from typing import Dict, overload, Optional
from labml.internal.tracker import tracker_singleton as _internal
from labml.internal.track_debug import tracker_debug_singleton as _tracker_debug_singleton
@overload
def debug(is_debug: bool):
...
@overload
def debug(name: str):
...
@overload
def debug(name: str, value: any):
...
def debug(*args):
if len(args) == 1:
if isinstance(args[0], bool):
_tracker_debug_singleton().is_debug = args[0]
else:
return _tracker_debug_singleton().get(args[0])
else:
_tracker_debug_singleton().store(args[0], args[1])
[docs]def set_global_step(global_step: Optional[int]):
_internal().set_global_step(global_step)
[docs]def add_global_step(increment_global_step: int = 1):
_internal().add_global_step(int(increment_global_step))
[docs]def get_global_step() -> int:
return _internal().global_step
[docs]def set_queue(name: str, queue_size: int = 10, is_print: bool = False):
from labml.internal.tracker.indicators.numeric import Queue
_internal().add_indicator(Queue(name, queue_size, is_print))
[docs]def set_histogram(name: str, is_print: bool = False):
from labml.internal.tracker.indicators.numeric import Histogram
_internal().add_indicator(Histogram(name, is_print))
[docs]def set_scalar(name: str, is_print: bool = False):
from labml.internal.tracker.indicators.numeric import Scalar
_internal().add_indicator(Scalar(name, is_print))
[docs]def set_indexed_scalar(name: str):
from labml.internal.tracker.indicators.indexed import IndexedScalar
_internal().add_indicator(IndexedScalar(name))
[docs]def set_image(name: str, is_print: bool = False, density: Optional[float] = None):
from labml.internal.tracker.indicators.artifacts import Image
_internal().add_indicator(Image(name, is_print, density))
[docs]def set_text(name: str, is_print: bool = False):
from labml.internal.tracker.indicators.artifacts import Text
_internal().add_indicator(Text(name, is_print))
def set_tensor(name: str, is_once: bool = False):
from labml.internal.tracker.indicators.artifacts import Tensor
_internal().add_indicator(Tensor(name, is_once=is_once))
[docs]def set_indexed_text(name: str, title: Optional[str] = None, is_print: bool = False):
from labml.internal.tracker.indicators.artifacts import IndexedText
_internal().add_indicator(IndexedText(name, title, is_print))
def _add_dict(values: Dict[str, any]):
for k, v in values.items():
_internal().store(k, v)
@overload
def add(values: Dict[str, any]):
...
@overload
def add(name: str, value: any):
...
@overload
def add(**kwargs: any):
...
[docs]def add(*args, **kwargs):
"""
This has multiple overloads
.. function:: add(values: Dict[str, any])
:noindex:
.. function:: add(name: str, value: any)
:noindex:
.. function:: add(**kwargs: any)
:noindex:
"""
if len(args) > 2:
raise TypeError('tracker.add should be called as add(name, value), add(dictionary) or add(k=v,k2=v2...)')
if len(args) == 0:
_add_dict(kwargs)
elif len(args) == 1:
if kwargs:
raise TypeError('tracker.add should be called as add(name, value), add(dictionary) or add(k=v,k2=v2...)')
if not isinstance(args[0], dict):
raise TypeError('tracker.add should be called as add(name, value), add(dictionary) or add(k=v,k2=v2...)')
_add_dict(args[0])
elif len(args) == 2:
if kwargs:
raise TypeError('tracker.add should be called as add(name, value), add(dictionary) or add(k=v,k2=v2...)')
if not isinstance(args[0], str):
raise TypeError('tracker.add should be called as add(name, value), add(dictionary) or add(k=v,k2=v2...)')
_internal().store(args[0], args[1])
@overload
def save():
...
@overload
def save(global_step: int):
...
@overload
def save(values: Dict[str, any]):
...
@overload
def save(name: str, value: any):
...
@overload
def save(**kwargs: any):
...
@overload
def save(global_step: int, values: Dict[str, any]):
...
@overload
def save(global_step: int, name: str, value: any):
...
@overload
def save(global_step: int, **kwargs: any):
...
[docs]def save(*args, **kwargs):
r"""
This has multiple overloads
.. function:: save()
:noindex:
.. function:: save(global_step: int)
:noindex:
.. function:: save(values: Dict[str, any])
:noindex:
.. function:: save(name: str, value: any)
:noindex:
.. function:: save(**kwargs: any)
:noindex:
.. function:: save(global_step: int, values: Dict[str, any])
:noindex:
.. function:: save(global_step: int, name: str, value: any)
:noindex:
.. function:: save(global_step: int, **kwargs: any)
:noindex:
"""
if len(args) > 0 and type(args[0]) == int:
_internal().set_global_step(args[0])
args = args[1:]
if len(args) > 0 or len(kwargs.keys()) > 0:
add(*args, **kwargs)
_internal().write()
def new_line():
r"""
Prints a new line.
Equivalent to ``logger.log``, but handles distributed training where only the rank=0
process is tracking data.
"""
_internal().new_line()
[docs]def namespace(name: str):
r"""
Set a namespace for tracking
"""
return _internal().namespace(name)
[docs]def reset():
r"""
Reset indicators, for a new experiment
"""
_internal().reset_store()