from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import binascii

from collections import namedtuple, OrderedDict

from datetime import datetime

import json

import os

import psutil

import pyarrow

import random

import redis

import resource

import shutil

import signal

import socket

import subprocess

import sys

import time

import threading

# Ray modules

import ray.local_scheduler

import ray.plasma

import ray.global_scheduler as global_scheduler

PROCESS_TYPE_MONITOR = "monitor"

PROCESS_TYPE_LOG_MONITOR = "log_monitor"

PROCESS_TYPE_WORKER = "worker"

PROCESS_TYPE_LOCAL_SCHEDULER = "local_scheduler"

PROCESS_TYPE_PLASMA_MANAGER = "plasma_manager"

PROCESS_TYPE_PLASMA_STORE = "plasma_store"

PROCESS_TYPE_GLOBAL_SCHEDULER = "global_scheduler"

PROCESS_TYPE_REDIS_SERVER = "redis_server"

PROCESS_TYPE_WEB_UI = "web_ui"

# This is a dictionary tracking all of the processes of different types that

# have been started by this services module. Note that the order of the keys is

# important because it determines the order in which these processes will be

# terminated when Ray exits, and certain orders will cause errors to be logged

# to the screen.

all_processes = OrderedDict([(PROCESS_TYPE_MONITOR, []),

(PROCESS_TYPE_LOG_MONITOR, []),

(PROCESS_TYPE_WORKER, []),

(PROCESS_TYPE_LOCAL_SCHEDULER, []),

(PROCESS_TYPE_PLASMA_MANAGER, []),

(PROCESS_TYPE_PLASMA_STORE, []),

(PROCESS_TYPE_GLOBAL_SCHEDULER, []),

(PROCESS_TYPE_REDIS_SERVER, []),

(PROCESS_TYPE_WEB_UI, [])],)

# True if processes are run in the valgrind profiler.

RUN_LOCAL_SCHEDULER_PROFILER = False

RUN_PLASMA_MANAGER_PROFILER = False

RUN_PLASMA_STORE_PROFILER = False

# Location of the redis server and module.

REDIS_EXECUTABLE = os.path.join(

os.path.abspath(os.path.dirname(__file__)),

"core/src/common/thirdparty/redis/src/redis-server")

REDIS_MODULE = os.path.join(

os.path.abspath(os.path.dirname(__file__)),

"core/src/common/redis_module/libray_redis_module.so")

# Location of the credis server and modules.

CREDIS_EXECUTABLE = os.path.join(

os.path.abspath(os.path.dirname(__file__)),

"core/src/credis/redis/src/redis-server")

CREDIS_MASTER_MODULE = os.path.join(

os.path.abspath(os.path.dirname(__file__)),

"core/src/credis/build/src/libmaster.so")

CREDIS_MEMBER_MODULE = os.path.join(

os.path.abspath(os.path.dirname(__file__)),

"core/src/credis/build/src/libmember.so")

# ObjectStoreAddress tuples contain all information necessary to connect to an

# object store. The fields are:

# - name: The socket name for the object store

# - manager_name: The socket name for the object store manager

# - manager_port: The Internet port that the object store manager listens on

ObjectStoreAddress = namedtuple("ObjectStoreAddress", ["name",

"manager_name",

"manager_port"])

def address(ip_address, port):

return ip_address + ":" + str(port)

def get_ip_address(address):

assert type(address) == str, "Address must be a string"

ip_address = address.split(":")[0]

return ip_address

def get_port(address):

try:

port = int(address.split(":")[1])

except Exception:

raise Exception("Unable to parse port from address {}".format(address))

return port

def new_port():

return random.randint(10000, 65535)

def random_name():

return str(random.randint(0, 99999999))

def kill_process(p):

"""Kill a process.

Args:

p: The process to kill.

Returns:

True if the process was killed successfully and false otherwise.

"""

if p.poll() is not None:

# The process has already terminated.

return True

if any([RUN_LOCAL_SCHEDULER_PROFILER, RUN_PLASMA_MANAGER_PROFILER,

RUN_PLASMA_STORE_PROFILER]):

# Give process signal to write profiler data.

os.kill(p.pid, signal.SIGINT)

# Wait for profiling data to be written.

time.sleep(0.1)

# Allow the process one second to exit gracefully.

p.terminate()

timer = threading.Timer(1, lambda p: p.kill(), [p])

try:

timer.start()

p.wait()

finally:

timer.cancel()

if p.poll() is not None:

return True

# If the process did not exit within one second, force kill it.

p.kill()

if p.poll() is not None:

return True

# The process was not killed for some reason.

return False

def cleanup():

"""When running in local mode, shutdown the Ray processes.

This method is used to shutdown processes that were started with

services.start_ray_head(). It kills all scheduler, object store, and worker

processes that were started by this services module. Driver processes are

started and disconnected by worker.py.

"""

successfully_shut_down = True

# Terminate the processes in reverse order.

for process_type in all_processes.keys():

# Kill all of the processes of a certain type.

for p in all_processes[process_type]:

success = kill_process(p)

successfully_shut_down = successfully_shut_down and success

# Reset the list of processes of this type.

all_processes[process_type] = []

if not successfully_shut_down:

print("Ray did not shut down properly.")

def all_processes_alive(exclude=[]):

"""Check if all of the processes are still alive.

Args:

exclude: Don't check the processes whose types are in this list.

"""

for process_type, processes in all_processes.items():

# Note that p.poll() returns the exit code that the process exited

# with, so an exit code of None indicates that the process is still

# alive.

processes_alive = [p.poll() is None for p in processes]

if (not all(processes_alive) and process_type not in exclude):

print("A process of type {} has died.".format(process_type))

return False

return True

def address_to_ip(address):

"""Convert a hostname to a numerical IP addresses in an address.

This should be a no-op if address already contains an actual numerical IP

address.

Args:

address: This can be either a string containing a hostname (or an IP

address) and a port or it can be just an IP address.

Returns:

The same address but with the hostname replaced by a numerical IP

address.

"""

address_parts = address.split(":")

ip_address = socket.gethostbyname(address_parts[0])

# Make sure localhost isn't resolved to the loopback ip

if ip_address == "127.0.0.1":

ip_address = get_node_ip_address()

return ":".join([ip_address] + address_parts[1:])

def get_node_ip_address(address="8.8.8.8:53"):

"""Determine the IP address of the local node.

Args:

address (str): The IP address and port of any known live service on the

network you care about.

Returns:

The IP address of the current node.

"""

ip_address, port = address.split(":")

s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)

try:

# This command will raise an exception if there is no internet

# connection.

s.connect((ip_address, int(port)))

node_ip_address = s.getsockname()[0]

except Exception as e:

node_ip_address = "127.0.0.1"

return node_ip_address

def record_log_files_in_redis(redis_address, node_ip_address, log_files):

"""Record in Redis that a new log file has been created.

This is used so that each log monitor can check Redis and figure out which

log files it is reponsible for monitoring.

Args:

redis_address: The address of the redis server.

node_ip_address: The IP address of the node that the log file exists

on.

log_files: A list of file handles for the log files. If one of the file

handles is None, we ignore it.

"""

for log_file in log_files:

if log_file is not None:

redis_ip_address, redis_port = redis_address.split(":")

redis_client = redis.StrictRedis(host=redis_ip_address,

port=redis_port)

# The name of the key storing the list of log filenames for this IP

# address.

log_file_list_key = "LOG_FILENAMES:{}".format(node_ip_address)

redis_client.rpush(log_file_list_key, log_file.name)

def create_redis_client(redis_address):

"""Create a Redis client.

Args:

The IP address and port of the Redis server.

Returns:

A Redis client.

"""

redis_ip_address, redis_port = redis_address.split(":")

# For this command to work, some other client (on the same machine

# as Redis) must have run "CONFIG SET protected-mode no".

return redis.StrictRedis(host=redis_ip_address, port=int(redis_port))

def wait_for_redis_to_start(redis_ip_address, redis_port, num_retries=5):

"""Wait for a Redis server to be available.

This is accomplished by creating a Redis client and sending a random

command to the server until the command gets through.

Args:

redis_ip_address (str): The IP address of the redis server.

redis_port (int): The port of the redis server.

num_retries (int): The number of times to try connecting with redis.

The client will sleep for one second between attempts.

Raises:

Exception: An exception is raised if we could not connect with Redis.

"""

redis_client = redis.StrictRedis(host=redis_ip_address, port=redis_port)

# Wait for the Redis server to start.

counter = 0

while counter < num_retries:

try:

# Run some random command and see if it worked.

print("Waiting for redis server at {}:{} to respond..."

.format(redis_ip_address, redis_port))

redis_client.client_list()

except redis.ConnectionError as e:

# Wait a little bit.

time.sleep(1)

print("Failed to connect to the redis server, retrying.")

counter += 1

else:

break

if counter == num_retries:

raise Exception("Unable to connect to Redis. If the Redis instance is "

"on a different machine, check that your firewall is "

"configured properly.")

def _autodetect_num_gpus():

"""Attempt to detect the number of GPUs on this machine.

TODO(rkn): This currently assumes Nvidia GPUs and Linux.

Returns:

The number of GPUs if any were detected, otherwise 0.

"""

proc_gpus_path = "/proc/driver/nvidia/gpus"

if os.path.isdir(proc_gpus_path):

return len(os.listdir(proc_gpus_path))

return 0

def _compute_version_info():

"""Compute the versions of Python, pyarrow, and Ray.

Returns:

A tuple containing the version information.

"""

ray_version = ray.__version__

python_version = ".".join(map(str, sys.version_info[:3]))

pyarrow_version = pyarrow.__version__

return (ray_version, python_version, pyarrow_version)

def _put_version_info_in_redis(redis_client):

"""Store version information in Redis.

This will be used to detect if workers or drivers are started using

different versions of Python, pyarrow, or Ray.

Args:

redis_client: A client for the primary Redis shard.

"""

redis_client.set("VERSION_INFO", json.dumps(_compute_version_info()))

def check_version_info(redis_client):

"""Check if various version info of this process is correct.

This will be used to detect if workers or drivers are started using

different versions of Python, pyarrow, or Ray. If the version

information is not present in Redis, then no check is done.

Args:

redis_client: A client for the primary Redis shard.

Raises:

Exception: An exception is raised if there is a version mismatch.

"""

redis_reply = redis_client.get("VERSION_INFO")

# Don't do the check if there is no version information in Redis. This

# is to make it easier to do things like start the processes by hand.

if redis_reply is None:

return

true_version_info = tuple(json.loads(redis_reply.decode("ascii")))

version_info = _compute_version_info()

if version_info != true_version_info:

node_ip_address = ray.services.get_node_ip_address()

error_message = ("Version mismatch: The cluster was started with:\n"

" Ray: " + true_version_info[0] + "\n"

" Python: " + true_version_info[1] + "\n"

" Pyarrow: " + str(true_version_info[2]) + "\n"

"This process on node " + node_ip_address +

" was started with:" + "\n"

" Ray: " + version_info[0] + "\n"

" Python: " + version_info[1] + "\n"

" Pyarrow: " + str(version_info[2]))

if version_info[:2] != true_version_info[:2]:

raise Exception(error_message)

else:

print(error_message)

def start_credis(node_ip_address,

port=None,

redirect_output=False,

cleanup=True):

"""Start the credis global state store.

Credis is a chain replicated reliable redis store. It consists

of one master process that acts as a controller and a number of

chain members (currently two, the head and the tail).

Args:

node_ip_address: The IP address of the current node. This is only used

for recording the log filenames in Redis.

port (int): If provided, the primary Redis shard will be started on

this port.

redirect_output (bool): True if output should be redirected to a file

and false otherwise.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then all Redis processes started by this method will be killed by

services.cleanup() when the Python process that imported services

exits.

Returns:

The address (ip_address:port) of the credis master process.

"""

components = ["credis_master", "credis_head", "credis_tail"]

modules = [CREDIS_MASTER_MODULE, CREDIS_MEMBER_MODULE,

CREDIS_MEMBER_MODULE]

ports = []

for i, component in enumerate(components):

stdout_file, stderr_file = new_log_files(

component, redirect_output)

new_port, _ = start_redis_instance(

node_ip_address=node_ip_address, port=port,

stdout_file=stdout_file, stderr_file=stderr_file,

cleanup=cleanup,

module=modules[i],

executable=CREDIS_EXECUTABLE)

ports.append(new_port)

[master_port, head_port, tail_port] = ports

# Connect the members to the master

master_client = redis.StrictRedis(host=node_ip_address, port=master_port)

master_client.execute_command("MASTER.ADD", node_ip_address, head_port)

master_client.execute_command("MASTER.ADD", node_ip_address, tail_port)

return address(node_ip_address, master_port)

def start_redis(node_ip_address,

port=None,

redis_shard_ports=None,

num_redis_shards=1,

redis_max_clients=None,

redirect_output=False,

redirect_worker_output=False,

cleanup=True):

"""Start the Redis global state store.

Args:

node_ip_address: The IP address of the current node. This is only used

for recording the log filenames in Redis.

port (int): If provided, the primary Redis shard will be started on

this port.

redis_shard_ports: A list of the ports to use for the non-primary Redis

shards.

num_redis_shards (int): If provided, the number of Redis shards to

start, in addition to the primary one. The default value is one

shard.

redis_max_clients: If this is provided, Ray will attempt to configure

Redis with this maxclients number.

redirect_output (bool): True if output should be redirected to a file

and false otherwise.

redirect_worker_output (bool): True if worker output should be

redirected to a file and false otherwise. Workers will have access

to this value when they start up.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then all Redis processes started by this method will be killed by

services.cleanup() when the Python process that imported services

exits.

Returns:

A tuple of the address for the primary Redis shard and a list of

addresses for the remaining shards.

"""

redis_stdout_file, redis_stderr_file = new_log_files(

"redis", redirect_output)

if redis_shard_ports is None:

redis_shard_ports = num_redis_shards * [None]

elif len(redis_shard_ports) != num_redis_shards:

raise Exception("The number of Redis shard ports does not match the "

"number of Redis shards.")

assigned_port, _ = start_redis_instance(

node_ip_address=node_ip_address, port=port,

redis_max_clients=redis_max_clients,

stdout_file=redis_stdout_file, stderr_file=redis_stderr_file,

cleanup=cleanup)

if port is not None:

assert assigned_port == port

port = assigned_port

redis_address = address(node_ip_address, port)

# Register the number of Redis shards in the primary shard, so that clients

# know how many redis shards to expect under RedisShards.

redis_client = redis.StrictRedis(host=node_ip_address, port=port)

redis_client.set("NumRedisShards", str(num_redis_shards))

# Put the redirect_worker_output bool in the Redis shard so that workers

# can access it and know whether or not to redirect their output.

redis_client.set("RedirectOutput", 1 if redirect_worker_output else 0)

# Store version information in the primary Redis shard.

_put_version_info_in_redis(redis_client)

# Start other Redis shards. Each Redis shard logs to a separate file,

# prefixed by "redis-<shard number>".

redis_shards = []

for i in range(num_redis_shards):

redis_stdout_file, redis_stderr_file = new_log_files(

"redis-{}".format(i), redirect_output)

redis_shard_port, _ = start_redis_instance(

node_ip_address=node_ip_address,

port=redis_shard_ports[i],

redis_max_clients=redis_max_clients,

stdout_file=redis_stdout_file, stderr_file=redis_stderr_file,

cleanup=cleanup)

if redis_shard_ports[i] is not None:

assert redis_shard_port == redis_shard_ports[i]

shard_address = address(node_ip_address, redis_shard_port)

redis_shards.append(shard_address)

# Store redis shard information in the primary redis shard.

redis_client.rpush("RedisShards", shard_address)

return redis_address, redis_shards

def start_redis_instance(node_ip_address="127.0.0.1",

port=None,

redis_max_clients=None,

num_retries=20,

stdout_file=None,

stderr_file=None,

cleanup=True,

executable=REDIS_EXECUTABLE,

module=REDIS_MODULE):

"""Start a single Redis server.

Args:

node_ip_address (str): The IP address of the current node. This is only

used for recording the log filenames in Redis.

port (int): If provided, start a Redis server with this port.

redis_max_clients: If this is provided, Ray will attempt to configure

Redis with this maxclients number.

num_retries (int): The number of times to attempt to start Redis. If a

port is provided, this defaults to 1.

stdout_file: A file handle opened for writing to redirect stdout to. If

no redirection should happen, then this should be None.

stderr_file: A file handle opened for writing to redirect stderr to. If

no redirection should happen, then this should be None.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by serices.cleanup() when the

Python process that imported services exits.

executable (str): Full path tho the redis-server executable.

module (str): Full path to the redis module that will be loaded in this

redis server.

Returns:

A tuple of the port used by Redis and a handle to the process that was

started. If a port is passed in, then the returned port value is

the same.

Raises:

Exception: An exception is raised if Redis could not be started.

"""

assert os.path.isfile(executable)

assert os.path.isfile(module)

counter = 0

if port is not None:

# If a port is specified, then try only once to connect.

num_retries = 1

else:

port = new_port()

while counter < num_retries:

if counter > 0:

print("Redis failed to start, retrying now.")

p = subprocess.Popen([executable,

"--port", str(port),

"--loglevel", "warning",

"--loadmodule", module],

stdout=stdout_file, stderr=stderr_file)

time.sleep(0.1)

# Check if Redis successfully started (or at least if it the executable

# did not exit within 0.1 seconds).

if p.poll() is None:

if cleanup:

all_processes[PROCESS_TYPE_REDIS_SERVER].append(p)

break

port = new_port()

counter += 1

if counter == num_retries:

raise Exception("Couldn't start Redis.")

# Create a Redis client just for configuring Redis.

redis_client = redis.StrictRedis(host="127.0.0.1", port=port)

# Wait for the Redis server to start.

wait_for_redis_to_start("127.0.0.1", port)

# Configure Redis to generate keyspace notifications. TODO(rkn): Change

# this to only generate notifications for the export keys.

redis_client.config_set("notify-keyspace-events", "Kl")

# Configure Redis to not run in protected mode so that processes on other

# hosts can connect to it. TODO(rkn): Do this in a more secure way.

redis_client.config_set("protected-mode", "no")

# If redis_max_clients is provided, attempt to raise the number of maximum

# number of Redis clients.

if redis_max_clients is not None:

redis_client.config_set("maxclients", str(redis_max_clients))

else:

# If redis_max_clients is not provided, determine the current ulimit.

# We will use this to attempt to raise the maximum number of Redis

# clients.

current_max_clients = int(

redis_client.config_get("maxclients")["maxclients"])

# The below command should be the same as doing ulimit -n.

ulimit_n = resource.getrlimit(resource.RLIMIT_NOFILE)[0]

# The quantity redis_client_buffer appears to be the required buffer

# between the maximum number of redis clients and ulimit -n. That is,

# if ulimit -n returns 10000, then we can set maxclients to

# 10000 - redis_client_buffer.

redis_client_buffer = 32

if current_max_clients < ulimit_n - redis_client_buffer:

redis_client.config_set("maxclients",

ulimit_n - redis_client_buffer)

# Increase the hard and soft limits for the redis client pubsub buffer to

# 128MB. This is a hack to make it less likely for pubsub messages to be

# dropped and for pubsub connections to therefore be killed.

cur_config = (redis_client.config_get("client-output-buffer-limit")

["client-output-buffer-limit"])

cur_config_list = cur_config.split()

assert len(cur_config_list) == 12

cur_config_list[8:] = ["pubsub", "134217728", "134217728", "60"]

redis_client.config_set("client-output-buffer-limit",

" ".join(cur_config_list))

# Put a time stamp in Redis to indicate when it was started.

redis_client.set("redis_start_time", time.time())

# Record the log files in Redis.

record_log_files_in_redis(address(node_ip_address, port), node_ip_address,

[stdout_file, stderr_file])

return port, p

def start_log_monitor(redis_address, node_ip_address, stdout_file=None,

stderr_file=None, cleanup=cleanup):

"""Start a log monitor process.

Args:

redis_address (str): The address of the Redis instance.

node_ip_address (str): The IP address of the node that this log monitor

is running on.

stdout_file: A file handle opened for writing to redirect stdout to. If

no redirection should happen, then this should be None.

stderr_file: A file handle opened for writing to redirect stderr to. If

no redirection should happen, then this should be None.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by services.cleanup() when the

Python process that imported services exits.

"""

log_monitor_filepath = os.path.join(

os.path.dirname(os.path.abspath(__file__)),

"log_monitor.py")

p = subprocess.Popen([sys.executable, "-u", log_monitor_filepath,

"--redis-address", redis_address,

"--node-ip-address", node_ip_address],

stdout=stdout_file, stderr=stderr_file)

if cleanup:

all_processes[PROCESS_TYPE_LOG_MONITOR].append(p)

record_log_files_in_redis(redis_address, node_ip_address,

[stdout_file, stderr_file])

def start_global_scheduler(redis_address, node_ip_address,

stdout_file=None, stderr_file=None, cleanup=True):

"""Start a global scheduler process.

Args:

redis_address (str): The address of the Redis instance.

node_ip_address: The IP address of the node that this scheduler will

run on.

stdout_file: A file handle opened for writing to redirect stdout to. If

no redirection should happen, then this should be None.

stderr_file: A file handle opened for writing to redirect stderr to. If

no redirection should happen, then this should be None.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by services.cleanup() when the

Python process that imported services exits.

"""

p = global_scheduler.start_global_scheduler(redis_address,

node_ip_address,

stdout_file=stdout_file,

stderr_file=stderr_file)

if cleanup:

all_processes[PROCESS_TYPE_GLOBAL_SCHEDULER].append(p)

record_log_files_in_redis(redis_address, node_ip_address,

[stdout_file, stderr_file])

def start_ui(redis_address, stdout_file=None, stderr_file=None, cleanup=True):

"""Start a UI process.

Args:

redis_address: The address of the primary Redis shard.

stdout_file: A file handle opened for writing to redirect stdout to. If

no redirection should happen, then this should be None.

stderr_file: A file handle opened for writing to redirect stderr to. If

no redirection should happen, then this should be None.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by services.cleanup() when the

Python process that imported services exits.

"""

new_env = os.environ.copy()

notebook_filepath = os.path.join(

os.path.dirname(os.path.abspath(__file__)),

"WebUI.ipynb")

# We copy the notebook file so that the original doesn't get modified by

# the user.

random_ui_id = random.randint(0, 100000)

new_notebook_filepath = "/tmp/raylogs/ray_ui{}.ipynb".format(random_ui_id)

new_notebook_directory = os.path.dirname(new_notebook_filepath)

shutil.copy(notebook_filepath, new_notebook_filepath)

port = 8888

while True:

try:

port_test_socket = socket.socket()

port_test_socket.bind(("127.0.0.1", port))

port_test_socket.close()

break

except socket.error:

port += 1

new_env = os.environ.copy()

new_env["REDIS_ADDRESS"] = redis_address

# We generate the token used for authentication ourselves to avoid

# querying the jupyter server.

token = binascii.hexlify(os.urandom(24)).decode("ascii")

command = ["jupyter", "notebook", "--no-browser",

"--port={}".format(port),

"--NotebookApp.iopub_data_rate_limit=10000000000",

"--NotebookApp.open_browser=False",

"--NotebookApp.token={}".format(token)]

# If the user is root, add the --allow-root flag.

if os.geteuid() == 0:

command.append("--allow-root")

try:

ui_process = subprocess.Popen(command, env=new_env,

cwd=new_notebook_directory,

stdout=stdout_file, stderr=stderr_file)

except Exception:

print("Failed to start the UI, you may need to run "

"'pip install jupyter'.")

else:

if cleanup:

all_processes[PROCESS_TYPE_WEB_UI].append(ui_process)

webui_url = ("http://localhost:{}/notebooks/ray_ui{}.ipynb?token={}"

.format(port, random_ui_id, token))

print("\n" + "=" * 70)

print("View the web UI at {}".format(webui_url))

print("=" * 70 + "\n")

return webui_url

def start_local_scheduler(redis_address,

node_ip_address,

plasma_store_name,

plasma_manager_name,

worker_path,

plasma_address=None,

stdout_file=None,

stderr_file=None,

cleanup=True,

resources=None,

num_workers=0):

"""Start a local scheduler process.

Args:

redis_address (str): The address of the Redis instance.

node_ip_address (str): The IP address of the node that this local

scheduler is running on.

plasma_store_name (str): The name of the plasma store socket to connect

to.

plasma_manager_name (str): The name of the plasma manager socket to

connect to.

worker_path (str): The path of the script to use when the local

scheduler starts up new workers.

stdout_file: A file handle opened for writing to redirect stdout to. If

no redirection should happen, then this should be None.

stderr_file: A file handle opened for writing to redirect stderr to. If

no redirection should happen, then this should be None.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by serices.cleanup() when the

Python process that imported services exits.

resources: A dictionary mapping the name of a resource to the available

quantity of that resource.

num_workers (int): The number of workers that the local scheduler

should start.

Return:

The name of the local scheduler socket.

"""

if resources is None:

resources = {}

if "CPU" not in resources:

# By default, use the number of hardware execution threads for the

# number of cores.

resources["CPU"] = psutil.cpu_count()

# See if CUDA_VISIBLE_DEVICES has already been set.

gpu_ids = ray.utils.get_cuda_visible_devices()

# Check that the number of GPUs that the local scheduler wants doesn't

# excede the amount allowed by CUDA_VISIBLE_DEVICES.

if ("GPU" in resources and gpu_ids is not None and

resources["GPU"] > len(gpu_ids)):

raise Exception("Attempting to start local scheduler with {} GPUs, "

"but CUDA_VISIBLE_DEVICES contains {}.".format(

resources["GPU"], gpu_ids))

if "GPU" not in resources:

# Try to automatically detect the number of GPUs.

resources["GPU"] = _autodetect_num_gpus()

# Don't use more GPUs than allowed by CUDA_VISIBLE_DEVICES.

if gpu_ids is not None:

resources["GPU"] = min(resources["GPU"], len(gpu_ids))

print("Starting local scheduler with the following resources: {}."

.format(resources))

local_scheduler_name, p = ray.local_scheduler.start_local_scheduler(

plasma_store_name,

plasma_manager_name,

worker_path=worker_path,

node_ip_address=node_ip_address,

redis_address=redis_address,

plasma_address=plasma_address,

use_profiler=RUN_LOCAL_SCHEDULER_PROFILER,

stdout_file=stdout_file,

stderr_file=stderr_file,

static_resources=resources,

num_workers=num_workers)

if cleanup:

all_processes[PROCESS_TYPE_LOCAL_SCHEDULER].append(p)

record_log_files_in_redis(redis_address, node_ip_address,

[stdout_file, stderr_file])

return local_scheduler_name

def start_objstore(node_ip_address, redis_address,

object_manager_port=None, store_stdout_file=None,

store_stderr_file=None, manager_stdout_file=None,

manager_stderr_file=None, objstore_memory=None,

cleanup=True, plasma_directory=None,

huge_pages=False):

"""This method starts an object store process.

Args:

node_ip_address (str): The IP address of the node running the object

store.

redis_address (str): The address of the Redis instance to connect to.

object_manager_port (int): The port to use for the object manager. If

this is not provided, one will be generated randomly.

store_stdout_file: A file handle opened for writing to redirect stdout

to. If no redirection should happen, then this should be None.

store_stderr_file: A file handle opened for writing to redirect stderr

to. If no redirection should happen, then this should be None.

manager_stdout_file: A file handle opened for writing to redirect

stdout to. If no redirection should happen, then this should be

None.

manager_stderr_file: A file handle opened for writing to redirect

stderr to. If no redirection should happen, then this should be

None.

objstore_memory: The amount of memory (in bytes) to start the object

store with.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by serices.cleanup() when the

Python process that imported services exits.

plasma_directory: A directory where the Plasma memory mapped files will

be created.

huge_pages: Boolean flag indicating whether to start the Object

Store with hugetlbfs support. Requires plasma_directory.

Return:

A tuple of the Plasma store socket name, the Plasma manager socket

name, and the plasma manager port.

"""

if objstore_memory is None:

# Compute a fraction of the system memory for the Plasma store to use.

system_memory = psutil.virtual_memory().total

if sys.platform == "linux" or sys.platform == "linux2":

# On linux we use /dev/shm, its size is half the size of the

# physical memory. To not overflow it, we set the plasma memory

# limit to 0.4 times the size of the physical memory.

objstore_memory = int(system_memory * 0.4)

# Compare the requested memory size to the memory available in

# /dev/shm.

shm_fd = os.open("/dev/shm", os.O_RDONLY)

try:

shm_fs_stats = os.fstatvfs(shm_fd)

# The value shm_fs_stats.f_bsize is the block size and the

# value shm_fs_stats.f_bavail is the number of available

# blocks.

shm_avail = shm_fs_stats.f_bsize * shm_fs_stats.f_bavail

if objstore_memory > shm_avail:

print("Warning: Reducing object store memory because "

"/dev/shm has only {} bytes available. You may be "

"able to free up space by deleting files in "

"/dev/shm. If you are inside a Docker container, "

"you may need to pass an argument with the flag "

"'--shm-size' to 'docker run'.".format(shm_avail))

objstore_memory = int(shm_avail * 0.8)

finally:

os.close(shm_fd)

else:

objstore_memory = int(system_memory * 0.8)

# Start the Plasma store.

plasma_store_name, p1 = ray.plasma.start_plasma_store(

plasma_store_memory=objstore_memory,

use_profiler=RUN_PLASMA_STORE_PROFILER,

stdout_file=store_stdout_file,

stderr_file=store_stderr_file,

plasma_directory=plasma_directory,

huge_pages=huge_pages)

# Start the plasma manager.

if object_manager_port is not None:

(plasma_manager_name, p2,

plasma_manager_port) = ray.plasma.start_plasma_manager(

plasma_store_name,

redis_address,

plasma_manager_port=object_manager_port,

node_ip_address=node_ip_address,

num_retries=1,

run_profiler=RUN_PLASMA_MANAGER_PROFILER,

stdout_file=manager_stdout_file,

stderr_file=manager_stderr_file)

assert plasma_manager_port == object_manager_port

else:

(plasma_manager_name, p2,

plasma_manager_port) = ray.plasma.start_plasma_manager(

plasma_store_name,

redis_address,

node_ip_address=node_ip_address,

run_profiler=RUN_PLASMA_MANAGER_PROFILER,

stdout_file=manager_stdout_file,

stderr_file=manager_stderr_file)

if cleanup:

all_processes[PROCESS_TYPE_PLASMA_STORE].append(p1)

all_processes[PROCESS_TYPE_PLASMA_MANAGER].append(p2)

record_log_files_in_redis(redis_address, node_ip_address,

[store_stdout_file, store_stderr_file,

manager_stdout_file, manager_stderr_file])

return ObjectStoreAddress(plasma_store_name, plasma_manager_name,

plasma_manager_port)

def start_worker(node_ip_address, object_store_name, object_store_manager_name,

local_scheduler_name, redis_address, worker_path,

stdout_file=None, stderr_file=None, cleanup=True):

"""This method starts a worker process.

Args:

node_ip_address (str): The IP address of the node that this worker is

running on.

object_store_name (str): The name of the object store.

object_store_manager_name (str): The name of the object store manager.

local_scheduler_name (str): The name of the local scheduler.

redis_address (str): The address that the Redis server is listening on.

worker_path (str): The path of the source code which the worker process

will run.

stdout_file: A file handle opened for writing to redirect stdout to. If

no redirection should happen, then this should be None.

stderr_file: A file handle opened for writing to redirect stderr to. If

no redirection should happen, then this should be None.

cleanup (bool): True if using Ray in local mode. If cleanup is true,

then this process will be killed by services.cleanup() when the

Python process that imported services exits. This is True by

default.

"""

command = [sys.executable,

"-u",

worker_path,

"--node-ip-address=" + node_ip_address,

"--object-store-name=" + object_store_name,

"--object-store-manager-name=" + object_store_manager_name,

"--local-scheduler-name=" + local_scheduler_name,

"--redis-address=" + str(redis_address)]

p = subprocess.Popen(command, stdout=stdout_file, stderr=stderr_file)

if cleanup:

all_processes[PROCESS_TYPE_WORKER].append(p)

record_log_files_in_redis(redis_address, node_ip_address,

[stdout_file, stderr_file])

def start_monitor(redis_address, node_ip_address, stdout_file=None,

stderr_file=None, cleanup=True, autoscaling_config=None):

"""Run a process to monitor the other processes.