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Allow multiple raylets to be started on a single machine. (#1904)

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This commit is contained in:
Robert Nishihara
2018-04-15 13:51:19 -07:00
committed by Philipp Moritz
parent 4b655b0ff6
commit 6f8b81d9e5
1 changed files with 18 additions and 16 deletions
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python/ray/services.py
+18 -16
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@@ -1382,6 +1382,9 @@ def start_ray_processes(address_info=None,
if "local_scheduler_socket_names" not in address_info:
address_info["local_scheduler_socket_names"] = []
local_scheduler_socket_names = address_info["local_scheduler_socket_names"]
if "raylet_socket_names" not in address_info:
address_info["raylet_socket_names"] = []
raylet_socket_names = address_info["raylet_socket_names"]
# Get the ports to use for the object managers if any are provided.
object_manager_ports = (address_info["object_manager_ports"] if
@@ -1413,8 +1416,8 @@ def start_ray_processes(address_info=None,
object_store_addresses.append(object_store_address)
time.sleep(0.1)
# Start any local schedulers that do not yet exist.
if not use_raylet:
# Start any local schedulers that do not yet exist.
for i in range(
len(local_scheduler_socket_names), num_local_schedulers):
# Connect the local scheduler to the object store at the same
@@ -1457,21 +1460,20 @@ def start_ray_processes(address_info=None,
assert len(local_scheduler_socket_names) == num_local_schedulers
else:
# Start the raylet. TODO(rkn): Modify this to allow starting
# multiple raylets on the same machine.
raylet_stdout_file, raylet_stderr_file = (new_log_files(
"raylet_{}".format(i), redirect_output=redirect_output))
address_info["raylet_socket_names"] = [
start_raylet(
redis_address,
node_ip_address,
object_store_addresses[i].name,
worker_path,
resources=resources[i],
stdout_file=None,
stderr_file=None,
cleanup=cleanup)
]
# Start any raylets that do not exist yet.
for i in range(len(raylet_socket_names), num_local_schedulers):
raylet_stdout_file, raylet_stderr_file = new_log_files(
"raylet_{}".format(i), redirect_output=redirect_output)
address_info["raylet_socket_names"].append(
start_raylet(
redis_address,
node_ip_address,
object_store_addresses[i].name,
worker_path,
resources=resources[i],
stdout_file=raylet_stdout_file,
stderr_file=raylet_stderr_file,
cleanup=cleanup))
if not use_raylet:
# Start any workers that the local scheduler has not already started.