def test_get_optimizer_info(self):
learning_rate = 0.1
momentum = 0.0
nesterov = False
expected_args = ("learning_rate=" + str(learning_rate) + ";momentum=" +
str(momentum) + ";nesterov=False;")
opt = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=momentum,
nesterov=nesterov)
opt_type, opt_args = get_optimizer_info(opt)
self.assertEqual(opt_type, "SGD")
self.assertEqual(opt_args, expected_args)
beta_1 = 0.8
beta_2 = 0.6
epsilon = 1e-08
amsgrad = False
expected_args = ("learning_rate=" + str(learning_rate) + ";beta_1=" +
str(beta_1) + ";beta_2=" + str(beta_2) + ";epsilon=" +
str(epsilon) + ";amsgrad=False;")
opt = tf.keras.optimizers.Adam(
learning_rate=learning_rate,
beta_1=beta_1,
beta_2=beta_2,
epsilon=epsilon,
amsgrad=amsgrad,
)
opt_type, opt_args = get_optimizer_info(opt)
self.assertEqual(opt_type, "Adam")
self.assertEqual(opt_args, expected_args)
def get_ps_args(self, args):
if args.distribution_strategy == DistributionStrategy.PARAMETER_SERVER:
opt_type, opt_args = get_optimizer_info(self._optimizer)
ps_command = "elasticdl_ps"
ps_command_args = [
"-job_name=" + args.job_name,
"-namespace=" + args.namespace,
"-master_addr=" + self.master_addr,
"-port=2222",
"-use_async=" + ("true" if args.use_async else "false"),
"-grads_to_wait=" + str(args.grads_to_wait),
"-lr_staleness_modulation=" +
("true" if args.lr_staleness_modulation else "false"),
"-sync_version_tolerance=" + str(args.sync_version_tolerance),
"-evaluation_steps=" + str(args.evaluation_steps),
"-num_ps_pods=" + str(args.num_ps_pods),
"-num_workers=" + str(args.num_workers),
"-checkpoint_dir=" + str(args.checkpoint_dir),
"-checkpoint_steps=" + str(args.checkpoint_steps),
"-keep_checkpoint_max=" + str(args.keep_checkpoint_max),
"-checkpoint_dir_for_init=" +
str(args.checkpoint_dir_for_init),
"-opt_type=" + opt_type,
"-opt_args=" + opt_args,
]
ps_command_args = wrap_go_args_with_string(ps_command_args)
# Execute source /root/.bashrc to add the file path
# of `elasticdl_ps` into the PATH environment variable.
ps_args = ["source", "/root/.bashrc_elasticdl", "&&", ps_command]
ps_args.extend(ps_command_args)
ps_args = ["-c", " ".join(ps_args)]
return ps_args
else:
return []
def _create_instance_manager(self, args):
instance_manager = None
container_command = ["/bin/bash"]
if args.num_workers:
assert args.worker_image, "Worker image cannot be empty"
worker_client_command = (
BashCommandTemplate.SET_PIPEFAIL
+ " python -m elasticdl.python.worker.main"
)
worker_args = [
"--master_addr",
self.master_addr,
"--job_type",
self.job_type,
]
worker_args.extend(
build_arguments_from_parsed_result(args, filter_args=["envs"])
)
worker_args = wrap_python_args_with_string(worker_args)
worker_args.insert(0, worker_client_command)
if args.use_go_ps:
opt_type, opt_args = get_optimizer_info(self.optimizer)
ps_command = "elasticdl_ps"
ps_command_args = [
"-job_name=" + args.job_name,
"-namespace=" + args.namespace,
"-master_addr=" + self.master_addr,
"-port=2222",
"-use_async=" + ("true" if args.use_async else "false"),
"-grads_to_wait=" + str(args.grads_to_wait),
"-lr_staleness_modulation="
+ ("true" if args.lr_staleness_modulation else "false"),
"-sync_version_tolerance="
+ str(args.sync_version_tolerance),
"-evaluation_steps=" + str(args.evaluation_steps),
"-num_ps_pods=" + str(args.num_ps_pods),
"-num_workers=" + str(args.num_workers),
"-checkpoint_dir=" + str(args.checkpoint_dir),
"-checkpoint_steps=" + str(args.checkpoint_steps),
"-keep_checkpoint_max=" + str(args.keep_checkpoint_max),
"-checkpoint_dir_for_init="
+ str(args.checkpoint_dir_for_init),
"-opt_type=" + opt_type,
"-opt_args=" + opt_args,
]
ps_command_args = wrap_go_args_with_string(ps_command_args)
# Execute source /root/.bashrc to add the file path
# of `elasticdl_ps` into the PATH environment variable.
ps_args = [
"source",
"/root/.bashrc_elasticdl",
"&&",
ps_command,
]
ps_args.extend(ps_command_args)
else:
ps_command = (
BashCommandTemplate.SET_PIPEFAIL
+ " python -m elasticdl.python.ps.main"
)
ps_command_args = [
"--grads_to_wait",
str(args.grads_to_wait),
"--lr_staleness_modulation",
str(args.lr_staleness_modulation),
"--sync_version_tolerance",
str(args.sync_version_tolerance),
"--use_async",
str(args.use_async),
"--model_zoo",
args.model_zoo,
"--model_def",
args.model_def,
"--job_name",
args.job_name,
"--port",
"2222",
"--master_addr",
self.master_addr,
"--namespace",
args.namespace,
"--evaluation_steps",
str(args.evaluation_steps),
"--checkpoint_dir",
str(args.checkpoint_dir),
"--checkpoint_steps",
str(args.checkpoint_steps),
"--keep_checkpoint_max",
str(args.keep_checkpoint_max),
"--num_ps_pods",
str(args.num_ps_pods),
"--checkpoint_dir_for_init",
str(args.checkpoint_dir_for_init),
"--num_workers",
str(args.num_workers),
"--log_level",
str(args.log_level),
"--minibatch_size",
str(args.minibatch_size),
"--num_minibatches_per_task",
str(args.num_minibatches_per_task),
]
ps_args = wrap_python_args_with_string(ps_command_args)
ps_args.insert(0, ps_command)
worker_args = ["-c", " ".join(worker_args)]
ps_args = ["-c", " ".join(ps_args)]
env_dict = parse_envs(args.envs)
env = []
for key in env_dict:
env.append(V1EnvVar(name=key, value=env_dict[key]))
kwargs = get_dict_from_params_str(args.aux_params)
disable_relaunch = kwargs.get("disable_relaunch", False)
cluster_spec = self._get_image_cluster_spec(args.cluster_spec)
instance_manager = InstanceManager(
self.task_d,
rendezvous_server=self.rendezvous_server,
job_name=args.job_name,
image_name=args.worker_image,
worker_command=container_command,
worker_args=worker_args,
namespace=args.namespace,
num_workers=args.num_workers,
worker_resource_request=args.worker_resource_request,
worker_resource_limit=args.worker_resource_limit,
worker_pod_priority=args.worker_pod_priority,
num_ps=args.num_ps_pods,
ps_command=container_command,
ps_args=ps_args,
ps_resource_request=args.ps_resource_request,
ps_resource_limit=args.ps_resource_limit,
ps_pod_priority=args.ps_pod_priority,
volume=args.volume,
image_pull_policy=args.image_pull_policy,
restart_policy=args.restart_policy,
cluster_spec=cluster_spec,
envs=env,
disable_relaunch=disable_relaunch,
log_file_path=args.log_file_path,
)
return instance_manager
def _create_instance_manager(self, args):
instance_manager = None
if args.num_workers:
assert args.worker_image, "Worker image cannot be empty"
worker_command = ["python"]
worker_args = [
"-m",
"elasticdl.python.worker.main",
"--master_addr",
self.master_addr,
"--job_type",
self.job_type,
]
worker_args.extend(build_arguments_from_parsed_result(args))
if args.use_go_ps:
opt_type, opt_args = get_optimizer_info(self.optimizer)
# TODO: rename the Go PS executable using a meaningful filename
ps_command = ["main"]
ps_args = [
"-job_name=" + args.job_name,
"-namespace=" + args.namespace,
"-master_addr=" + self.master_addr,
"-port=2222",
"-use_async=" + ("true" if args.use_async else "false"),
"-grads_to_wait=" + str(args.grads_to_wait),
"-lr_staleness_modulation="
+ ("true" if args.lr_staleness_modulation else "false"),
"-sync_version_tolerance="
+ str(args.sync_version_tolerance),
"-evaluation_steps=" + str(args.evaluation_steps),
"-num_ps_pods=" + str(args.num_ps_pods),
"-num_workers=" + str(args.num_workers),
"-checkpoint_dir=" + str(args.checkpoint_dir),
"-checkpoint_steps=" + str(args.checkpoint_steps),
"-keep_checkpoint_max=" + str(args.keep_checkpoint_max),
"-checkpoint_dir_for_init="
+ str(args.checkpoint_dir_for_init),
"-opt_type=" + opt_type,
"-opt_args=" + opt_args,
]
else:
ps_command = ["python"]
ps_args = [
"-m",
"elasticdl.python.ps.main",
"--grads_to_wait",
str(args.grads_to_wait),
"--lr_staleness_modulation",
str(args.lr_staleness_modulation),
"--sync_version_tolerance",
str(args.sync_version_tolerance),
"--use_async",
str(args.use_async),
"--model_zoo",
args.model_zoo,
"--model_def",
args.model_def,
"--job_name",
args.job_name,
"--port",
"2222",
"--master_addr",
self.master_addr,
"--namespace",
args.namespace,
"--evaluation_steps",
str(args.evaluation_steps),
"--checkpoint_dir",
str(args.checkpoint_dir),
"--checkpoint_steps",
str(args.checkpoint_steps),
"--keep_checkpoint_max",
str(args.keep_checkpoint_max),
"--num_ps_pods",
str(args.num_ps_pods),
"--checkpoint_dir_for_init",
str(args.checkpoint_dir_for_init),
"--num_workers",
str(args.num_workers),
"--log_level",
str(args.log_level),
"--minibatch_size",
str(args.minibatch_size),
"--num_minibatches_per_task",
str(args.num_minibatches_per_task),
]
env_dict = parse_envs(args.envs)
env = []
for key in env_dict:
env.append(V1EnvVar(name=key, value=env_dict[key]))
instance_manager = InstanceManager(
self.task_d,
job_name=args.job_name,
image_name=args.worker_image,
worker_command=worker_command,
worker_args=worker_args,
namespace=args.namespace,
num_workers=args.num_workers,
worker_resource_request=args.worker_resource_request,
worker_resource_limit=args.worker_resource_limit,
worker_pod_priority=args.worker_pod_priority,
num_ps=args.num_ps_pods,
ps_command=ps_command,
ps_args=ps_args,
ps_resource_request=args.ps_resource_request,
ps_resource_limit=args.ps_resource_limit,
ps_pod_priority=args.ps_pod_priority,
volume=args.volume,
image_pull_policy=args.image_pull_policy,
restart_policy=args.restart_policy,
cluster_spec=args.cluster_spec,
envs=env,
expose_ports=self.distribution_strategy
== DistributionStrategy.ALLREDUCE,
)
return instance_manager
