def attach(config, backend, start, debug):
"""Create or attach to a SSH session on Lithops master VM"""
if config:
config = load_yaml_config(config)
log_level = logging.INFO if not debug else logging.DEBUG
setup_lithops_logger(log_level)
config_ow = set_config_ow(backend)
config = default_config(config, config_ow)
if config['lithops']['mode'] != STANDALONE:
raise Exception(
'lithops attach method is only available for standalone backends')
compute_config = extract_standalone_config(config)
compute_handler = StandaloneHandler(compute_config)
compute_handler.init()
if start:
compute_handler.backend.master.start()
master_ip = compute_handler.backend.master.get_public_ip()
user = compute_handler.backend.master.ssh_credentials['username']
key_file = compute_handler.backend.master.ssh_credentials[
'key_filename'] or '~/.ssh/id_rsa'
key_file = os.path.abspath(os.path.expanduser(key_file))
if not os.path.exists(key_file):
raise Exception(f'Private key file {key_file} does not exists')
print(f'Got master VM public IP address: {master_ip}')
print(f'Loading ssh private key from: {key_file}')
print('Creating SSH Connection to lithops master VM')
cmd = (
'ssh -o "UserKnownHostsFile=/dev/null" -o "StrictHostKeyChecking=no" '
f'-i {key_file} {user}@{master_ip}')
compute_handler.backend.master.wait_ready()
sp.run(shlex.split(cmd))
class FunctionExecutor:
"""
Executor abstract class that contains the common logic
for the Localhost, Serverless and Standalone executors
"""
def __init__(self, type=None, mode=None, config=None, backend=None, storage=None,
runtime=None, runtime_memory=None, rabbitmq_monitor=None,
workers=None, remote_invoker=None, log_level=None):
mode = mode or type
if mode is None:
config = default_config(copy.deepcopy(config))
mode = config['lithops']['mode']
if mode not in [LOCALHOST, SERVERLESS, STANDALONE]:
raise Exception("Function executor mode must be one of '{}', '{}' "
"or '{}'".format(LOCALHOST, SERVERLESS, STANDALONE))
if log_level:
setup_logger(log_level)
if type is not None:
logger.warning("'type' parameter is deprecated and it will be removed"
"in future releases. Use 'mode' parameter instead")
config_ow = {'lithops': {'mode': mode}, mode: {}}
if runtime is not None:
config_ow[mode]['runtime'] = runtime
if backend is not None:
config_ow[mode]['backend'] = backend
if runtime_memory is not None:
config_ow[mode]['runtime_memory'] = int(runtime_memory)
if remote_invoker is not None:
config_ow[mode]['remote_invoker'] = remote_invoker
if storage is not None:
config_ow['lithops']['storage'] = storage
if workers is not None:
config_ow['lithops']['workers'] = workers
if rabbitmq_monitor is not None:
config_ow['lithops']['rabbitmq_monitor'] = rabbitmq_monitor
self.config = default_config(copy.deepcopy(config), config_ow)
self.log_active = logger.getEffectiveLevel() != logging.WARNING
self.is_lithops_worker = is_lithops_worker()
self.executor_id = create_executor_id()
self.data_cleaner = self.config['lithops'].get('data_cleaner', True)
if self.data_cleaner and not self.is_lithops_worker:
spawn_cleaner = int(self.executor_id.split('-')[1]) == 0
atexit.register(self.clean, spawn_cleaner=spawn_cleaner,
clean_cloudobjects=False)
self.rabbitmq_monitor = self.config['lithops'].get('rabbitmq_monitor', False)
if self.rabbitmq_monitor:
if 'rabbitmq' in self.config and 'amqp_url' in self.config['rabbitmq']:
self.rabbit_amqp_url = self.config['rabbitmq'].get('amqp_url')
else:
raise Exception("You cannot use rabbitmq_mnonitor since "
"'amqp_url' is not present in configuration")
storage_config = extract_storage_config(self.config)
self.internal_storage = InternalStorage(storage_config)
self.storage = self.internal_storage.storage
self.futures = []
self.cleaned_jobs = set()
self.total_jobs = 0
self.last_call = None
if mode == LOCALHOST:
localhost_config = extract_localhost_config(self.config)
self.compute_handler = LocalhostHandler(localhost_config)
self.invoker = StandaloneInvoker(self.config,
self.executor_id,
self.internal_storage,
self.compute_handler)
elif mode == SERVERLESS:
serverless_config = extract_serverless_config(self.config)
self.compute_handler = ServerlessHandler(serverless_config,
storage_config)
self.invoker = ServerlessInvoker(self.config,
self.executor_id,
self.internal_storage,
self.compute_handler)
elif mode == STANDALONE:
standalone_config = extract_standalone_config(self.config)
self.compute_handler = StandaloneHandler(standalone_config)
self.invoker = StandaloneInvoker(self.config,
self.executor_id,
self.internal_storage,
self.compute_handler)
logger.info('{} Executor created with ID: {}'
.format(mode.capitalize(), self.executor_id))
def __enter__(self):
return self
def _create_job_id(self, call_type):
job_id = str(self.total_jobs).zfill(3)
self.total_jobs += 1
return '{}{}'.format(call_type, job_id)
def call_async(self, func, data, extra_env=None, runtime_memory=None,
timeout=None, include_modules=[], exclude_modules=[]):
"""
For running one function execution asynchronously
:param func: the function to map over the data
:param data: input data
:param extra_env: Additional env variables for action environment
:param runtime_memory: Memory to use to run the function
:param timeout: Time that the functions have to complete their
execution before raising a timeout
:param include_modules: Explicitly pickle these dependencies
:param exclude_modules: Explicitly keep these modules from pickled
dependencies
:return: future object.
"""
job_id = self._create_job_id('A')
self.last_call = 'call_async'
runtime_meta = self.invoker.select_runtime(job_id, runtime_memory)
job = create_map_job(self.config, self.internal_storage,
self.executor_id, job_id,
map_function=func,
iterdata=[data],
runtime_meta=runtime_meta,
runtime_memory=runtime_memory,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout)
futures = self.invoker.run(job)
self.futures.extend(futures)
return futures[0]
def map(self, map_function, map_iterdata, extra_args=None, extra_env=None,
runtime_memory=None, chunk_size=None, chunk_n=None, timeout=None,
invoke_pool_threads=500, include_modules=[], exclude_modules=[]):
"""
For running multiple function executions asynchronously
:param map_function: the function to map over the data
:param map_iterdata: An iterable of input data
:param extra_args: Additional args to pass to the function activations
:param extra_env: Additional env variables for action environment
:param runtime_memory: Memory to use to run the function
:param chunk_size: the size of the data chunks to split each object.
'None' for processing the whole file in one function
activation.
:param chunk_n: Number of chunks to split each object. 'None' for
processing the whole file in one function activation
:param remote_invocation: Enable or disable remote_invocation mechanism
:param timeout: Time that the functions have to complete their execution
before raising a timeout
:param invoke_pool_threads: Number of threads to use to invoke
:param include_modules: Explicitly pickle these dependencies
:param exclude_modules: Explicitly keep these modules from pickled
dependencies
:return: A list with size `len(iterdata)` of futures.
"""
job_id = self._create_job_id('M')
self.last_call = 'map'
runtime_meta = self.invoker.select_runtime(job_id, runtime_memory)
job = create_map_job(self.config, self.internal_storage,
self.executor_id, job_id,
map_function=map_function,
iterdata=map_iterdata,
runtime_meta=runtime_meta,
runtime_memory=runtime_memory,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout,
extra_args=extra_args,
obj_chunk_size=chunk_size,
obj_chunk_number=chunk_n,
invoke_pool_threads=invoke_pool_threads)
futures = self.invoker.run(job)
self.futures.extend(futures)
return futures
def map_reduce(self, map_function, map_iterdata, reduce_function,
extra_args=None, extra_env=None, map_runtime_memory=None,
reduce_runtime_memory=None, chunk_size=None, chunk_n=None,
timeout=None, invoke_pool_threads=500, reducer_one_per_object=False,
reducer_wait_local=False, include_modules=[], exclude_modules=[]):
"""
Map the map_function over the data and apply the reduce_function across all futures.
This method is executed all within CF.
:param map_function: the function to map over the data
:param map_iterdata: An iterable of input data
:param reduce_function: the function to reduce over the futures
:param extra_env: Additional environment variables for action environment. Default None.
:param extra_args: Additional arguments to pass to function activation. Default None.
:param map_runtime_memory: Memory to use to run the map function. Default None (loaded from config).
:param reduce_runtime_memory: Memory to use to run the reduce function. Default None (loaded from config).
:param chunk_size: the size of the data chunks to split each object. 'None' for processing
the whole file in one function activation.
:param chunk_n: Number of chunks to split each object. 'None' for processing the whole
file in one function activation.
:param remote_invocation: Enable or disable remote_invocation mechanism. Default 'False'
:param timeout: Time that the functions have to complete their execution before raising a timeout.
:param reducer_one_per_object: Set one reducer per object after running the partitioner
:param reducer_wait_local: Wait for results locally
:param invoke_pool_threads: Number of threads to use to invoke.
:param include_modules: Explicitly pickle these dependencies.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: A list with size `len(map_iterdata)` of futures.
"""
self.last_call = 'map_reduce'
map_job_id = self._create_job_id('M')
runtime_meta = self.invoker.select_runtime(map_job_id, map_runtime_memory)
map_job = create_map_job(self.config, self.internal_storage,
self.executor_id, map_job_id,
map_function=map_function,
iterdata=map_iterdata,
runtime_meta=runtime_meta,
runtime_memory=map_runtime_memory,
extra_args=extra_args,
extra_env=extra_env,
obj_chunk_size=chunk_size,
obj_chunk_number=chunk_n,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout,
invoke_pool_threads=invoke_pool_threads)
map_futures = self.invoker.run(map_job)
self.futures.extend(map_futures)
if reducer_wait_local:
self.wait(fs=map_futures)
reduce_job_id = map_job_id.replace('M', 'R')
runtime_meta = self.invoker.select_runtime(reduce_job_id, reduce_runtime_memory)
reduce_job = create_reduce_job(self.config, self.internal_storage,
self.executor_id, reduce_job_id,
reduce_function, map_job, map_futures,
runtime_meta=runtime_meta,
runtime_memory=reduce_runtime_memory,
reducer_one_per_object=reducer_one_per_object,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules)
reduce_futures = self.invoker.run(reduce_job)
self.futures.extend(reduce_futures)
for f in map_futures:
f._produce_output = False
return map_futures + reduce_futures
def wait(self, fs=None, throw_except=True, return_when=ALL_COMPLETED,
download_results=False, timeout=None, THREADPOOL_SIZE=128,
WAIT_DUR_SEC=1):
"""
Wait for the Future instances (possibly created by different Executor instances)
given by fs to complete. Returns a named 2-tuple of sets. The first set, named done,
contains the futures that completed (finished or cancelled futures) before the wait
completed. The second set, named not_done, contains the futures that did not complete
(pending or running futures). timeout can be used to control the maximum number of
seconds to wait before returning.
:param fs: Futures list. Default None
:param throw_except: Re-raise exception if call raised. Default True.
:param return_when: One of `ALL_COMPLETED`, `ANY_COMPLETED`, `ALWAYS`
:param download_results: Download results. Default false (Only get statuses)
:param timeout: Timeout of waiting for results.
:param THREADPOOL_SIZE: Number of threads to use. Default 64
:param WAIT_DUR_SEC: Time interval between each check.
:return: `(fs_done, fs_notdone)`
where `fs_done` is a list of futures that have completed
and `fs_notdone` is a list of futures that have not completed.
:rtype: 2-tuple of list
"""
futures = fs or self.futures
if type(futures) != list:
futures = [futures]
if not futures:
raise Exception('You must run the call_async(), map() or map_reduce(), or provide'
' a list of futures before calling the wait()/get_result() method')
if download_results:
msg = 'ExecutorID {} - Getting results...'.format(self.executor_id)
fs_done = [f for f in futures if f.done]
fs_not_done = [f for f in futures if not f.done]
else:
msg = 'ExecutorID {} - Waiting for functions to complete...'.format(self.executor_id)
fs_done = [f for f in futures if f.ready or f.done]
fs_not_done = [f for f in futures if not f.ready and not f.done]
if not fs_not_done:
return fs_done, fs_not_done
logger.info(msg)
if not self.log_active:
print(msg)
if is_unix_system() and timeout is not None:
logger.debug('Setting waiting timeout to {} seconds'.format(timeout))
error_msg = 'Timeout of {} seconds exceeded waiting for function activations to finish'.format(timeout)
signal.signal(signal.SIGALRM, partial(timeout_handler, error_msg))
signal.alarm(timeout)
pbar = None
error = False
if not self.is_lithops_worker and not self.log_active:
from tqdm.auto import tqdm
if is_notebook():
pbar = tqdm(bar_format='{n}/|/ {n_fmt}/{total_fmt}', total=len(fs_not_done)) # ncols=800
else:
print()
pbar = tqdm(bar_format=' {l_bar}{bar}| {n_fmt}/{total_fmt} ', total=len(fs_not_done), disable=False)
try:
if self.rabbitmq_monitor:
logger.info('Using RabbitMQ to monitor function activations')
wait_rabbitmq(futures, self.internal_storage, rabbit_amqp_url=self.rabbit_amqp_url,
download_results=download_results, throw_except=throw_except,
pbar=pbar, return_when=return_when, THREADPOOL_SIZE=THREADPOOL_SIZE)
else:
wait_storage(futures, self.internal_storage, download_results=download_results,
throw_except=throw_except, return_when=return_when, pbar=pbar,
THREADPOOL_SIZE=THREADPOOL_SIZE, WAIT_DUR_SEC=WAIT_DUR_SEC)
except KeyboardInterrupt as e:
if download_results:
not_dones_call_ids = [(f.job_id, f.call_id) for f in futures if not f.done]
else:
not_dones_call_ids = [(f.job_id, f.call_id) for f in futures if not f.ready and not f.done]
msg = ('ExecutorID {} - Cancelled - Total Activations not done: {}'
.format(self.executor_id, len(not_dones_call_ids)))
if pbar:
pbar.close()
print()
logger.info(msg)
if not self.log_active:
print(msg)
error = True
raise e
except Exception as e:
error = True
raise e
finally:
self.invoker.stop()
if is_unix_system():
signal.alarm(0)
if pbar and not pbar.disable:
pbar.close()
if not is_notebook():
print()
if self.data_cleaner and not self.is_lithops_worker:
self.clean(clean_cloudobjects=False)
if not fs and error and is_notebook():
del self.futures[len(self.futures)-len(futures):]
if download_results:
fs_done = [f for f in futures if f.done]
fs_notdone = [f for f in futures if not f.done]
else:
fs_done = [f for f in futures if f.ready or f.done]
fs_notdone = [f for f in futures if not f.ready and not f.done]
return fs_done, fs_notdone
def get_result(self, fs=None, throw_except=True, timeout=None,
THREADPOOL_SIZE=128, WAIT_DUR_SEC=1):
"""
For getting the results from all function activations
:param fs: Futures list. Default None
:param throw_except: Reraise exception if call raised. Default True.
:param verbose: Shows some information prints. Default False
:param timeout: Timeout for waiting for results.
:param THREADPOOL_SIZE: Number of threads to use. Default 128
:param WAIT_DUR_SEC: Time interval between each check.
:return: The result of the future/s
"""
fs_done, _ = self.wait(fs=fs, throw_except=throw_except,
timeout=timeout, download_results=True,
THREADPOOL_SIZE=THREADPOOL_SIZE,
WAIT_DUR_SEC=WAIT_DUR_SEC)
result = []
fs_done = [f for f in fs_done if not f.futures and f._produce_output]
for f in fs_done:
if fs:
# Process futures provided by the user
result.append(f.result(throw_except=throw_except,
internal_storage=self.internal_storage))
elif not fs and not f._read:
# Process internally stored futures
result.append(f.result(throw_except=throw_except,
internal_storage=self.internal_storage))
f._read = True
logger.debug("ExecutorID {} Finished getting results"
.format(self.executor_id))
if len(result) == 1 and self.last_call != 'map':
return result[0]
return result
def plot(self, fs=None, dst=None):
"""
Creates timeline and histogram of the current execution in dst_dir.
:param dst_dir: destination folder to save .png plots.
:param dst_file_name: prefix name of the file.
:param fs: list of futures.
"""
ftrs = self.futures if not fs else fs
if type(ftrs) != list:
ftrs = [ftrs]
ftrs_to_plot = [f for f in ftrs if (f.ready or f.done) and not f.error]
if not ftrs_to_plot:
logger.debug('ExecutorID {} - No futures ready to plot'
.format(self.executor_id))
return
logging.getLogger('matplotlib').setLevel(logging.WARNING)
from lithops.plots import create_timeline, create_histogram
msg = 'ExecutorID {} - Creating execution plots'.format(self.executor_id)
logger.info(msg)
if not self.log_active:
print(msg)
create_timeline(ftrs_to_plot, dst)
create_histogram(ftrs_to_plot, dst)
def clean(self, fs=None, cs=None, clean_cloudobjects=True, spawn_cleaner=True):
"""
Deletes all the temp files from storage. These files include the function,
the data serialization and the function invocation results. It can also clean
cloudobjects.
:param fs: list of futures to clean
:param cs: list of cloudobjects to clean
:param clean_cloudobjects: true/false
:param spawn_cleaner true/false
"""
os.makedirs(CLEANER_DIR, exist_ok=True)
def save_data_to_clean(data):
with tempfile.NamedTemporaryFile(dir=CLEANER_DIR, delete=False) as temp:
pickle.dump(data, temp)
if cs:
data = {'cos_to_clean': list(cs),
'storage_config': self.internal_storage.get_storage_config()}
save_data_to_clean(data)
if not fs:
return
futures = fs or self.futures
futures = [futures] if type(futures) != list else futures
present_jobs = {create_job_key(f.executor_id, f.job_id) for f in futures
if f.executor_id.count('-') == 1}
jobs_to_clean = present_jobs - self.cleaned_jobs
if jobs_to_clean:
logger.info("ExecutorID {} - Cleaning temporary data"
.format(self.executor_id))
data = {'jobs_to_clean': jobs_to_clean,
'clean_cloudobjects': clean_cloudobjects,
'storage_config': self.internal_storage.get_storage_config()}
save_data_to_clean(data)
self.cleaned_jobs.update(jobs_to_clean)
if (jobs_to_clean or cs) and spawn_cleaner:
log_file = open(CLEANER_LOG_FILE, 'a')
cmdstr = '{} -m lithops.scripts.cleaner'.format(sys.executable)
sp.Popen(cmdstr, shell=True, stdout=log_file, stderr=log_file)
def dismantle(self):
self.compute_handler.dismantle()
def init(self):
self.compute_handler.init()
def __exit__(self, exc_type, exc_value, traceback):
self.invoker.stop()