def populate_experiment(config, app_id, run_id, exp_function):
"""Creates a dictionary with the experiment information.
Args:
:config: Experiment config object
:app_id: Application ID
:run_id: Current experiment run ID
:exp_function: Name of experiment driver.
Returns:
:experiment_json: Dictionary with config info on the experiment.
"""
try:
direction = config.direction
except AttributeError:
direction = "N/A"
try:
opt_key = config.optimization_key
except AttributeError:
opt_key = "N/A"
experiment_json = EnvSing.get_instance().populate_experiment(
config.name,
exp_function,
"MAGGY",
None,
config.description,
app_id,
direction,
opt_key,
)
exp_ml_id = app_id + "_" + str(run_id)
experiment_json = EnvSing.get_instance().attach_experiment_xattr(
exp_ml_id, experiment_json, "INIT")
return experiment_json
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 json(self) -> str:
"""Exports the experiment's metadata in JSON format.
:returns: The metadata string.
"""
user = None
constants = EnvSing.get_instance().get_constants()
try:
if constants.ENV_VARIABLES.HOPSWORKS_USER_ENV_VAR in os.environ:
user = os.environ[
constants.ENV_VARIABLES.HOPSWORKS_USER_ENV_VAR]
except AttributeError:
pass
experiment_json = {
"project":
EnvSing.get_instance().project_name(),
"user":
user,
"name":
self.name,
"module":
"maggy",
"app_id":
str(self.app_id),
"start":
time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime(self.job_start)),
"memory_per_executor":
str(self.spark_context._conf.get("spark.executor.memory")),
"gpus_per_executor":
str(self.spark_context._conf.get("spark.executor.gpus")),
"executors":
self.num_executors,
"logdir":
self.log_dir,
# 'versioned_resources': versioned_resources,
"description":
self.description,
"experiment_type":
self.controller.name(),
}
experiment_json["controller"] = self.controller.name()
experiment_json["config"] = json.dumps(self.config_to_dict())
if self.experiment_done:
experiment_json["status"] = "FINISHED"
experiment_json["finished"] = time.strftime(
"%Y-%m-%dT%H:%M:%S", time.localtime(self.job_end))
experiment_json["duration"] = self.duration
experiment_json["config"] = json.dumps(self.result["best_config"])
experiment_json["metric"] = self.result["best_val"]
else:
experiment_json["status"] = "RUNNING"
return json.dumps(experiment_json, default=util.json_default_numpy)
def _final_msg_callback(self, msg: dict) -> None:
"""Final message callback.
Logs trial results and registers executor as idle.
:param msg: The final executor message from the message queue.
"""
trial = self.get_trial(msg["trial_id"])
logs = msg.get("logs", None)
if logs is not None:
with self.log_lock:
self.executor_logs = self.executor_logs + logs
# finalize the trial object
with trial.lock:
trial.status = Trial.FINALIZED
trial.final_metric = msg["data"]
trial.duration = util.seconds_to_milliseconds(time.time() -
trial.start)
# move trial to the finalized ones
self._final_store.append(trial)
self._trial_store.pop(trial.trial_id)
# update result dictionary
self._update_result(trial)
# keep for later in case tqdm doesn't work
self.maggy_log = self._update_maggy_log()
self.log(self.maggy_log)
EnvSing.get_instance().dump(
trial.to_json(),
self.log_dir + "/" + trial.trial_id + "/trial.json",
)
# assign new trial
trial = self.controller_get_next(trial)
if trial is None:
self.server.reservations.assign_trial(msg["partition_id"], None)
self.experiment_done = True
elif trial == "IDLE":
self.add_message({
"type": "IDLE",
"partition_id": msg["partition_id"],
"idle_start": time.time(),
})
self.server.reservations.assign_trial(msg["partition_id"], None)
else:
with trial.lock:
trial.start = time.time()
trial.status = Trial.SCHEDULED
self.server.reservations.assign_trial(msg["partition_id"],
trial.trial_id)
self.add_trial(trial)
def _exit_handler() -> None:
"""Handles jobs killed by the user."""
try:
global RUNNING
global EXPERIMENT_JSON
if RUNNING:
EXPERIMENT_JSON["status"] = "KILLED"
exp_ml_id = APP_ID + "_" + str(RUN_ID)
EnvSing.get_instance().attach_experiment_xattr(
exp_ml_id, EXPERIMENT_JSON, "FULL_UPDATE")
except Exception as err:
util.log(err)
def _load_hparams(hparams_file):
"""Loads the HParams configuration from a hparams file of a trial."""
hparams_file_contents = EnvSing.get_instance().load(hparams_file)
hparams = json.loads(hparams_file_contents)
return hparams
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 _exception_handler(duration: int) -> None:
"""Handles exceptions during execution of an experiment.
:param duration: Duration of the experiment until exception in milliseconds
"""
try:
global RUNNING
global EXPERIMENT_JSON
if RUNNING:
EXPERIMENT_JSON["state"] = "FAILED"
EXPERIMENT_JSON["duration"] = duration
exp_ml_id = APP_ID + "_" + str(RUN_ID)
EnvSing.get_instance().attach_experiment_xattr(
exp_ml_id, EXPERIMENT_JSON, "FULL_UPDATE")
except Exception as err:
util.log(err)
def __init__(
self, dataset: str, batch_size: int = 1, transform_spec: TransformSpec = None
):
"""Initializes a reader depending on the dataset (Petastorm/Parquet).
:param dataset: Path to the dataset.
:param batch_size: How many samples per batch to load (default: ``1``).
:param transform_spec: Petastorm transform spec for data augmentation.
"""
num_workers = int(os.environ["WORLD_SIZE"]) # Is set at lagom startup.
rank = int(os.environ["RANK"])
is_peta_ds = EnvSing.get_instance().exists(
dataset.rstrip("/") + "/_common_metadata"
)
# Make reader only compatible with petastorm dataset.
ds_type = "Petastorm" if is_peta_ds else "Parquet"
print(f"{ds_type} dataset detected in folder {dataset}")
reader_factory = make_reader if is_peta_ds else make_batch_reader
reader = reader_factory(
dataset,
cur_shard=rank,
shard_count=num_workers,
transform_spec=TransformSpec(transform_spec),
)
super().__init__(reader, batch_size=batch_size)
self.iterator = None
def log(self, log_msg: str) -> None:
"""Logs a string to the maggy driver log file.
:param log_msg: The log message.
"""
msg = datetime.now().isoformat() + ": " + str(log_msg)
self.log_file_handle.write(EnvSing.get_instance().str_or_byte((msg + "\n")))
def log(self, log_msg, jupyter=False):
"""Logs a message to the executor logfile and executor stderr and
optionally prints the message in jupyter.
:param log_msg: Message to log.
:type log_msg: str
:param verbose: Print in Jupyter Notebook, defaults to True
:type verbose: bool, optional
"""
with self.lock:
env = EnvSing.get_instance()
try:
msg = (datetime.now().isoformat() +
" ({0}/{1}): {2} \n").format(self.partition_id,
self.task_attempt, log_msg)
if jupyter:
jupyter_log = str(self.partition_id) + ": " + log_msg
if self.trial_fd:
self.trial_fd.write(env.str_or_byte(msg))
self.logs = self.logs + jupyter_log + "\n"
else:
self.fd.write(env.str_or_byte(msg))
if self.trial_fd:
self.trial_fd.write(env.str_or_byte(msg))
self.print_executor(msg)
# Throws ValueError when operating on closed HDFS file object
# Throws AttributeError when calling file ops on NoneType object
except (IOError, ValueError, AttributeError) as e:
self.fd.write(
env.str_or_byte(
"An error occurred while writing logs: {}".format(e)))
def init_logger(self, trial_log_file):
"""Initializes the trial log file"""
self.trial_log_file = trial_log_file
env = EnvSing.get_instance()
# Open trial log file descriptor
if not env.exists(self.trial_log_file):
env.dump("", self.trial_log_file)
self.trial_fd = env.open_file(self.trial_log_file, flags="w")
def _consume_data(config):
"""Load and return the training and test datasets from config file. If the config.dataset and config.test_set are
strings they are assumed as path, the functions check if the files or directories exists, if they exists then it
will run the function in config.process_data, with paramneters config.dataset and config.test_set and return the
result.
If the config.dataset and cofig.test_set are not strings but anything else (like a List, nparray, tf.data.Dataset)
they will returned as they are.
The types of config.dataset and config.test_set have to be the same.
:param config: the experiment configuration dictionary
:returns: dataset
:raises TypeError: if the config.dataset and config.test_set are of different type
:raises TypeError: if the process_data function is missing or cannot process the data
:raises FileNotFoundError: in case config.dataset or config.test_set are not found
"""
dataset_list = config.dataset
if not isinstance(dataset_list, list):
raise TypeError(
"Dataset must be a list, got {}. If you have only 1 set, provide it within a list".format(
type(dataset_list)
)
)
data_type = dataset_list[0]
if data_type == str:
for ds in dataset_list:
if type(ds) != data_type:
raise TypeError(
"Dataset contains string and other types, "
"if a string is included, it must contain all strings."
)
env = EnvSing.get_instance()
for ds in dataset_list:
if not (env.isdir(ds) or env.exists(ds)):
raise FileNotFoundError(f"Path {ds} does not exists.")
try:
return config.process_data(dataset_list)
except TypeError:
raise TypeError(
(
f"process_data function missing in config, "
f"please provide a function that takes 1 argument dataset, "
f"reads it and "
f"returns the transformed dataset as the list before. "
f"config: {config}"
)
)
else: # type is not str (could be anything)
return config.process_data(dataset_list)
def _setup_logging(reporter: Reporter, log_dir: str) -> Tuple[str, str]:
"""Sets up logging directories and files.
:param reporter: Reporter responsible for logging.
:param log_dir: Log directory path on the file system.
:returns: Tuple containing the path of the tensorboard directory
and the trial log file.
"""
reporter.set_trial_id(0)
tb_logdir = log_dir + "/" + "training_logs_" + str(reporter.partition_id)
trial_log_file = tb_logdir + "/output.log"
reporter.set_trial_id(0)
# If trial is repeated, delete trial directory, except log file
if EnvSing.get_instance().exists(tb_logdir):
util.clean_dir(tb_logdir, [trial_log_file])
else:
EnvSing.get_instance().mkdir(tb_logdir)
reporter.init_logger(trial_log_file)
return tb_logdir, trial_log_file
def finalize(self, job_end: float) -> dict:
"""Saves a summary of the experiment to a dict and logs it in the DFS.
:param job_end: Time of the job end.
:returns: The experiment summary dict.
"""
self.job_end = job_end
self.duration = util.seconds_to_milliseconds(self.job_end -
self.job_start)
duration_str = util.time_diff(self.job_start, self.job_end)
results = self.prep_results(duration_str)
print(results)
self.log(results)
EnvSing.get_instance().dump(
json.dumps(self.result, default=util.json_default_numpy),
self.log_dir + "/result.json",
)
EnvSing.get_instance().dump(self.json(), self.log_dir + "/maggy.json")
return self.result_dict
def num_executors(sc):
"""
Get the number of executors configured for Jupyter
:param sc: The SparkContext to take the executors from.
:type sc: [SparkContext
:return: Number of configured executors for Jupyter
:rtype: int
"""
return EnvSing.get_instance().get_executors(sc)
def clean_dir(clean_dir, keep=[]):
"""Deletes all files in a directory but keeps a few."""
env = EnvSing.get_instance()
if not env.isdir(clean_dir):
raise ValueError(
"{} is not a directory. Use `hops.hdfs.delete()` to delete single "
"files.".format(clean_dir))
for path in env.ls(clean_dir):
if path not in keep:
env.delete(path, recursive=True)
def _exp_final_callback(self, job_end: float, _: Any) -> dict:
"""Calculates the average test error from all partitions.
:param job_end: Time of the job end.
:param _: Catches additional callback arguments.
:returns: The result in a dictionary.
"""
result = {"test result": self.average_metric()}
exp_ml_id = str(self.app_id) + "_" + str(self.run_id)
EnvSing.get_instance().attach_experiment_xattr(
exp_ml_id,
{"state": "FINISHED", "duration": int(job_end - self.job_start) * 1000},
"FULL_UPDATE",
)
print("Final average test loss: {:.3f}".format(self.average_metric()))
print(
"Finished experiment. Total run time: "
+ util.time_diff(self.job_start, job_end)
)
return result
def __init__(self, config: LagomConfig, app_id: int, run_id: int):
"""Sets up the RPC server, message queue and logs.
:param config: Experiment config.
:param app_id: Maggy application ID.
:param run_id: Maggy run ID.
"""
global DRIVER_SECRET
self.config = config
self.app_id = app_id
self.run_id = run_id
self.name = config.name
self.description = config.description
self.spark_context = None
self.num_executors = util.num_physical_devices()
self.server_addr = None
self.hb_interval = config.hb_interval
self.job_start = None
DRIVER_SECRET = (DRIVER_SECRET if DRIVER_SECRET else
self._generate_secret(self.SECRET_BYTES))
self._secret = DRIVER_SECRET
# Logging related initialization
self._message_q = queue.Queue()
self.message_callbacks = {}
self.server = None
self._register_msg_callbacks()
self.worker_done = False
self.executor_logs = ""
self.log_lock = threading.RLock()
self.log_dir = EnvSing.get_instance().get_logdir(app_id, run_id)
log_file = self.log_dir + "/maggy.log"
# Open File desc for HDFS to log
if not EnvSing.get_instance().exists(log_file):
EnvSing.get_instance().dump("", log_file)
self.log_file_handle = EnvSing.get_instance().open_file(log_file,
flags="w")
self.exception = None
self.result = None
self.result_dict = {}
self.main_metric_key = None
def start(self, exp_driver):
"""
Start listener in a background thread.
Returns:
address of the Server as a tuple of (host, port)
"""
global SERVER_HOST_PORT
server_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_sock, SERVER_HOST_PORT = EnvSing.get_instance().connect_host(
server_sock, SERVER_HOST_PORT, exp_driver
)
def _listen(self, sock, driver):
CONNECTIONS = []
CONNECTIONS.append(sock)
while not self.done:
read_socks, _, _ = select.select(CONNECTIONS, [], [], 1)
for sock in read_socks:
if sock == server_sock:
client_sock, client_addr = sock.accept()
CONNECTIONS.append(client_sock)
_ = client_addr
else:
try:
msg = self.receive(sock)
# raise exception if secret does not match
# so client socket gets closed
if not secrets.compare_digest(
msg["secret"], exp_driver._secret
):
exp_driver.log(
"SERVER secret: {}".format(exp_driver._secret)
)
exp_driver.log(
"ERROR: wrong secret {}".format(msg["secret"])
)
raise Exception
self._handle_message(sock, msg, driver)
except Exception:
sock.close()
CONNECTIONS.remove(sock)
server_sock.close()
threading.Thread(
target=_listen, args=(self, server_sock, exp_driver), daemon=True
).start()
return SERVER_HOST_PORT
def _initialize_logger(self, exp_dir):
"""Initialize logger of optimizer
:param exp_dir: path of experiment directory
:rtype exp_dir: str
"""
env = EnvSing.get_instance()
# configure logger
self.log_file = exp_dir + "/optimizer.log"
if not env.exists(self.log_file):
env.dump("", self.log_file)
self.fd = env.open_file(self.log_file, flags="w")
self._log("Initialized Optimizer Logger")
def finalize_experiment(
experiment_json,
metric,
app_id,
run_id,
state,
duration,
logdir,
best_logdir,
optimization_key,
):
EnvSing.get_instance().finalize_experiment(
experiment_json,
metric,
app_id,
run_id,
state,
duration,
logdir,
best_logdir,
optimization_key,
)
def lagom(train_fn: Callable, config: LagomConfig) -> dict:
"""Launches a maggy experiment, which depending on 'config' can either
be a hyperparameter optimization, an ablation study experiment or distributed
training. Given a search space, objective and a model training procedure `train_fn`
(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 train_fn: User defined experiment containing the model training.
:param config: An experiment configuration. For more information, see config.
:returns: The experiment results as a dict.
"""
global APP_ID
global RUNNING
global RUN_ID
job_start = time.time()
try:
if RUNNING:
raise RuntimeError("An experiment is currently running.")
RUNNING = True
spark_context = util.find_spark().sparkContext
APP_ID = str(spark_context.applicationId)
APP_ID, RUN_ID = util.register_environment(APP_ID, RUN_ID)
EnvSing.get_instance().set_app_id(APP_ID)
driver = lagom_driver(config, APP_ID, RUN_ID)
return driver.run_experiment(train_fn, config)
except: # noqa: E722
_exception_handler(
util.seconds_to_milliseconds(time.time() - job_start))
raise
finally:
# Clean up spark jobs
RUN_ID += 1
RUNNING = False
util.find_spark().sparkContext.setJobGroup("", "")
def __init__(self, server_addr, partition_id, task_attempt, hb_interval, secret):
# socket for main thread
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.connect(server_addr)
# socket for heartbeat thread
self.hb_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.hb_sock.connect(server_addr)
self.server_addr = server_addr
self.done = False
self.client_addr = (
EnvSing.get_instance().get_ip_address(),
self.sock.getsockname()[1],
)
self.partition_id = partition_id
self.task_attempt = task_attempt
self.hb_interval = hb_interval
self._secret = secret
def build_summary_json(logdir):
"""Builds the summary json to be read by the experiments service."""
combinations = []
env = EnvSing.get_instance()
for trial in env.ls(logdir):
if env.isdir(trial):
return_file = trial + "/.outputs.json"
hparams_file = trial + "/.hparams.json"
if env.exists(return_file) and env.exists(hparams_file):
metric_arr = env.convert_return_file_to_arr(return_file)
hparams_dict = _load_hparams(hparams_file)
combinations.append({
"parameters": hparams_dict,
"outputs": metric_arr
})
return json.dumps({"combinations": combinations},
default=json_default_numpy)
def create_tf_dataset(num_epochs, batch_size):
conn = EnvSing.get_instance().connect_hsfs(engine="training")
fs = conn.get_feature_store()
td = fs.get_training_dataset(
training_dataset_name, training_dataset_version
)
feature_names = [f.name for f in td.schema]
if ablated_feature is not None:
feature_names.remove(ablated_feature)
return td.tf_data(
target_name=label_name, feature_names=feature_names
).tf_record_dataset(
batch_size=batch_size, num_epochs=num_epochs, process=True
)
def __init__(self, log_file, partition_id, task_attempt, print_executor):
self.metric = None
self.step = -1
self.lock = threading.RLock()
self.stop = False
self.trial_id = None
self.trial_log_file = None
self.logs = ""
self.log_file = log_file
self.partition_id = partition_id
self.task_attempt = task_attempt
self.print_executor = print_executor
# Open executor log file descriptor
# This log is for all maggy system related log messages
env = EnvSing.get_instance()
if not env.exists(log_file):
env.dump("", log_file)
self.fd = env.open_file(log_file, flags="w")
self.trial_fd = None
def handle_return_val(return_val, log_dir, optimization_key, log_file):
"""Handles the return value of the user defined training function."""
env = EnvSing.get_instance()
env.upload_file_output(return_val, log_dir)
# Return type validation
if not optimization_key:
raise ValueError("Optimization key cannot be None.")
if not return_val:
raise exceptions.ReturnTypeError(optimization_key, return_val)
if not isinstance(return_val, constants.USER_FCT.RETURN_TYPES):
raise exceptions.ReturnTypeError(optimization_key, return_val)
if isinstance(return_val, dict) and optimization_key not in return_val:
raise KeyError(
"Returned dictionary does not contain optimization key with the "
"provided name: {}".format(optimization_key))
# validate that optimization metric is numeric
if isinstance(return_val, dict):
opt_val = return_val[optimization_key]
else:
opt_val = return_val
return_val = {optimization_key: opt_val}
if not isinstance(opt_val, constants.USER_FCT.NUMERIC_TYPES):
raise exceptions.MetricTypeError(optimization_key, opt_val)
# for key, value in return_val.items():
# return_val[key] = value if isinstance(value, str) else str(value)
return_val["log"] = log_file.replace(env.project_path(), "")
return_file = log_dir + "/.outputs.json"
env.dump(json.dumps(return_val, default=json_default_numpy), return_file)
metric_file = log_dir + "/.metric"
env.dump(json.dumps(opt_val, default=json_default_numpy), metric_file)
return opt_val
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 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()
