def resolve(self, is_head):
"""Returns a copy with values filled out with system defaults."""
resources = (self.resources or {}).copy()
assert "CPU" not in resources, resources
assert "GPU" not in resources, resources
assert "memory" not in resources, resources
assert "object_store_memory" not in resources, resources
num_cpus = self.num_cpus
if num_cpus is None:
num_cpus = multiprocessing.cpu_count()
num_gpus = self.num_gpus
gpu_ids = ray.utils.get_cuda_visible_devices()
# Check that the number of GPUs that the raylet wants doesn't
# excede the amount allowed by CUDA_VISIBLE_DEVICES.
if (num_gpus is not None and gpu_ids is not None
and num_gpus > len(gpu_ids)):
raise Exception("Attempting to start raylet with {} GPUs, "
"but CUDA_VISIBLE_DEVICES contains {}.".format(
num_gpus, gpu_ids))
if num_gpus is None:
# Try to automatically detect the number of GPUs.
num_gpus = _autodetect_num_gpus()
# Don't use more GPUs than allowed by CUDA_VISIBLE_DEVICES.
if gpu_ids is not None:
num_gpus = min(num_gpus, len(gpu_ids))
# Choose a default object store size.
system_memory = ray.utils.get_system_memory()
avail_memory = ray.utils.estimate_available_memory()
object_store_memory = self.object_store_memory
if object_store_memory is None:
object_store_memory = int(avail_memory * 0.3)
# 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)
redis_max_memory = self.redis_max_memory
if redis_max_memory is None:
redis_max_memory = min(
ray_constants.DEFAULT_REDIS_MAX_MEMORY_BYTES,
max(int(avail_memory * 0.1),
ray_constants.REDIS_MINIMUM_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))
memory = self.memory
if memory is None:
memory = (avail_memory - object_store_memory -
(redis_max_memory if is_head else 0))
if memory < 100e6 and memory < 0.05 * system_memory:
raise ValueError(
"After taking into account object store and redis memory "
"usage, the amount of memory on this node available for "
"tasks and actors ({} GB) is less than {}% of total. "
"You can adjust these settings with "
"ray.init(memory=<bytes>, "
"object_store_memory=<bytes>).".format(
round(memory / 1e9, 2),
int(100 * (memory / system_memory))))
logger.info(
"Starting Ray with {} GiB memory available for workers and up to "
"{} GiB for objects. You can adjust these settings "
"with ray.init(memory=<bytes>, "
"object_store_memory=<bytes>).".format(
round(
ray_constants.round_to_memory_units(
memory, round_up=False) / (1024**3), 2),
round(object_store_memory / (1024**3), 2)))
spec = ResourceSpec(num_cpus, num_gpus, memory, object_store_memory,
resources, redis_max_memory)
assert spec.resolved()
return spec
def resolve(self, is_head, node_ip_address=None):
"""Returns a copy with values filled out with system defaults.
Args:
is_head (bool): Whether this is the head node.
node_ip_address (str): The IP address of the node that we are on.
This is used to automatically create a node id resource.
"""
resources = (self.resources or {}).copy()
assert "CPU" not in resources, resources
assert "GPU" not in resources, resources
assert "memory" not in resources, resources
assert "object_store_memory" not in resources, resources
if node_ip_address is None:
node_ip_address = ray.services.get_node_ip_address()
# Automatically create a node id resource on each node. This is
# queryable with ray.state.node_ids() and ray.state.current_node_id().
resources[NODE_ID_PREFIX + node_ip_address] = 1.0
num_cpus = self.num_cpus
if num_cpus is None:
num_cpus = multiprocessing.cpu_count()
num_gpus = self.num_gpus
gpu_ids = ray.utils.get_cuda_visible_devices()
# Check that the number of GPUs that the raylet wants doesn't
# excede the amount allowed by CUDA_VISIBLE_DEVICES.
if (num_gpus is not None and gpu_ids is not None
and num_gpus > len(gpu_ids)):
raise ValueError("Attempting to start raylet with {} GPUs, "
"but CUDA_VISIBLE_DEVICES contains {}.".format(
num_gpus, gpu_ids))
if num_gpus is None:
# Try to automatically detect the number of GPUs.
num_gpus = _autodetect_num_gpus()
# Don't use more GPUs than allowed by CUDA_VISIBLE_DEVICES.
if gpu_ids is not None:
num_gpus = min(num_gpus, len(gpu_ids))
try:
info_string = _get_gpu_info_string()
gpu_types = _constraints_from_gpu_info(info_string)
resources.update(gpu_types)
except Exception:
logger.exception("Could not parse gpu information.")
# Choose a default object store size.
system_memory = ray.utils.get_system_memory()
avail_memory = ray.utils.estimate_available_memory()
object_store_memory = self.object_store_memory
if object_store_memory is None:
object_store_memory = int(avail_memory * 0.3)
# 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.debug(
"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)
redis_max_memory = self.redis_max_memory
if redis_max_memory is None:
redis_max_memory = min(
ray_constants.DEFAULT_REDIS_MAX_MEMORY_BYTES,
max(int(avail_memory * 0.1),
ray_constants.REDIS_MINIMUM_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))
memory = self.memory
if memory is None:
memory = (avail_memory - object_store_memory -
(redis_max_memory if is_head else 0))
if memory < 100e6 and memory < 0.05 * system_memory:
raise ValueError(
"After taking into account object store and redis memory "
"usage, the amount of memory on this node available for "
"tasks and actors ({} GB) is less than {}% of total. "
"You can adjust these settings with "
"ray.init(memory=<bytes>, "
"object_store_memory=<bytes>).".format(
round(memory / 1e9, 2),
int(100 * (memory / system_memory))))
rounded_memory = ray_constants.round_to_memory_units(memory,
round_up=False)
worker_ram = round(rounded_memory / (1024**3), 2)
object_ram = round(object_store_memory / (1024**3), 2)
# TODO(maximsmol): this behavior is strange since we do not have a
# good grasp on when this will get called
# (you have to study node.py to make a guess)
with cli_logger.group("Available RAM"):
cli_logger.labeled_value("Workers", "{} GiB", str(worker_ram))
cli_logger.labeled_value("Objects", "{} GiB", str(object_ram))
cli_logger.newline()
cli_logger.print("To adjust these values, use")
with cf.with_style("monokai") as c:
cli_logger.print(
" ray{0}init(memory{1}{2}, "
"object_store_memory{1}{2})", c.magenta("."),
c.magenta("="), c.purple("<bytes>"))
cli_logger.old_info(
logger,
"Starting Ray with {} GiB memory available for workers and up to "
"{} GiB for objects. You can adjust these settings "
"with ray.init(memory=<bytes>, "
"object_store_memory=<bytes>).", worker_ram, object_ram)
spec = ResourceSpec(num_cpus, num_gpus, memory, object_store_memory,
resources, redis_max_memory)
assert spec.resolved()
return spec
