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import json
import sys
from typing import List
_setup_paths: List[str] = []
try:
import radical.pilot as rp
import radical.utils as ru
except ImportError:
pass
MPI = "mpi"
RP_ENV = "rp"
CLIENT = "client"
RPEX_ENV = "ve_rpex"
MPI_WORKER = "MPIWorker"
DEFAULT_WORKER = "DefaultWorker"
class ResourceConfig:
"""
This ResourceConfig class is an abstraction of the resource
configuration of the RAPTOR layer in the RADICAL-Pilot runtime system.
This class sets up the default configuration values for the executor and
allows the user to specify different resource requirements flexibly.
For more information:
https://radicalpilot.readthedocs.io/en/stable/tutorials/raptor.html
Parameters
----------
masters : int
The number of masters to be deployed by RAPTOR.
Default is 1.
workers : int
The number of workers to be deployed by RAPTOR.
Default is 1.
worker_gpus_per_node : int
The number of GPUs a worker will operate on per node.
Default is 0.
worker_cores_per_node : int
The number of CPU cores a worker will operate on per node.
Default is 4.
cores_per_master : int
The number of cores a master will operate on per node.
Default is 1.
nodes_per_worker : int
The number of nodes to be occupied by every worker.
Default is 1.
pilot_env_path : str
The path to an exisitng pilot environment.
Default is an empty string (RADICAL-Pilot will create one).
pilot_env_name : str
The name of the pilot environment.
Default is "ve_rpex".
pilot_env_pre_exec : list
List of commands to be executed before starting the pilot environment.
Default is an empty list.
pilot_env_type : str
The type of the pilot environment (e.g., 'venv', 'conda').
Default is "venv".
pilot_env_setup : list
List of setup commands/packages for the pilot environment.
Default is an empty list.
python_v : str
The Python version to be used in the pilot environment.
Default is determined by the system's Python version.
worker_type : str
The type of worker(s) to be deployed by RAPTOR on the compute
resources.
Default is "DefaultWorker".
"""
masters: int = 1
workers: int = 1
worker_gpus_per_node: int = 0
worker_cores_per_node: int = 4
cores_per_master: int = 1
nodes_per_worker: int = 1
pilot_env_mode: str = CLIENT
pilot_env_path: str = ""
pilot_env_type: str = "venv"
pilot_env_name: str = RP_ENV
pilot_env_pre_exec: List[str] = []
pilot_env_setup: List[str] = _setup_paths
python_v: str = f'{sys.version_info[0]}.{sys.version_info[1]}'
worker_type: str = DEFAULT_WORKER
def get_config(cls, path=None):
# Default ENV mode for RP is to reuse
# the client side. If this is not the case,
# then RP will create a new env named ve_rpex
# The user need to make sure that under:
# $HOME/.radical/pilot/configs/*_resource.json
# that virtenv_mode = local
if cls.pilot_env_mode != CLIENT:
cls.pilot_env_name = RPEX_ENV
if MPI in cls.worker_type.lower() and \
"mpi4py" not in cls.pilot_env_setup:
cls.pilot_env_setup.append("mpi4py")
cfg = {
'n_masters': cls.masters,
'n_workers': cls.workers,
'worker_type': cls.worker_type,
'gpus_per_node': cls.worker_gpus_per_node,
'cores_per_node': cls.worker_cores_per_node,
'cores_per_master': cls.cores_per_master,
'nodes_per_worker': cls.nodes_per_worker,
'pilot_env': {
"version": cls.python_v,
"name": cls.pilot_env_name,
"path": cls.pilot_env_path,
"type": cls.pilot_env_type,
"setup": cls.pilot_env_setup,
"pre_exec": cls.pilot_env_pre_exec
},
'pilot_env_mode': cls.pilot_env_mode,
'master_descr': {
"ranks": 1,
"cores_per_rank": 1,
"mode": rp.RAPTOR_MASTER,
"named_env": cls.pilot_env_name,
},
'worker_descr': {
"mode": rp.RAPTOR_WORKER,
"named_env": cls.pilot_env_name,
"raptor_file": "./rpex_worker.py",
"raptor_class": cls.worker_type if
cls.worker_type.lower() != MPI else MPI_WORKER,
"ranks": cls.nodes_per_worker * cls.worker_cores_per_node,
"gpus_per_rank": cls.nodes_per_worker * cls.worker_gpus_per_node,
}}
# Convert the class instance to a Json file or a Config dict.
if path:
config_path = 'rpex.cfg'
config_path = path + '/' + config_path
with open(config_path, 'w') as f:
json.dump(cfg, f, indent=4)
else:
config_obj = ru.Config(from_dict=cfg)
return config_obj
|
