def _exp_startup_callback(self) -> None:
"""Registers the hp config to tensorboard upon experiment startup."""
tensorboard._register(EnvSing.get_instance().get_logdir(
self.app_id, self.run_id))
tensorboard._write_hparams_config(
EnvSing.get_instance().get_logdir(self.app_id, self.run_id),
self.config.searchspace,
)
def register_environment(app_id, run_id):
"""Validates IDs and creates an experiment folder in the fs.
Args:
:app_id: Application ID
:run_id: Current experiment run ID
Returns: (app_id, run_id) with the updated IDs.
"""
app_id = str(find_spark().sparkContext.applicationId)
app_id, run_id = validate_ml_id(app_id, run_id)
set_ml_id(app_id, run_id)
# Create experiment directory.
EnvSing.get_instance().create_experiment_dir(app_id, run_id)
tensorboard._register(EnvSing.get_instance().get_logdir(app_id, run_id))
return app_id, run_id
def wrapper_function(_: Any) -> None:
"""Patched function from dist_executor_fn factory.
:param _: Necessary catch for the iterator given by Spark to the
function upon foreach calls. Can safely be disregarded.
"""
EnvSing.get_instance().set_ml_id(app_id, run_id)
partition_id, _ = util.get_partition_attempt_id()
client = Client(server_addr, partition_id, 0, hb_interval, secret)
log_file = log_dir + "/executor_" + str(partition_id) + ".log"
builtin_print = __builtin__.print
reporter = Reporter(log_file, partition_id, 0, builtin_print)
def maggy_print(*args, **kwargs):
builtin_print(*args, **kwargs)
reporter.log(" ".join(str(x) for x in args), True)
__builtin__.print = maggy_print
try:
_register_with_servers(client, reporter, partition_id)
tb_logdir, trial_log_file = _setup_logging(reporter, log_dir)
reporter.log("Awaiting worker reservations.", True)
client.await_reservations()
reporter.log("Reservations complete, configuring PyTorch.", True)
master_config = client.get_exec_config()[0]
if not master_config:
reporter.log("RuntimeError: PyTorch registration failed.",
True)
raise RuntimeError("PyTorch registration failed.")
addr, port = master_config["host_port"].split(":")
torch_config = {
"MASTER_ADDR": addr,
"MASTER_PORT": port,
"WORLD_SIZE": str(master_config["num_executors"]),
"RANK": str(partition_id),
"LOCAL_RANK": str(0), # DeepSpeed requires local rank.
"NCCL_BLOCKING_WAIT": "1",
"NCCL_DEBUG": "INFO",
}
tensorboard._register(tb_logdir)
reporter.log(f"Torch config is {torch_config}", True)
_setup_torch_env(torch_config)
_sanitize_config(config)
_init_cluster(timeout=60, random_seed=0)
module = _wrap_module_dispatcher(config)
_monkey_patch_pytorch(config.zero_lvl)
reporter.log("Starting distributed training.", True)
sig = inspect.signature(train_fn)
if sig.parameters.get("reporter", None):
retval = train_fn(
module=module,
hparams=config.hparams,
train_set=config.train_set,
test_set=config.test_set,
reporter=reporter,
)
else:
retval = train_fn(
module=module,
hparams=config.hparams,
train_set=config.train_set,
test_set=config.test_set,
)
retval = util.handle_return_val(retval, tb_logdir, "Metric",
trial_log_file)
dist.barrier(
) # Don't exit until all executors are done (else NCCL crashes)
reporter.log("Finished distributed training.", True)
client.finalize_metric(retval, reporter)
except: # noqa: E722
reporter.log(traceback.format_exc())
raise
finally:
reporter.close_logger()
client.stop()
client.close()
def _wrapper_fun(iter):
"""
Wraps the user supplied training function in order to be passed to the
Spark Executors.
Args:
iter:
Returns:
"""
experiment_utils._set_ml_id(app_id, run_id)
# get task context information to determine executor identifier
partition_id, task_attempt = util.get_partition_attempt_id()
client = rpc.Client(server_addr, partition_id, task_attempt,
hb_interval, secret)
log_file = (log_dir + "/executor_" + str(partition_id) + "_" +
str(task_attempt) + ".log")
# save the builtin print
original_print = __builtin__.print
reporter = Reporter(log_file, partition_id, task_attempt,
original_print)
def maggy_print(*args, **kwargs):
"""Maggy custom print() function."""
original_print(*args, **kwargs)
reporter.log(" ".join(str(x) for x in args), True)
# override the builtin print
__builtin__.print = maggy_print
try:
client_addr = client.client_addr
host_port = client_addr[0] + ":" + str(client_addr[1])
exec_spec = {}
exec_spec["partition_id"] = partition_id
exec_spec["task_attempt"] = task_attempt
exec_spec["host_port"] = host_port
exec_spec["trial_id"] = None
reporter.log("Registering with experiment driver", False)
client.register(exec_spec)
client.start_heartbeat(reporter)
# blocking
trial_id, parameters = client.get_suggestion(reporter)
while not client.done:
if experiment_type == "ablation":
ablation_params = {
"ablated_feature":
parameters.get("ablated_feature", "None"),
"ablated_layer":
parameters.get("ablated_layer", "None"),
}
parameters.pop("ablated_feature")
parameters.pop("ablated_layer")
tb_logdir = log_dir + "/" + trial_id
trial_log_file = tb_logdir + "/output.log"
reporter.set_trial_id(trial_id)
# If trial is repeated, delete trial directory, except log file
if hopshdfs.exists(tb_logdir):
util._clean_dir(tb_logdir, [trial_log_file])
else:
hopshdfs.mkdir(tb_logdir)
reporter.init_logger(trial_log_file)
tensorboard._register(tb_logdir)
if experiment_type == "ablation":
hopshdfs.dump(
json.dumps(ablation_params,
default=util.json_default_numpy),
tb_logdir + "/.hparams.json",
)
else:
hopshdfs.dump(
json.dumps(parameters,
default=util.json_default_numpy),
tb_logdir + "/.hparams.json",
)
try:
reporter.log("Starting Trial: {}".format(trial_id), False)
reporter.log("Trial Configuration: {}".format(parameters),
False)
if experiment_type == "optimization":
tensorboard._write_hparams(parameters, trial_id)
sig = inspect.signature(map_fun)
if sig.parameters.get("reporter", None):
retval = map_fun(**parameters, reporter=reporter)
else:
retval = map_fun(**parameters)
if experiment_type == "optimization":
tensorboard._write_session_end()
retval = util._handle_return_val(retval, tb_logdir,
optimization_key,
trial_log_file)
except exceptions.EarlyStopException as e:
retval = e.metric
reporter.log("Early Stopped Trial.", False)
reporter.log("Finished Trial: {}".format(trial_id), False)
reporter.log("Final Metric: {}".format(retval), False)
client.finalize_metric(retval, reporter)
# blocking
trial_id, parameters = client.get_suggestion(reporter)
except: # noqa: E722
reporter.log(traceback.format_exc(), False)
raise
finally:
reporter.close_logger()
client.stop()
client.close()
def init_ml_tracking(self, app_id, run_id):
tensorboard._register(experiment_utils._get_logdir(app_id, run_id))
def spark_wrapper_function(_: Any) -> None:
"""Patched function from tf_dist_executor_fn factory.
:param _: Necessary catch for the iterator given by Spark to the
function upon foreach calls. Can safely be disregarded.
"""
EnvSing.get_instance().set_ml_id(app_id, run_id)
partition_id, _ = util.get_partition_attempt_id()
client = EnvSing.get_instance().get_client(
server_addr,
partition_id,
hb_interval,
secret,
socket.socket(socket.AF_INET, socket.SOCK_STREAM),
)
log_file = log_dir + "/executor_" + str(partition_id) + ".log"
reporter = Reporter(log_file, partition_id, 0, __builtin__.print)
builtin_print = __builtin__.print
_setup_logging(reporter, log_dir)
def maggy_print(*args, **kwargs):
builtin_print(*args, **kwargs)
reporter.log(" ".join(str(x) for x in args), True)
__builtin__.print = maggy_print
try:
host = EnvSing.get_instance().get_ip_address()
tmp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tmp_socket.bind(("", 0))
port = tmp_socket.getsockname()[1] + 1
host_port = host + ":" + str(port)
_register_with_servers(client, reporter, partition_id)
tb_logdir, trial_log_file = _setup_logging(reporter, log_dir)
tensorboard._register(tb_logdir)
reporter.log("Awaiting worker reservations.")
client.await_reservations()
reservations = client.get_message("RESERVATIONS")
reporter.log(reservations)
reporter.log(host_port)
reporter.log("Reservations complete, configuring Tensorflow.")
if not reservations:
reporter.log("Tensorflow registration failed, exiting from all tasks.")
return
workers_host_port = []
for i in list(reservations["cluster"]):
if len(reservations["cluster"][i]) > 0:
workers_host_port.append(reservations["cluster"][i][0])
is_chief = False
task_index = find_index(host_port, reservations)
tf_config = reservations
if task_index == -1:
tf_config["task"] = {"type": "chief", "index": 0}
is_chief = True
else:
tf_config["task"] = {"type": "worker", "index": task_index}
last_worker_index = len(reservations["cluster"]["worker"]) - 1
if not last_worker_index < 0:
evaluator_node = reservations["cluster"]["worker"][last_worker_index]
reservations["cluster"]["evaluator"] = [evaluator_node]
del reservations["cluster"]["worker"][last_worker_index]
if evaluator_node == host_port:
tf_config["task"] = {"type": "evaluator", "index": 0}
reporter.log(f"Tensorflow config is {tf_config}")
_setup_tf_config(tf_config)
strategy = tf.distribute.MultiWorkerMirroredStrategy
model = _wrap_model(config, strategy, is_chief)
if config.dataset is not None and config.process_data is not None:
config.dataset = _consume_data(config)
reporter.log(f"index of slice {partition_id}")
reporter.log("Starting distributed training.")
sig = inspect.signature(train_fn)
kwargs = {}
if sig.parameters.get("model", None):
kwargs["model"] = model
if sig.parameters.get("dataset", None):
kwargs["dataset"] = config.dataset
if sig.parameters.get("hparams", None):
kwargs["hparams"] = config.hparams
if sig.parameters.get("reporter", None):
kwargs["reporter"] = reporter
retval = train_fn(**kwargs)
else:
retval = train_fn(**kwargs)
# Set retval to work with util.handle_return_value,
# if there is more than 1 metrics, retval will be a list and
# retval[0] will contain the final loss
retval_list = []
if isinstance(retval, dict):
for item in retval.items():
retval_list.append(item[1])
retval = retval_list
retval = {"Metric": retval[0] if isinstance(retval, list) else retval}
retval = util.handle_return_val(retval, tb_logdir, "Metric", trial_log_file)
reporter.log("Finished distributed training.")
client.finalize_metric(retval, reporter)
except: # noqa: E722
reporter.log(traceback.format_exc())
raise
finally:
reporter.close_logger()
client.stop()
client.close()
def python_wrapper_function(_: Any) -> None:
"""Patched function from tf_dist_executor_fn factory.
:param _: Necessary catch for the iterator given by Spark to the
function upon foreach calls. Can safely be disregarded.
"""
EnvSing.get_instance().set_ml_id(app_id, run_id)
partition_id, _ = util.get_partition_attempt_id()
log_file = log_dir + "/executor_" + str(partition_id) + ".log"
reporter = Reporter(log_file, partition_id, 0, __builtin__.print)
builtin_print = __builtin__.print
def maggy_print(*args, **kwargs):
builtin_print(*args, **kwargs)
reporter.log(" ".join(str(x) for x in args), True)
__builtin__.print = maggy_print
try:
tmp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tmp_socket.bind(("", 0))
tb_logdir, trial_log_file = _setup_logging(reporter, log_dir)
tensorboard._register(tb_logdir)
tf_config = None
physical_devices = tf.config.list_physical_devices("GPU")
if physical_devices is not None:
strategy = tf.distribute.MultiWorkerMirroredStrategy
for count, pd in enumerate(physical_devices):
if pd == "/gpu:0":
tf_config["task"] = {"type": "chief", "index": 0}
else:
tf_config["task"] = {"type": "worker", "index": count}
else: # Use the Default Strategy
strategy = tf.distribute.get_strategy
model = _wrap_model(config, strategy, False)
if config.dataset is not None and config.process_data is not None:
config.dataset = _consume_data(config)
reporter.log(f"index of slice {partition_id}")
reporter.log("Starting distributed training.")
sig = inspect.signature(train_fn)
kwargs = {}
if sig.parameters.get("model", None):
kwargs["model"] = model
if sig.parameters.get("dataset", None):
kwargs["dataset"] = config.dataset
if sig.parameters.get("hparams", None):
kwargs["hparams"] = config.hparams
if sig.parameters.get("reporter", None):
kwargs["reporter"] = reporter
retval = train_fn(**kwargs)
else:
retval = train_fn(**kwargs)
# Set retval to work with util.handle_return_value,
# if there is more than 1 metrics, retval will be a list and
# retval[0] will contain the final loss
retval_list = []
if isinstance(retval, dict):
for item in retval.items():
retval_list.append(item[1])
retval = retval_list
retval = {"Metric": retval[0] if isinstance(retval, list) else retval}
retval = util.handle_return_val(retval, tb_logdir, "Metric", trial_log_file)
reporter.log("Finished distributed training.")
except: # noqa: E722
reporter.log(traceback.format_exc())
raise
finally:
reporter.close_logger()
__builtin__.print = builtin_print
return retval
def lagom(
map_fun,
name="no-name",
experiment_type="optimization",
searchspace=None,
optimizer=None,
direction="max",
num_trials=1,
ablation_study=None,
ablator=None,
optimization_key="metric",
hb_interval=1,
es_policy="median",
es_interval=300,
es_min=10,
description="",
):
"""Launches a maggy experiment, which depending on `experiment_type` can
either be a hyperparameter optimization or an ablation study experiment.
Given a search space, objective and a model training procedure `map_fun`
(black-box function), an experiment is the whole process of finding the
best hyperparameter combination in the search space, optimizing the
black-box function. Currently maggy supports random search and a median
stopping rule.
**lagom** is a Swedish word meaning "just the right amount".
:param map_fun: User defined experiment containing the model training.
:type map_fun: function
:param name: A user defined experiment identifier.
:type name: str
:param experiment_type: Type of Maggy experiment, either 'optimization'
(default) or 'ablation'.
:type experiment_type: str
:param searchspace: A maggy Searchspace object from which samples are
drawn.
:type searchspace: Searchspace
:param optimizer: The optimizer is the part generating new trials.
:type optimizer: str, AbstractOptimizer
:param direction: If set to ‘max’ the highest value returned will
correspond to the best solution, if set to ‘min’ the opposite is true.
:type direction: str
:param num_trials: the number of trials to evaluate given the search space,
each containing a different hyperparameter combination
:type num_trials: int
:param ablation_study: Ablation study object. Can be None for optimization
experiment type.
:type ablation_study: AblationStudy
:param ablator: Ablator to use for experiment type 'ablation'.
:type ablator: str, AbstractAblator
:param optimization_key: Name of the metric to be optimized
:type optimization_key: str, optional
:param hb_interval: The heartbeat interval in seconds from trial executor
to experiment driver, defaults to 1
:type hb_interval: int, optional
:param es_policy: The earlystopping policy, defaults to 'median'
:type es_policy: str, optional
:param es_interval: Frequency interval in seconds to check currently
running trials for early stopping, defaults to 300
:type es_interval: int, optional
:param es_min: Minimum number of trials finalized before checking for
early stopping, defaults to 10
:type es_min: int, optional
:param description: A longer description of the experiment.
:type description: str, optional
:raises RuntimeError: An experiment is currently running.
:return: A dictionary indicating the best trial and best hyperparameter
combination with it's performance metric
:rtype: dict
"""
global running
if running:
raise RuntimeError("An experiment is currently running.")
job_start = time.time()
sc = hopsutil._find_spark().sparkContext
exp_driver = None
try:
global app_id
global experiment_json
global run_id
app_id = str(sc.applicationId)
app_id, run_id = util._validate_ml_id(app_id, run_id)
# start run
running = True
experiment_utils._set_ml_id(app_id, run_id)
# create experiment dir
experiment_utils._create_experiment_dir(app_id, run_id)
tensorboard._register(experiment_utils._get_logdir(app_id, run_id))
num_executors = util.num_executors(sc)
# start experiment driver
if experiment_type == "optimization":
assert num_trials > 0, "number of trials should be greater " + "than zero"
tensorboard._write_hparams_config(
experiment_utils._get_logdir(app_id, run_id), searchspace
)
if num_executors > num_trials:
num_executors = num_trials
exp_driver = experimentdriver.ExperimentDriver(
"optimization",
searchspace=searchspace,
optimizer=optimizer,
direction=direction,
num_trials=num_trials,
name=name,
num_executors=num_executors,
hb_interval=hb_interval,
es_policy=es_policy,
es_interval=es_interval,
es_min=es_min,
description=description,
log_dir=experiment_utils._get_logdir(app_id, run_id),
)
exp_function = exp_driver.optimizer.name()
elif experiment_type == "ablation":
exp_driver = experimentdriver.ExperimentDriver(
"ablation",
ablation_study=ablation_study,
ablator=ablator,
name=name,
num_executors=num_executors,
hb_interval=hb_interval,
description=description,
log_dir=experiment_utils._get_logdir(app_id, run_id),
)
# using exp_driver.num_executor since
# it has been set using ablator.get_number_of_trials()
# in experiment.py
if num_executors > exp_driver.num_executors:
num_executors = exp_driver.num_executors
exp_function = exp_driver.ablator.name()
else:
running = False
raise RuntimeError(
"Unknown experiment_type:"
"should be either 'optimization' or 'ablation', "
"But it is '{0}'".format(str(experiment_type))
)
nodeRDD = sc.parallelize(range(num_executors), num_executors)
# Do provenance after initializing exp_driver, because exp_driver does
# the type checks for optimizer and searchspace
sc.setJobGroup(os.environ["ML_ID"], "{0} | {1}".format(name, exp_function))
experiment_json = experiment_utils._populate_experiment(
name,
exp_function,
"MAGGY",
exp_driver.searchspace.json(),
description,
app_id,
direction,
optimization_key,
)
experiment_json = experiment_utils._attach_experiment_xattr(
app_id, run_id, experiment_json, "CREATE"
)
util._log(
"Started Maggy Experiment: {0}, {1}, run {2}".format(name, app_id, run_id)
)
exp_driver.init(job_start)
server_addr = exp_driver.server_addr
# Force execution on executor, since GPU is located on executor
nodeRDD.foreachPartition(
trialexecutor._prepare_func(
app_id,
run_id,
experiment_type,
map_fun,
server_addr,
hb_interval,
exp_driver._secret,
optimization_key,
experiment_utils._get_logdir(app_id, run_id),
)
)
job_end = time.time()
result = exp_driver.finalize(job_end)
best_logdir = (
experiment_utils._get_logdir(app_id, run_id) + "/" + result["best_id"]
)
util._finalize_experiment(
experiment_json,
float(result["best_val"]),
app_id,
run_id,
"FINISHED",
exp_driver.duration,
experiment_utils._get_logdir(app_id, run_id),
best_logdir,
optimization_key,
)
util._log("Finished Experiment")
return result
except: # noqa: E722
_exception_handler(
experiment_utils._seconds_to_milliseconds(time.time() - job_start)
)
if exp_driver:
if exp_driver.exception:
raise exp_driver.exception
raise
finally:
# grace period to send last logs to sparkmagic
# sparkmagic hb poll intervall is 5 seconds, therefore wait 6 seconds
time.sleep(6)
# cleanup spark jobs
if running and exp_driver is not None:
exp_driver.stop()
run_id += 1
running = False
sc.setJobGroup("", "")
return result
def _wrapper_fun(_: Any) -> None:
"""Patched function from trial_executor_fn factory.
:param _: Necessary catch for the iterator given by Spark to the
function upon foreach calls. Can safely be disregarded.
"""
env = EnvSing.get_instance()
env.set_ml_id(app_id, run_id)
# get task context information to determine executor identifier
partition_id, task_attempt = util.get_partition_attempt_id()
client = EnvSing.get_instance().get_client(
server_addr,
partition_id,
hb_interval,
secret,
socket.socket(socket.AF_INET, socket.SOCK_STREAM),
)
log_file = (log_dir + "/executor_" + str(partition_id) + "_" +
str(task_attempt) + ".log")
# save the builtin print
original_print = __builtin__.print
reporter = Reporter(log_file, partition_id, task_attempt,
original_print)
def maggy_print(*args, **kwargs):
"""Maggy custom print() function."""
original_print(*args, **kwargs)
reporter.log(" ".join(str(x) for x in args), True)
# override the builtin print
__builtin__.print = maggy_print
try:
client_addr = client.client_addr
host_port = client_addr[0] + ":" + str(client_addr[1])
exec_spec = {}
exec_spec["partition_id"] = partition_id
exec_spec["task_attempt"] = task_attempt
exec_spec["host_port"] = host_port
exec_spec["trial_id"] = None
reporter.log("Registering with experiment driver", False)
client.register(exec_spec)
client.start_heartbeat(reporter)
# blocking
trial_id, parameters = client.get_suggestion(reporter)
while not client.done:
if experiment_type == "ablation":
ablation_params = {
"ablated_feature":
parameters.get("ablated_feature", "None"),
"ablated_layer":
parameters.get("ablated_layer", "None"),
}
parameters.pop("ablated_feature")
parameters.pop("ablated_layer")
tb_logdir = log_dir + "/" + trial_id
trial_log_file = tb_logdir + "/output.log"
reporter.set_trial_id(trial_id)
# If trial is repeated, delete trial directory, except log file
if env.exists(tb_logdir):
util.clean_dir(tb_logdir, [trial_log_file])
else:
env.mkdir(tb_logdir)
reporter.init_logger(trial_log_file)
tensorboard._register(tb_logdir)
if experiment_type == "ablation":
env.dump(
json.dumps(ablation_params,
default=util.json_default_numpy),
tb_logdir + "/.hparams.json",
)
else:
env.dump(
json.dumps(parameters,
default=util.json_default_numpy),
tb_logdir + "/.hparams.json",
)
model = config.model
dataset = config.dataset
try:
reporter.log("Starting Trial: {}".format(trial_id), False)
reporter.log("Trial Configuration: {}".format(parameters),
False)
if experiment_type == "optimization":
tensorboard._write_hparams(parameters, trial_id)
sig = inspect.signature(train_fn)
kwargs = {}
if sig.parameters.get("model", None):
kwargs["model"] = model
if sig.parameters.get("dataset", None):
kwargs["dataset"] = dataset
if sig.parameters.get("hparams", None):
kwargs["hparams"] = parameters
if sig.parameters.get("reporter", None):
kwargs["reporter"] = reporter
retval = train_fn(**kwargs)
else:
retval = train_fn(**kwargs)
# todo: test this change
retval_list = []
if isinstance(retval, dict):
for item in retval.items():
retval_list.append(item[1])
retval = retval_list
retval = {
"Metric":
retval[0] if isinstance(retval, list) else retval
}
retval = util.handle_return_val(retval, tb_logdir,
optimization_key,
trial_log_file)
except exceptions.EarlyStopException as e:
retval = e.metric
reporter.log("Early Stopped Trial.", False)
reporter.log("Finished Trial: {}".format(trial_id), False)
reporter.log("Final Metric: {}".format(retval), False)
client.finalize_metric(retval, reporter)
# blocking
trial_id, parameters = client.get_suggestion(reporter)
except: # noqa: E722
reporter.log(traceback.format_exc(), False)
raise
finally:
reporter.close_logger()
client.stop()
client.close()
