wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-07-01 03:59:39 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
8f6bd6cece11d799ecdd185d82ec48bb9ad21302
ray/python/ray/services.py
T
Robert Nishihara 0b1608a546 Factor out code for starting new processes and test plasma store in valgrind. (#3824) 
* Factor out starting Ray processes.

* Detect flags through environment variables.

* Return ProcessInfo from start_ray_process.

* Print valgrind errors at exit.

* Test valgrind in travis.

* Some valgrind fixes.

* Undo raylet monitor change.

* Only test plasma store in valgrind.
2019-01-22 14:59:11 -08:00

1375 lines
55 KiB
Python
Raw Blame History

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import logging
import multiprocessing
import os
import random
import resource
import socket
import subprocess
import sys
import time
import redis
import pyarrow
# Ray modules
import ray
import ray.ray_constants as ray_constants
from ray.tempfile_services import (get_ipython_notebook_path, get_temp_root,
new_redis_log_file)
# True if processes are run in the valgrind profiler.
RUN_RAYLET_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/ray/thirdparty/redis/src/redis-server")
REDIS_MODULE = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
"core/src/ray/gcs/redis_module/libray_redis_module.so")
# Location of the credis server and modules.
# credis will be enabled if the environment variable RAY_USE_NEW_GCS is set.
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")
# Location of the raylet executables.
RAYLET_MONITOR_EXECUTABLE = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
"core/src/ray/raylet/raylet_monitor")
RAYLET_EXECUTABLE = os.path.join(
os.path.abspath(os.path.dirname(__file__)), "core/src/ray/raylet/raylet")
# Logger for this module. It should be configured at the entry point
# into the program using Ray. Ray configures it by default automatically
# using logging.basicConfig in its entry/init points.
logger = logging.getLogger(__name__)
ProcessInfo = collections.namedtuple("ProcessInfo", [
"process", "stdout_file", "stderr_file", "use_valgrind", "use_gdb",
"use_valgrind_profiler", "use_perftools_profiler", "use_tmux"
])
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 remaining_processes_alive(exclude=None):
"""See if the remaining processes are alive or not.
Note that this ignores processes that have been explicitly killed,
e.g., via a command like node.kill_raylet().
Returns:
True if the remaining processes started by ray.init() are alive and
False otherwise.
Raises:
Exception: An exception is raised if the processes were not started by
ray.init().
"""
if ray.worker._global_node is None:
raise Exception("This process is not in a position to determine "
"whether all processes are alive or not.")
return ray.worker._global_node.remaining_processes_alive()
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"
# [Errno 101] Network is unreachable
if e.errno == 101:
try:
# try get node ip address from host name
host_name = socket.getfqdn(socket.gethostname())
node_ip_address = socket.gethostbyname(host_name)
except Exception:
pass
return node_ip_address
def record_log_files_in_redis(redis_address,
node_ip_address,
log_files,
password=None):
"""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.
password (str): The password of the redis server.
"""
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, password=password)
# 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, password=None):
"""Create a Redis client.
Args:
The IP address, port, and password 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), password=password)
def start_ray_process(command,
process_type,
env_updates=None,
cwd=None,
use_valgrind=False,
use_gdb=False,
use_valgrind_profiler=False,
use_perftools_profiler=False,
use_tmux=False,
stdout_file=None,
stderr_file=None):
"""Start one of the Ray processes.
TODO(rkn): We need to figure out how these commands interact. For example,
it may only make sense to start a process in gdb if we also start it in
tmux. Similarly, certain combinations probably don't make sense, like
simultaneously running the process in valgrind and the profiler.
Args:
command (List[str]): The command to use to start the Ray process.
process_type (str): The type of the process that is being started
(e.g., "raylet").
env_updates (dict): A dictionary of additional environment variables to
run the command with (in addition to the caller's environment
variables).
cwd (str): The directory to run the process in.
use_valgrind (bool): True if we should start the process in valgrind.
use_gdb (bool): True if we should start the process in gdb.
use_valgrind_profiler (bool): True if we should start the process in
the valgrind profiler.
use_perftools_profiler (bool): True if we should profile the process
using perftools.
use_tmux (bool): True if we should start the process in tmux.
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.
Returns:
Inormation about the process that was started including a handle to the
process that was started.
"""
# Detect which flags are set through environment variables.
valgrind_env_var = "RAY_{}_VALGRIND".format(process_type.upper())
if os.environ.get(valgrind_env_var) == "1":
logger.info("Detected environment variable '%s'.", valgrind_env_var)
use_valgrind = True
valgrind_profiler_env_var = "RAY_{}_VALGRIND_PROFILER".format(
process_type.upper())
if os.environ.get(valgrind_profiler_env_var) == "1":
logger.info("Detected environment variable '%s'.",
valgrind_profiler_env_var)
use_valgrind_profiler = True
perftools_profiler_env_var = "RAY_{}_PERFTOOLS_PROFILER".format(
process_type.upper())
if os.environ.get(perftools_profiler_env_var) == "1":
logger.info("Detected environment variable '%s'.",
perftools_profiler_env_var)
use_perftools_profiler = True
tmux_env_var = "RAY_{}_TMUX".format(process_type.upper())
if os.environ.get(tmux_env_var) == "1":
logger.info("Detected environment variable '%s'.", tmux_env_var)
use_tmux = True
if use_gdb:
raise NotImplementedError
if use_tmux:
raise NotImplementedError
if sum([use_valgrind, use_valgrind_profiler, use_perftools_profiler]) > 1:
raise ValueError(
"At most one of the 'use_valgrind', 'use_valgrind_profiler', and "
"'use_perftools_profiler' flags can be used at a time.")
if env_updates is None:
env_updates = {}
if not isinstance(env_updates, dict):
raise ValueError("The 'env_updates' argument must be a dictionary.")
modified_env = os.environ.copy()
modified_env.update(env_updates)
if use_valgrind:
command = [
"valgrind", "--track-origins=yes", "--leak-check=full",
"--show-leak-kinds=all", "--leak-check-heuristics=stdstring",
"--error-exitcode=1"
] + command
if use_valgrind_profiler:
command = ["valgrind", "--tool=callgrind"] + command
if use_perftools_profiler:
modified_env["LD_PRELOAD"] = os.environ["PERFTOOLS_PATH"]
modified_env["CPUPROFILE"] = os.environ["PERFTOOLS_LOGFILE"]
process = subprocess.Popen(
command,
env=modified_env,
cwd=cwd,
stdout=stdout_file,
stderr=stderr_file)
return ProcessInfo(
process=process,
stdout_file=stdout_file.name if stdout_file is not None else None,
stderr_file=stderr_file.name if stderr_file is not None else None,
use_valgrind=use_valgrind,
use_gdb=use_gdb,
use_valgrind_profiler=use_valgrind_profiler,
use_perftools_profiler=use_perftools_profiler,
use_tmux=use_tmux)
def wait_for_redis_to_start(redis_ip_address,
redis_port,
password=None,
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.
password (str): The password 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, password=password)
# Wait for the Redis server to start.
counter = 0
while counter < num_retries:
try:
# Run some random command and see if it worked.
logger.info(
"Waiting for redis server at {}:{} to respond...".format(
redis_ip_address, redis_port))
redis_client.client_list()
except redis.ConnectionError:
# Wait a little bit.
time.sleep(1)
logger.info("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(ray.utils.decode(redis_reply)))
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:
logger.warning(error_message)
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,
password=None,
use_credis=None,
redis_max_memory=None):
"""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.
password (str): Prevents external clients without the password
from connecting to Redis if provided.
use_credis: If True, additionally load the chain-replicated libraries
into the redis servers. Defaults to None, which means its value is
set by the presence of "RAY_USE_NEW_GCS" in os.environ.
redis_max_memory: The max amount of memory (in bytes) to allow each
redis shard to use. Once the limit is exceeded, redis will start
LRU eviction of entries. This only applies to the sharded redis
tables (task, object, and profile tables). By default, this is
capped at 10GB but can be set higher.
Returns:
A tuple of the address for the primary Redis shard, a list of
addresses for the remaining shards, and the processes that were
started.
"""
redis_stdout_file, redis_stderr_file = new_redis_log_file(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.")
processes = []
if use_credis is None:
use_credis = ("RAY_USE_NEW_GCS" in os.environ)
if use_credis and password is not None:
# TODO(pschafhalter) remove this once credis supports
# authenticating Redis ports
raise Exception("Setting the `redis_password` argument is not "
"supported in credis. To run Ray with "
"password-protected Redis ports, ensure that "
"the environment variable `RAY_USE_NEW_GCS=off`.")
if not use_credis:
assigned_port, p = _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,
password=password,
# Below we use None to indicate no limit on the memory of the
# primary Redis shard.
redis_max_memory=None)
processes.append(p)
else:
assigned_port, p = _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,
executable=CREDIS_EXECUTABLE,
# It is important to load the credis module BEFORE the ray module,
# as the latter contains an extern declaration that the former
# supplies.
modules=[CREDIS_MASTER_MODULE, REDIS_MODULE],
password=password,
# Below we use None to indicate no limit on the memory of the
# primary Redis shard.
redis_max_memory=None)
processes.append(p)
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.
primary_redis_client = redis.StrictRedis(
host=node_ip_address, port=port, password=password)
primary_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.
primary_redis_client.set("RedirectOutput", 1
if redirect_worker_output else 0)
# Store version information in the primary Redis shard.
_put_version_info_in_redis(primary_redis_client)
# Cap the memory of the other redis shards if no limit is provided.
redis_max_memory = (redis_max_memory if redis_max_memory is not None else
ray_constants.DEFAULT_REDIS_MAX_MEMORY_BYTES)
if redis_max_memory < ray_constants.REDIS_MINIMUM_MEMORY_BYTES:
raise ValueError("Attempting to cap Redis memory usage at {} bytes, "
"but the minimum allowed is {} bytes.".format(
redis_max_memory,
ray_constants.REDIS_MINIMUM_MEMORY_BYTES))
# 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_redis_log_file(
redirect_output, shard_number=i)
if not use_credis:
redis_shard_port, p = _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,
password=password,
redis_max_memory=redis_max_memory)
processes.append(p)
else:
assert num_redis_shards == 1, \
"For now, RAY_USE_NEW_GCS supports 1 shard, and credis "\
"supports 1-node chain for that shard only."
redis_shard_port, p = _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,
password=password,
executable=CREDIS_EXECUTABLE,
# It is important to load the credis module BEFORE the ray
# module, as the latter contains an extern declaration that the
# former supplies.
modules=[CREDIS_MEMBER_MODULE, REDIS_MODULE],
redis_max_memory=redis_max_memory)
processes.append(p)
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.
primary_redis_client.rpush("RedisShards", shard_address)
if use_credis:
shard_client = redis.StrictRedis(
host=node_ip_address, port=redis_shard_port, password=password)
# Configure the chain state.
primary_redis_client.execute_command("MASTER.ADD", node_ip_address,
redis_shard_port)
shard_client.execute_command("MEMBER.CONNECT_TO_MASTER",
node_ip_address, port)
return redis_address, redis_shards, processes
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,
password=None,
executable=REDIS_EXECUTABLE,
modules=None,
redis_max_memory=None):
"""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.
password (str): Prevents external clients without the password
from connecting to Redis if provided.
executable (str): Full path tho the redis-server executable.
modules (list of str): A list of pathnames, pointing to the redis
module(s) that will be loaded in this redis server. If None, load
the default Ray redis module.
redis_max_memory: The max amount of memory (in bytes) to allow redis
to use, or None for no limit. Once the limit is exceeded, redis
will start LRU eviction of entries.
Returns:
A tuple of the port used by Redis and ProcessInfo for 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)
if modules is None:
modules = [REDIS_MODULE]
for module in modules:
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()
load_module_args = []
for module in modules:
load_module_args += ["--loadmodule", module]
while counter < num_retries:
if counter > 0:
logger.warning("Redis failed to start, retrying now.")
# Construct the command to start the Redis server.
command = [executable]
if password:
command += ["--requirepass", password]
command += (
["--port", str(port), "--loglevel", "warning"] + load_module_args)
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_REDIS_SERVER,
stdout_file=stdout_file,
stderr_file=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 process_info.process.poll() is None:
break
port = new_port()
counter += 1
if counter == num_retries:
raise Exception("Couldn't start Redis. Check log files: {} {}".format(
stdout_file.name, stderr_file.name))
# Create a Redis client just for configuring Redis.
redis_client = redis.StrictRedis(
host="127.0.0.1", port=port, password=password)
# Wait for the Redis server to start.
wait_for_redis_to_start("127.0.0.1", port, password=password)
# 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")
# Discard old task and object metadata.
if redis_max_memory is not None:
redis_client.config_set("maxmemory", str(redis_max_memory))
redis_client.config_set("maxmemory-policy", "allkeys-lru")
redis_client.config_set("maxmemory-samples", "10")
logger.info("Starting Redis shard with {} GB max memory.".format(
round(redis_max_memory / 1e9, 2)))
# 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],
password=password)
return port, process_info
def start_log_monitor(redis_address,
node_ip_address,
stdout_file=None,
stderr_file=None,
redis_password=None):
"""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.
redis_password (str): The password of the redis server.
Returns:
ProcessInfo for the process that was started.
"""
log_monitor_filepath = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "log_monitor.py")
command = [
sys.executable, "-u", log_monitor_filepath, "--redis-address",
redis_address, "--node-ip-address", node_ip_address
]
if redis_password:
command += ["--redis-password", redis_password]
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_LOG_MONITOR,
stdout_file=stdout_file,
stderr_file=stderr_file)
record_log_files_in_redis(
redis_address,
node_ip_address, [stdout_file, stderr_file],
password=redis_password)
return process_info
def start_ui(redis_address, stdout_file=None, stderr_file=None):
"""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.
Returns:
A tuple of the web UI url and ProcessInfo for the process that was
started.
"""
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
# We generate the token used for authentication ourselves to avoid
# querying the jupyter server.
new_notebook_directory, webui_url, token = (
get_ipython_notebook_path(port))
# The --ip=0.0.0.0 flag is intended to enable connecting to a notebook
# running within a docker container (from the outside).
command = [
"jupyter", "notebook", "--no-browser", "--port={}".format(port),
"--ip=0.0.0.0", "--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:
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_WEB_UI,
env_updates={"REDIS_ADDRESS": redis_address},
cwd=new_notebook_directory,
stdout_file=stdout_file,
stderr_file=stderr_file)
except Exception:
logger.warning("Failed to start the UI, you may need to run "
"'pip install jupyter'.")
else:
logger.info("\n" + "=" * 70)
logger.info("View the web UI at {}".format(webui_url))
logger.info("=" * 70 + "\n")
return webui_url, process_info
return None, None
def check_and_update_resources(num_cpus, num_gpus, resources):
"""Sanity check a resource dictionary and add sensible defaults.
Args:
num_cpus: The number of CPUs.
num_gpus: The number of GPUs.
resources: A dictionary mapping resource names to resource quantities.
Returns:
A new resource dictionary.
"""
if resources is None:
resources = {}
resources = resources.copy()
assert "CPU" not in resources
assert "GPU" not in resources
if num_cpus is not None:
resources["CPU"] = num_cpus
if num_gpus is not None:
resources["GPU"] = num_gpus
if "CPU" not in resources:
# By default, use the number of hardware execution threads for the
# number of cores.
resources["CPU"] = multiprocessing.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))
# Check types.
for _, resource_quantity in resources.items():
assert (isinstance(resource_quantity, int)
or isinstance(resource_quantity, float))
if (isinstance(resource_quantity, float)
and not resource_quantity.is_integer()):
raise ValueError("Resource quantities must all be whole numbers.")
if resource_quantity > ray_constants.MAX_RESOURCE_QUANTITY:
raise ValueError("Resource quantities must be at most {}.".format(
ray_constants.MAX_RESOURCE_QUANTITY))
return resources
def start_raylet(redis_address,
node_ip_address,
raylet_name,
plasma_store_name,
worker_path,
num_cpus=None,
num_gpus=None,
resources=None,
object_manager_port=None,
node_manager_port=None,
redis_password=None,
use_valgrind=False,
use_profiler=False,
stdout_file=None,
stderr_file=None,
config=None):
"""Start a raylet, which is a combined local scheduler and object manager.
Args:
redis_address (str): The address of the primary Redis server.
node_ip_address (str): The IP address of this node.
raylet_name (str): The name of the raylet socket to create.
plasma_store_name (str): The name of the plasma store socket to connect
to.
worker_path (str): The path of the Python file that new worker
processes will execute.
num_cpus: The CPUs allocated for this raylet.
num_gpus: The GPUs allocated for this raylet.
resources: The custom resources allocated for this raylet.
object_manager_port: The port to use for the object manager. If this is
None, then the object manager will choose its own port.
node_manager_port: The port to use for the node manager. If this is
None, then the node manager will choose its own port.
redis_password: The password to use when connecting to Redis.
use_valgrind (bool): True if the raylet should be started inside
of valgrind. If this is True, use_profiler must be False.
use_profiler (bool): True if the raylet should be started inside
a profiler. If this is True, use_valgrind must be False.
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.
config (dict|None): Optional Raylet configuration that will
override defaults in RayConfig.
Returns:
ProcessInfo for the process that was started.
"""
config = config or {}
config_str = ",".join(["{},{}".format(*kv) for kv in config.items()])
if use_valgrind and use_profiler:
raise Exception("Cannot use valgrind and profiler at the same time.")
num_initial_workers = (num_cpus if num_cpus is not None else
multiprocessing.cpu_count())
static_resources = check_and_update_resources(num_cpus, num_gpus,
resources)
# Limit the number of workers that can be started in parallel by the
# raylet. However, make sure it is at least 1.
maximum_startup_concurrency = max(
1, min(multiprocessing.cpu_count(), static_resources["CPU"]))
# Format the resource argument in a form like 'CPU,1.0,GPU,0,Custom,3'.
resource_argument = ",".join(
["{},{}".format(*kv) for kv in static_resources.items()])
gcs_ip_address, gcs_port = redis_address.split(":")
# Create the command that the Raylet will use to start workers.
start_worker_command = ("{} {} "
"--node-ip-address={} "
"--object-store-name={} "
"--raylet-name={} "
"--redis-address={} "
"--temp-dir={}".format(
sys.executable, worker_path, node_ip_address,
plasma_store_name, raylet_name, redis_address,
get_temp_root()))
if redis_password:
start_worker_command += " --redis-password {}".format(redis_password)
# If the object manager port is None, then use 0 to cause the object
# manager to choose its own port.
if object_manager_port is None:
object_manager_port = 0
# If the node manager port is None, then use 0 to cause the node manager
# to choose its own port.
if node_manager_port is None:
node_manager_port = 0
command = [
RAYLET_EXECUTABLE,
raylet_name,
plasma_store_name,
str(object_manager_port),
str(node_manager_port),
node_ip_address,
gcs_ip_address,
gcs_port,
str(num_initial_workers),
str(maximum_startup_concurrency),
resource_argument,
config_str,
start_worker_command,
"", # Worker command for Java, not needed for Python.
redis_password or "",
get_temp_root(),
]
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_RAYLET,
use_valgrind=use_valgrind,
use_gdb=False,
use_valgrind_profiler=use_profiler,
use_perftools_profiler=("RAYLET_PERFTOOLS_PATH" in os.environ),
stdout_file=stdout_file,
stderr_file=stderr_file)
record_log_files_in_redis(
redis_address,
node_ip_address, [stdout_file, stderr_file],
password=redis_password)
return process_info
def determine_plasma_store_config(object_store_memory=None,
plasma_directory=None,
huge_pages=False):
"""Figure out how to configure the plasma object store.
This will determine which directory to use for the plasma store (e.g.,
/tmp or /dev/shm) and how much memory to start the store with. On Linux,
we will try to use /dev/shm unless the shared memory file system is too
small, in which case we will fall back to /tmp. If any of the object store
memory or plasma directory parameters are specified by the user, then those
values will be preserved.
Args:
object_store_memory (int): The user-specified object store memory
parameter.
plasma_directory (str): The user-specified plasma directory parameter.
huge_pages (bool): The user-specified huge pages parameter.
Returns:
A tuple of the object store memory to use and the plasma directory to
use. If either of these values is specified by the user, then that
value will be preserved.
"""
system_memory = ray.utils.get_system_memory()
# Choose a default object store size.
if object_store_memory is None:
object_store_memory = int(system_memory * 0.4)
# Cap memory to avoid memory waste and perf issues on large nodes
if (object_store_memory >
ray_constants.DEFAULT_OBJECT_STORE_MAX_MEMORY_BYTES):
logger.warning(
"Warning: Capping object memory store to {}GB. ".format(
ray_constants.DEFAULT_OBJECT_STORE_MAX_MEMORY_BYTES // 1e9)
+ "To increase this further, specify `object_store_memory` "
"when calling ray.init() or ray start.")
object_store_memory = (
ray_constants.DEFAULT_OBJECT_STORE_MAX_MEMORY_BYTES)
# Determine which directory to use. By default, use /tmp on MacOS and
# /dev/shm on Linux, unless the shared-memory file system is too small,
# in which case we default to /tmp on Linux.
if plasma_directory is None:
if sys.platform == "linux" or sys.platform == "linux2":
shm_avail = ray.utils.get_shared_memory_bytes()
# Compare the requested memory size to the memory available in
# /dev/shm.
if shm_avail > object_store_memory:
plasma_directory = "/dev/shm"
else:
plasma_directory = "/tmp"
logger.warning(
"WARNING: The object store is using /tmp instead of "
"/dev/shm because /dev/shm has only {} bytes available. "
"This may slow down performance! You may be able to free "
"up space by deleting files in /dev/shm or terminating "
"any running plasma_store_server processes. 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))
else:
plasma_directory = "/tmp"
# Do some sanity checks.
if object_store_memory > system_memory:
raise Exception(
"The requested object store memory size is greater "
"than the total available memory.")
else:
plasma_directory = os.path.abspath(plasma_directory)
logger.warning("WARNING: object_store_memory is not verified when "
"plasma_directory is set.")
if not os.path.isdir(plasma_directory):
raise Exception("The file {} does not exist or is not a directory."
.format(plasma_directory))
return object_store_memory, plasma_directory
def _start_plasma_store(plasma_store_memory,
use_valgrind=False,
use_profiler=False,
stdout_file=None,
stderr_file=None,
plasma_directory=None,
huge_pages=False,
socket_name=None):
"""Start a plasma store process.
Args:
plasma_store_memory (int): The amount of memory in bytes to start the
plasma store with.
use_valgrind (bool): True if the plasma store should be started inside
of valgrind. If this is True, use_profiler must be False.
use_profiler (bool): True if the plasma store should be started inside
a profiler. If this is True, use_valgrind must be False.
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.
plasma_directory: A directory where the Plasma memory mapped files will
be created.
huge_pages: a boolean flag indicating whether to start the
Object Store with hugetlbfs support. Requires plasma_directory.
socket_name (str): If provided, it will specify the socket
name used by the plasma store.
Return:
A tuple of the name of the plasma store socket and ProcessInfo for the
plasma store process.
"""
if use_valgrind and use_profiler:
raise Exception("Cannot use valgrind and profiler at the same time.")
if huge_pages and not (sys.platform == "linux"
or sys.platform == "linux2"):
raise Exception("The huge_pages argument is only supported on "
"Linux.")
if huge_pages and plasma_directory is None:
raise Exception("If huge_pages is True, then the "
"plasma_directory argument must be provided.")
if not isinstance(plasma_store_memory, int):
raise Exception("plasma_store_memory should be an integer.")
plasma_store_executable = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
"core/src/plasma/plasma_store_server")
plasma_store_name = socket_name
command = [
plasma_store_executable, "-s", plasma_store_name, "-m",
str(plasma_store_memory)
]
if plasma_directory is not None:
command += ["-d", plasma_directory]
if huge_pages:
command += ["-h"]
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_PLASMA_STORE,
use_valgrind=use_valgrind,
use_valgrind_profiler=use_profiler,
stdout_file=stdout_file,
stderr_file=stderr_file)
return plasma_store_name, process_info
def start_plasma_store(node_ip_address,
redis_address,
stdout_file=None,
stderr_file=None,
object_store_memory=None,
plasma_directory=None,
huge_pages=False,
plasma_store_socket_name=None,
redis_password=None):
"""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.
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.
object_store_memory: The amount of memory (in bytes) to start the
object store with.
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.
redis_password (str): The password of the redis server.
Returns:
ProcessInfo for the process that was started.
"""
object_store_memory, plasma_directory = determine_plasma_store_config(
object_store_memory, plasma_directory, huge_pages)
if object_store_memory < ray_constants.OBJECT_STORE_MINIMUM_MEMORY_BYTES:
raise ValueError("Attempting to cap object store memory usage at {} "
"bytes, but the minimum allowed is {} bytes.".format(
object_store_memory,
ray_constants.OBJECT_STORE_MINIMUM_MEMORY_BYTES))
# Print the object store memory using two decimal places.
object_store_memory_str = (object_store_memory / 10**7) / 10**2
logger.info("Starting the Plasma object store with {} GB memory "
"using {}.".format(object_store_memory_str, plasma_directory))
# Start the Plasma store.
plasma_store_name, process_info = _start_plasma_store(
object_store_memory,
use_profiler=RUN_PLASMA_STORE_PROFILER,
stdout_file=stdout_file,
stderr_file=stderr_file,
plasma_directory=plasma_directory,
huge_pages=huge_pages,
socket_name=plasma_store_socket_name)
record_log_files_in_redis(
redis_address,
node_ip_address, [stdout_file, stderr_file],
password=redis_password)
return process_info
def start_worker(node_ip_address,
object_store_name,
local_scheduler_name,
redis_address,
worker_path,
stdout_file=None,
stderr_file=None):
"""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.
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.
Returns:
ProcessInfo for the process that was started.
"""
command = [
sys.executable, "-u", worker_path,
"--node-ip-address=" + node_ip_address,
"--object-store-name=" + object_store_name,
"--redis-address=" + str(redis_address),
"--temp-dir=" + get_temp_root()
]
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_WORKER,
stdout_file=stdout_file,
stderr_file=stderr_file)
record_log_files_in_redis(redis_address, node_ip_address,
[stdout_file, stderr_file])
return process_info
def start_monitor(redis_address,
node_ip_address,
stdout_file=None,
stderr_file=None,
autoscaling_config=None,
redis_password=None):
"""Run a process to monitor the other processes.
Args:
redis_address (str): The address that the Redis server is listening on.
node_ip_address: The IP address of the node that this process 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.
autoscaling_config: path to autoscaling config file.
redis_password (str): The password of the redis server.
Returns:
ProcessInfo for the process that was started.
"""
monitor_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "monitor.py")
command = [
sys.executable, "-u", monitor_path,
"--redis-address=" + str(redis_address)
]
if autoscaling_config:
command.append("--autoscaling-config=" + str(autoscaling_config))
if redis_password:
command.append("--redis-password=" + redis_password)
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_MONITOR,
stdout_file=stdout_file,
stderr_file=stderr_file)
record_log_files_in_redis(
redis_address,
node_ip_address, [stdout_file, stderr_file],
password=redis_password)
return process_info
def start_raylet_monitor(redis_address,
stdout_file=None,
stderr_file=None,
redis_password=None,
config=None):
"""Run a process to monitor the other processes.
Args:
redis_address (str): The address that the Redis server is listening 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.
redis_password (str): The password of the redis server.
config (dict|None): Optional configuration that will
override defaults in RayConfig.
Returns:
ProcessInfo for the process that was started.
"""
gcs_ip_address, gcs_port = redis_address.split(":")
redis_password = redis_password or ""
config = config or {}
config_str = ",".join(["{},{}".format(*kv) for kv in config.items()])
command = [RAYLET_MONITOR_EXECUTABLE, gcs_ip_address, gcs_port, config_str]
if redis_password:
command += [redis_password]
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_RAYLET_MONITOR,
stdout_file=stdout_file,
stderr_file=stderr_file)
return process_info
Reference in New Issue View Git Blame Copy Permalink