads=128,
data_all_as_one=True, overwrite_invoke_args=None, exclude_modules=None,
original_func_name=None, remote_invocation=False, original_iterdata_len=None,
job_max_runtime=wrenconfig.RUNTIME_TIMEOUT):
"""
:param func: the function to map over the data
:param iterdata: An iterable of input data
:param extra_env: Additional environment variables for CF environment. Default None.
:param extra_meta: Additional metadata to pass to CF. Default None.
:param remote_invocation: Enable remote invocation. Default False.
:param invoke_pool_threads: Number of threads to use to invoke.
:param data_all_as_one: upload the data as a single object. Default True
:param overwrite_invoke_args: Overwrite other args. Mainly used for testing.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:param original_func_name: Name of the function to invoke.
:return: A list with size `len(iterdata)` of futures for each job
:rtype: list of futures.
"""
if original_func_name:
func_name = original_func_name
else:
func_name = func.__name__
data = wrenutil.iterdata_as_list(iterdata)
if extra_env is not None:
extra_env = wrenutil.convert_bools_to_string(extra_env)
if not data:
return []
if self.map_item_limit is not None and len(data) > self.map_item_limit:
raise ValueError("len(data) ={}, exceeding map item limit of {}"
"consider mapping over a smaller"
"number of items".format(len(data),
self.map_item_limit))
# This allows multiple parameters in functions
data = wrenutil.verify_args(func, data)
callgroup_id = wrenutil.create_callgroup_id()
host_job_meta = {}
log_msg = 'Executor ID {} Serializing function and data'.format(self.executor_id)
logger.debug(log_msg)
# pickle func and all data (to capture module dependencies)
func_and_data_ser, mod_paths = self.serializer([func] + data)
func_str = func_and_data_ser[0]
data_strs = func_and_data_ser[1:]
data_size_bytes = sum(len(x) for x in data_strs)
agg_data_key = None
host_job_meta['agg_data'] = False
host_job_meta['data_size_bytes'] = data_size_bytes
log_msg = 'Executor ID {} Uploading function and data'.format(self.executor_id)
logger.info(log_msg)
if not self.log_level:
print(log_msg, end=' ')
if data_size_bytes < wrenconfig.MAX_AGG_DATA_SIZE and data_all_as_one:
agg_data_key = create_agg_data_key(self.internal_storage.prefix, self.executor_id, callgroup_id)
agg_data_bytes, agg_data_ranges = self.agg_data(data_strs)
agg_upload_time = time.time()
self.internal_storage.put_data(agg_data_key, agg_data_bytes)
host_job_meta['agg_data'] = True
host_job_meta['data_upload_time'] = time.time() - agg_upload_time
host_job_meta['data_upload_timestamp'] = time.time()
else:
log_msg = ('Executor ID {} Total data exceeded '
'maximum size of {} bytes'.format(self.executor_id,
wrenconfig.MAX_AGG_DATA_SIZE))
logger.warning(log_msg)
if exclude_modules:
for module in exclude_modules:
for mod_path in list(mod_paths):
if module in mod_path and mod_path in mod_paths:
mod_paths.remove(mod_path)
module_data = create_mod_data(mod_paths)
# Create func and upload
func_module_str = pickle.dumps({'func': func_str, 'module_data': module_data}, -1)
host_job_meta['func_module_bytes'] = len(func_module_str)
func_upload_time = time.time()
func_key = create_func_key(self.internal_storage.prefix, self.executor_id, callgroup_id)
self.internal_storage.put_func(func_key, func_module_str)
host_job_meta['func_upload_time'] = time.time() - func_upload_time
host_job_meta['func_upload_timestamp'] = time.time()
if not self.log_level:
func_and_data_size = wrenutil.sizeof_fmt(host_job_meta['func_module_bytes']+host_job_meta['data_size_bytes'])
log_msg = '- Total: {}'.format(func_and_data_size)
print(log_msg)
def invoke(data_str, executor_id, callgroup_id, call_id, func_key,
host_job_meta, agg_data_key=None, data_byte_range=None):
data_key, output_key, status_key = create_keys(self.internal_storage.prefix,
executor_id, callgroup_id, call_id)
host_job_meta['job_invoke_timestamp'] = time.time()
if agg_data_key is None:
data_upload_time = time.time()
self.internal_storage.put_data(data_key, data_str)
data_upload_time = time.time() - data_upload_time
host_job_meta['data_upload_time'] = data_upload_time
host_job_meta['data_upload_timestamp'] = time.time()
data_key = data_key
else:
data_key = agg_data_key
return self.invoke_with_keys(func_key, data_key,
output_key, status_key,
executor_id, callgroup_id,
call_id, extra_env,
extra_meta, data_byte_range,
host_job_meta.copy(),
job_max_runtime,
overwrite_invoke_args=overwrite_invoke_args)
N = len(data)
call_futures = []
if remote_invocation and original_iterdata_len > 1:
log_msg = 'Executor ID {} Starting {} remote invocation function: Spawning {}() - Total: {} activations'.format(self.executor_id, N, func_name,
original_iterdata_len)
else:
log_msg = 'Executor ID {} Starting function invocation: {}() - Total: {} activations'.format(self.executor_id, func_name, N)
logger.info(log_msg)
if not self.log_level:
print(log_msg)
with ThreadPoolExecutor(max_workers=invoke_pool_threads) as executor:
for i in range(N):
call_id = "{:05d}".format(i)
data_byte_range = None
if agg_data_key is not None:
data_byte_range = agg_data_ranges[i]
future = executor.submit(invoke, data_strs[i], self.executor_id,
callgroup_id, call_id, func_key,
host_job_meta.copy(),
agg_data_key,
data_byte_range)
call_futures.append(future)
res = [ft.result() for ft in call_futures]
return res
def _create_job(config,
internal_storage,
executor_id,
job_id,
func,
data,
runtime_meta,
runtime_memory=None,
extra_env=None,
invoke_pool_threads=128,
include_modules=[],
exclude_modules=[],
execution_timeout=None,
job_created_timestamp=None):
"""
:param func: the function to map over the data
:param iterdata: An iterable of input data
:param extra_env: Additional environment variables for CF environment. Default None.
:param extra_meta: Additional metadata to pass to CF. Default None.
:param remote_invocation: Enable remote invocation. Default False.
:param invoke_pool_threads: Number of threads to use to invoke.
:param data_all_as_one: upload the data as a single object. Default True
:param overwrite_invoke_args: Overwrite other args. Mainly used for testing.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: A list with size `len(iterdata)` of futures for each job
:rtype: list of futures.
"""
log_level = os.getenv('PYWREN_LOGLEVEL')
runtime_name = config['pywren']['runtime']
if runtime_memory is None:
runtime_memory = config['pywren']['runtime_memory']
ext_env = {} if extra_env is None else extra_env.copy()
if ext_env:
ext_env = utils.convert_bools_to_string(ext_env)
logger.debug("Extra environment vars {}".format(ext_env))
if not data:
return []
if execution_timeout is None:
execution_timeout = config['pywren']['runtime_timeout'] - 5
job_description = {}
job_description['runtime_name'] = runtime_name
job_description['runtime_memory'] = int(runtime_memory)
job_description['execution_timeout'] = execution_timeout
job_description['function_name'] = func.__name__
job_description['extra_env'] = ext_env
job_description['total_calls'] = len(data)
job_description['invoke_pool_threads'] = invoke_pool_threads
job_description['executor_id'] = executor_id
job_description['job_id'] = job_id
exclude_modules_cfg = config['pywren'].get('exclude_modules', [])
include_modules_cfg = config['pywren'].get('include_modules', [])
exc_modules = set()
inc_modules = set()
if exclude_modules_cfg:
exc_modules.update(exclude_modules_cfg)
if exclude_modules:
exc_modules.update(exclude_modules)
if include_modules_cfg is not None:
inc_modules.update(include_modules_cfg)
if include_modules_cfg is None and not include_modules:
inc_modules = None
if include_modules is not None and include_modules:
inc_modules.update(include_modules)
if include_modules is None:
inc_modules = None
host_job_meta = {'job_created_timestamp': job_created_timestamp}
logger.debug(
'ExecutorID {} | JobID {} - Serializing function and data'.format(
executor_id, job_id))
serializer = SerializeIndependent(runtime_meta['preinstalls'])
func_and_data_ser, mod_paths = serializer([func] + data, inc_modules,
exc_modules)
data_strs = func_and_data_ser[1:]
data_size_bytes = sum(len(x) for x in data_strs)
module_data = create_module_data(mod_paths)
func_str = func_and_data_ser[0]
func_module_str = pickle.dumps(
{
'func': func_str,
'module_data': module_data
}, -1)
func_module_size_bytes = len(func_module_str)
total_size = utils.sizeof_fmt(data_size_bytes + func_module_size_bytes)
host_job_meta['data_size_bytes'] = data_size_bytes
host_job_meta['func_module_size_bytes'] = func_module_size_bytes
if 'data_limit' in config['pywren']:
data_limit = config['pywren']['data_limit']
else:
data_limit = MAX_AGG_DATA_SIZE
if data_limit and data_size_bytes > data_limit * 1024**2:
log_msg = (
'ExecutorID {} | JobID {} - Total data exceeded maximum size '
'of {}'.format(executor_id, job_id,
sizeof_fmt(data_limit * 1024**2)))
raise Exception(log_msg)
log_msg = ('ExecutorID {} | JobID {} - Uploading function and data '
'- Total: {}'.format(executor_id, job_id, total_size))
print(log_msg) if not log_level else logger.info(log_msg)
# Upload data
data_key = create_agg_data_key(JOBS_PREFIX, executor_id, job_id)
job_description['data_key'] = data_key
data_bytes, data_ranges = utils.agg_data(data_strs)
job_description['data_ranges'] = data_ranges
data_upload_time = time.time()
internal_storage.put_data(data_key, data_bytes)
host_job_meta['data_upload_time'] = time.time() - data_upload_time
host_job_meta['data_upload_timestamp'] = time.time()
# Upload function and modules
func_upload_time = time.time()
func_key = create_func_key(JOBS_PREFIX, executor_id, job_id)
job_description['func_key'] = func_key
internal_storage.put_func(func_key, func_module_str)
host_job_meta['func_upload_time'] = time.time() - func_upload_time
host_job_meta['func_upload_timestamp'] = time.time()
job_description['metadata'] = host_job_meta
return job_description
