def __init__(
self,
config: Config,
dataset: Dataset,
parent_job: Job = None,
model=None,
forward_only=False,
) -> None:
from kge.job import EvaluationJob
super().__init__(config, dataset, parent_job)
if model is None:
self.model: KgeModel = KgeModel.create(config, dataset)
else:
self.model: KgeModel = model
self.loss = KgeLoss.create(config)
self.abort_on_nan: bool = config.get("train.abort_on_nan")
self.batch_size: int = config.get("train.batch_size")
self._subbatch_auto_tune: bool = config.get("train.subbatch_auto_tune")
self._max_subbatch_size: int = config.get("train.subbatch_size")
self.device: str = self.config.get("job.device")
self.train_split = config.get("train.split")
self.config.check("train.trace_level", ["batch", "epoch"])
self.trace_batch: bool = self.config.get(
"train.trace_level") == "batch"
self.epoch: int = 0
self.is_forward_only = forward_only
if not self.is_forward_only:
self.model.train()
self.optimizer = KgeOptimizer.create(config, self.model)
self.kge_lr_scheduler = KgeLRScheduler(config, self.optimizer)
self.valid_trace: List[Dict[str, Any]] = []
valid_conf = config.clone()
valid_conf.set("job.type", "eval")
if self.config.get("valid.split") != "":
valid_conf.set("eval.split", self.config.get("valid.split"))
valid_conf.set("eval.trace_level",
self.config.get("valid.trace_level"))
self.valid_job = EvaluationJob.create(valid_conf,
dataset,
parent_job=self,
model=self.model)
# attributes filled in by implementing classes
self.loader = None
self.num_examples = None
self.type_str: Optional[str] = None
# Hooks run after validation. The corresponding valid trace entry can be found
# in self.valid_trace[-1] Signature: job
self.post_valid_hooks: List[Callable[[Job], Any]] = []
if self.__class__ == TrainingJob:
for f in Job.job_created_hooks:
f(self)
def __init__(self,
config: Config,
dataset: Dataset,
parent_job: Job = None) -> None:
from kge.job import EvaluationJob
super().__init__(config, dataset, parent_job)
self.model: KgeModel = KgeModel.create(config, dataset)
self.optimizer = KgeOptimizer.create(config, self.model)
self.kge_lr_scheduler = KgeLRScheduler(config, self.optimizer)
self.loss = KgeLoss.create(config)
self.abort_on_nan: bool = config.get("train.abort_on_nan")
self.batch_size: int = config.get("train.batch_size")
self.device: str = self.config.get("job.device")
self.train_split = config.get("train.split")
valid_conf = config.clone()
valid_conf.set("job.type", "eval")
if self.config.get("valid.split") != "":
valid_conf.set("eval.split", self.config.get("valid.split"))
valid_conf.set("eval.trace_level",
self.config.get("valid.trace_level"))
self.valid_job = EvaluationJob.create(valid_conf,
dataset,
parent_job=self,
model=self.model)
self.config.check("train.trace_level", ["batch", "epoch"])
self.trace_batch: bool = self.config.get(
"train.trace_level") == "batch"
self.epoch: int = 0
self.valid_trace: List[Dict[str, Any]] = []
self.is_prepared = False
self.model.train()
# attributes filled in by implementing classes
self.loader = None
self.num_examples = None
self.type_str: Optional[str] = None
#: Hooks run after training for an epoch.
#: Signature: job, trace_entry
self.post_epoch_hooks: List[Callable[[Job, Dict[str, Any]], Any]] = []
#: Hooks run before starting a batch.
#: Signature: job
self.pre_batch_hooks: List[Callable[[Job], Any]] = []
#: Hooks run before outputting the trace of a batch. Can modify trace entry.
#: Signature: job, trace_entry
self.post_batch_trace_hooks: List[Callable[[Job, Dict[str, Any]],
Any]] = []
#: Hooks run before outputting the trace of an epoch. Can modify trace entry.
#: Signature: job, trace_entry
self.post_epoch_trace_hooks: List[Callable[[Job, Dict[str, Any]],
Any]] = []
#: Hooks run after a validation job.
#: Signature: job, trace_entry
self.post_valid_hooks: List[Callable[[Job, Dict[str, Any]], Any]] = []
#: Hooks run after training
#: Signature: job, trace_entry
self.post_train_hooks: List[Callable[[Job, Dict[str, Any]], Any]] = []
if self.__class__ == TrainingJob:
for f in Job.job_created_hooks:
f(self)