def __init__(self,
config,
train_data,
model_creator,
loss_creator=None,
train_resize_batch_num=None,
eval_metrics_creator=None,
test_data=None,
validation_metrics_creator=None,
num_workers=1,
num_servers=None,
runner_cores=None):
self.config = config
self.train_data = train_data
self.test_data = test_data
self.model_creator = model_creator
self.loss_creator = loss_creator
self.validation_metrics_creator = validation_metrics_creator
self.eval_metrics_creator = eval_metrics_creator
self.num_workers = num_workers
self.num_servers = num_servers if num_servers else self.num_workers
self.train_resize_batch_num = train_resize_batch_num
# Generate actor class
# Add a dummy custom resource: _mxnet_worker and _mxnet_server to diff worker from server
# if runner_cores is specified so that we can place one worker and one server on a node
# for better performance.
Worker = ray.remote(num_cpus=runner_cores, resources={"_mxnet_worker": 1})(MXNetRunner) \
if runner_cores else ray.remote(MXNetRunner)
Server = ray.remote(num_cpus=runner_cores, resources={"_mxnet_server": 1})(MXNetRunner) \
if runner_cores else ray.remote(MXNetRunner)
# Start runners: workers followed by servers
self.runners = [Worker.remote() for i in range(self.num_workers)]
self.runners += [Server.remote() for i in range(self.num_servers)]
# Compute URL for initializing distributed setup
ips = ray.get([runner.get_node_ip.remote() for runner in self.runners])
ports = ray.get(
[runner.find_free_port.remote() for runner in self.runners])
logger = logging.getLogger()
logger.info(ips)
logger.info(ports)
env = {
"DMLC_PS_ROOT_URI": str(get_host_ip()),
"DMLC_PS_ROOT_PORT": str(find_free_port()),
"DMLC_NUM_SERVER": str(self.num_servers),
"DMLC_NUM_WORKER": str(self.num_workers),
}
envs = []
for i in range(self.num_workers):
current_env = env.copy()
current_env['DMLC_ROLE'] = 'worker'
envs.append(current_env)
for i in range(self.num_servers):
current_env = env.copy()
current_env['DMLC_ROLE'] = 'server'
envs.append(current_env)
env['DMLC_ROLE'] = 'scheduler'
modified_env = os.environ.copy()
modified_env.update(env)
# Need to contain system env to run bash
# TODO: Need to kill this process manually?
subprocess.Popen("python -c 'import mxnet'",
shell=True,
env=modified_env)
ray.get([
runner.setup_distributed.remote(
envs[i], self.config, self.train_data, self.model_creator,
self.loss_creator, self.validation_metrics_creator,
self.test_data, self.train_resize_batch_num,
self.eval_metrics_creator)
for i, runner in enumerate(self.runners)
])
def __init__(self,
config,
model_creator,
loss_creator=None,
eval_metrics_creator=None,
validation_metrics_creator=None,
num_workers=None,
num_servers=None,
runner_cores=None):
ray_ctx = RayContext.get()
if not num_workers:
num_workers = ray_ctx.num_ray_nodes
self.config = {} if config is None else config
assert isinstance(config, dict), "config must be a dict"
for param in ["optimizer", "optimizer_params", "log_interval"]:
assert param in config, param + " must be specified in config"
self.model_creator = model_creator
self.loss_creator = loss_creator
self.validation_metrics_creator = validation_metrics_creator
self.eval_metrics_creator = eval_metrics_creator
self.num_workers = num_workers
self.num_servers = num_servers if num_servers else self.num_workers
# Generate actor class
# Add a dummy custom resource: _mxnet_worker and _mxnet_server to diff worker from server
# if runner_cores is specified so that we can place one worker and one server on a node
# for better performance.
Worker = ray.remote(num_cpus=runner_cores, resources={"_mxnet_worker": 1})(MXNetRunner) \
if runner_cores else ray.remote(MXNetRunner)
Server = ray.remote(num_cpus=runner_cores, resources={"_mxnet_server": 1})(MXNetRunner) \
if runner_cores else ray.remote(MXNetRunner)
# Start runners: workers followed by servers
self.workers = [Worker.remote() for i in range(self.num_workers)]
self.servers = [Server.remote() for i in range(self.num_servers)]
self.runners = self.workers + self.servers
env = {
"DMLC_PS_ROOT_URI": str(get_host_ip()),
"DMLC_PS_ROOT_PORT": str(find_free_port()),
"DMLC_NUM_SERVER": str(self.num_servers),
"DMLC_NUM_WORKER": str(self.num_workers),
}
envs = []
for i in range(self.num_workers):
current_env = env.copy()
current_env['DMLC_ROLE'] = 'worker'
envs.append(current_env)
for i in range(self.num_servers):
current_env = env.copy()
current_env['DMLC_ROLE'] = 'server'
envs.append(current_env)
env['DMLC_ROLE'] = 'scheduler'
modified_env = os.environ.copy()
modified_env.update(env)
# Need to contain system env to run bash
# TODO: Need to kill this process manually?
subprocess.Popen("python -c 'import mxnet'",
shell=True,
env=modified_env)
ray.get([
runner.setup_distributed.remote(envs[i], self.config,
self.model_creator,
self.loss_creator,
self.validation_metrics_creator,
self.eval_metrics_creator)
for i, runner in enumerate(self.runners)
])
def __init__(self,
config,
train_data,
model_creator,
loss_creator=None,
train_resize_batch_num=None,
eval_metrics_creator=None,
test_data=None,
validation_metrics_creator=None,
num_workers=1,
num_servers=None,
runner_cores=None):
self.config = config
self.model_creator = model_creator
self.loss_creator = loss_creator
self.validation_metrics_creator = validation_metrics_creator
self.eval_metrics_creator = eval_metrics_creator
self.num_workers = num_workers
self.num_servers = num_servers if num_servers else self.num_workers
self.train_resize_batch_num = train_resize_batch_num
from zoo.orca.data import RayXShards, SparkXShards
if isinstance(train_data, SparkXShards):
train_data = train_data.repartition(self.num_workers).to_ray()
if test_data:
assert isinstance(test_data, SparkXShards)
test_data = test_data.repartition(self.num_workers).to_ray()
if isinstance(train_data, RayXShards):
if train_data.num_partitions() != self.num_workers:
train_data.repartition(self.num_workers)
if test_data:
assert isinstance(test_data, RayXShards)
if test_data.num_partitions() != self.num_workers:
test_data.repartition(self.num_workers)
self.train_data = train_data
self.test_data = test_data
# Generate actor class
# Add a dummy custom resource: _mxnet_worker and _mxnet_server to diff worker from server
# if runner_cores is specified so that we can place one worker and one server on a node
# for better performance.
Worker = ray.remote(num_cpus=runner_cores, resources={"_mxnet_worker": 1})(MXNetRunner) \
if runner_cores else ray.remote(MXNetRunner)
Server = ray.remote(num_cpus=runner_cores, resources={"_mxnet_server": 1})(MXNetRunner) \
if runner_cores else ray.remote(MXNetRunner)
# Start runners: workers followed by servers
self.workers = [Worker.remote() for i in range(self.num_workers)]
self.servers = [Server.remote() for i in range(self.num_servers)]
if isinstance(self.train_data, RayXShards):
self.workers = self.train_data.colocate_actors(self.workers)
self.runners = self.workers + self.servers
env = {
"DMLC_PS_ROOT_URI": str(get_host_ip()),
"DMLC_PS_ROOT_PORT": str(find_free_port()),
"DMLC_NUM_SERVER": str(self.num_servers),
"DMLC_NUM_WORKER": str(self.num_workers),
}
envs = []
for i in range(self.num_workers):
current_env = env.copy()
current_env['DMLC_ROLE'] = 'worker'
envs.append(current_env)
for i in range(self.num_servers):
current_env = env.copy()
current_env['DMLC_ROLE'] = 'server'
envs.append(current_env)
env['DMLC_ROLE'] = 'scheduler'
modified_env = os.environ.copy()
modified_env.update(env)
# Need to contain system env to run bash
# TODO: Need to kill this process manually?
subprocess.Popen("python -c 'import mxnet'",
shell=True,
env=modified_env)
ray.get([
runner.setup_distributed.remote(
envs[i], self.config, self.train_data, self.model_creator,
self.loss_creator, self.validation_metrics_creator,
self.test_data, self.train_resize_batch_num,
self.eval_metrics_creator)
for i, runner in enumerate(self.runners)
])
