def _get_kubernetes_node_by_ip(self, node_ip: str) -> Optional[str]:
"""Return node name by internal or external IP"""
kubernetes.config.load_incluster_config()
api = kubernetes.client.CoreV1Api()
pods = api.list_namespaced_pod(self._namespace)
for pod in pods.items:
if pod.status.host_ip == node_ip or pod.status.pod_ip == node_ip:
return pod.metadata.name
logger.error(
"Could not find Kubernetes pod name for IP {}".format(node_ip))
return None
import os
from multiprocessing import Process, Queue
from numbers import Number
from ray import logger
from ray.tune import Trainable
from ray.tune.function_runner import FunctionRunner
from ray.tune.logger import Logger
from ray.tune.utils import flatten_dict
try:
import wandb
except ImportError:
logger.error("pip install 'wandb' to use WandbLogger/WandbTrainableMixin.")
wandb = None
WANDB_ENV_VAR = "WANDB_API_KEY"
_WANDB_QUEUE_END = (None, )
def wandb_mixin(func):
"""wandb_mixin
Weights and biases (https://www.wandb.com/) is a tool for experiment
tracking, model optimization, and dataset versioning. This Ray Tune
Trainable mixin helps initializing the Wandb API for use with the
``Trainable`` class or with `@wandb_mixin` for the function API.
For basic usage, just prepend your training function with the
``@wandb_mixin`` decorator:
def _missing_(cls, value):
logger.error(f'Missing weapon type {value}')
return cls.MISSING
def __post_init__(self):
self.weapon = Weapons(self.gun_type).name
if self.weapon == Weapons.MISSING.value:
logger.error(self)
