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_settings.py
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_settings.py
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import logging
import os
import warnings
from pathlib import Path
from typing import Literal, Optional, Union
import torch
from lightning.pytorch import seed_everything
from rich.console import Console
from rich.logging import RichHandler
scvi_logger = logging.getLogger("scvi")
class ScviConfig:
"""Config manager for scvi-tools.
Examples
--------
To set the seed
>>> scvi.settings.seed = 1
To set the batch size for functions like `SCVI.get_latent_representation`
>>> scvi.settings.batch_size = 1024
To set the progress bar style, choose one of "rich", "tqdm"
>>> scvi.settings.progress_bar_style = "rich"
To set the verbosity
>>> import logging
>>> scvi.settings.verbosity = logging.INFO
To set pin memory for GPU training
>>> scvi.settings.dl_pin_memory_gpu_training = True
To set the number of threads PyTorch will use
>>> scvi.settings.num_threads = 2
To prevent Jax from preallocating GPU memory on start (default)
>>> scvi.settings.jax_preallocate_gpu_memory = False
"""
def __init__(
self,
verbosity: int = logging.INFO,
progress_bar_style: Literal["rich", "tqdm"] = "tqdm",
batch_size: int = 128,
seed: Optional[int] = None,
logging_dir: str = "./scvi_log/",
dl_num_workers: int = 0,
dl_pin_memory_gpu_training: bool = False, # TODO: remove in v1.1
jax_preallocate_gpu_memory: bool = False,
warnings_stacklevel: int = 2,
):
self.warnings_stacklevel = warnings_stacklevel
self.seed = seed
self.batch_size = batch_size
if progress_bar_style not in ["rich", "tqdm"]:
raise ValueError("Progress bar style must be in ['rich', 'tqdm']")
self.progress_bar_style = progress_bar_style
self.logging_dir = logging_dir
self.dl_num_workers = dl_num_workers
self.dl_pin_memory_gpu_training = (
dl_pin_memory_gpu_training # TODO: remove in 1.1
)
self._num_threads = None
self.jax_preallocate_gpu_memory = jax_preallocate_gpu_memory
self.verbosity = verbosity
@property
def batch_size(self) -> int:
"""Minibatch size for loading data into the model.
This is only used after a model is trained. Trainers have specific
`batch_size` parameters.
"""
return self._batch_size
@batch_size.setter
def batch_size(self, batch_size: int):
"""Minibatch size for loading data into the model.
This is only used after a model is trained. Trainers have specific
`batch_size` parameters.
"""
self._batch_size = batch_size
@property
def dl_num_workers(self) -> int:
"""Number of workers for PyTorch data loaders (Default is 0)."""
return self._dl_num_workers
@dl_num_workers.setter
def dl_num_workers(self, dl_num_workers: int):
"""Number of workers for PyTorch data loaders (Default is 0)."""
self._dl_num_workers = dl_num_workers
@property
def dl_pin_memory_gpu_training(self) -> int:
"""Set `pin_memory` in data loaders when using a GPU for training."""
return self._dl_pin_memory_gpu_training
@dl_pin_memory_gpu_training.setter
def dl_pin_memory_gpu_training(self, dl_pin_memory_gpu_training: int):
"""Set `pin_memory` in data loaders when using a GPU for training."""
warnings.warn(
"Setting `dl_pin_memory_gpu_training` is deprecated in v1.0 and will be "
"removed in v1.1. Please pass in `pin_memory` to the data loaders instead.",
UserWarning,
stacklevel=self.warnings_stacklevel,
)
self._dl_pin_memory_gpu_training = dl_pin_memory_gpu_training
@property
def logging_dir(self) -> Path:
"""Directory for training logs (default `'./scvi_log/'`)."""
return self._logging_dir
@logging_dir.setter
def logging_dir(self, logging_dir: Union[str, Path]):
self._logging_dir = Path(logging_dir).resolve()
@property
def num_threads(self) -> None:
"""Number of threads PyTorch will use."""
return self._num_threads
@num_threads.setter
def num_threads(self, num: int):
"""Number of threads PyTorch will use."""
self._num_threads = num
torch.set_num_threads(num)
@property
def progress_bar_style(self) -> str:
"""Library to use for progress bar."""
return self._pbar_style
@progress_bar_style.setter
def progress_bar_style(self, pbar_style: Literal["tqdm", "rich"]):
"""Library to use for progress bar."""
self._pbar_style = pbar_style
@property
def seed(self) -> int:
"""Random seed for torch and numpy."""
return self._seed
@seed.setter
def seed(self, seed: Union[int, None] = None):
"""Random seed for torch and numpy."""
if seed is None:
self._seed = None
warnings.warn(
"Since v1.0.0, scvi-tools no longer uses a random seed by default. Run "
"`scvi.settings.seed = 0` to reproduce results from previous versions.",
UserWarning,
stacklevel=self.warnings_stacklevel,
)
else:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
seed_everything(seed)
self._seed = seed
@property
def verbosity(self) -> int:
"""Verbosity level (default `logging.INFO`)."""
return self._verbosity
@verbosity.setter
def verbosity(self, level: Union[str, int]):
"""Sets logging configuration for scvi based on chosen level of verbosity.
If "scvi" logger has no StreamHandler, add one.
Else, set its level to `level`.
Parameters
----------
level
Sets "scvi" logging level to `level`
force_terminal
Rich logging option, set to False if piping to file output.
"""
self._verbosity = level
scvi_logger.setLevel(level)
if len(scvi_logger.handlers) == 0:
console = Console(force_terminal=True)
if console.is_jupyter is True:
console.is_jupyter = False
ch = RichHandler(
level=level, show_path=False, console=console, show_time=False
)
formatter = logging.Formatter("%(message)s")
ch.setFormatter(formatter)
scvi_logger.addHandler(ch)
else:
scvi_logger.setLevel(level)
@property
def warnings_stacklevel(self) -> int:
"""Stacklevel for warnings."""
return self._warnings_stacklevel
@warnings_stacklevel.setter
def warnings_stacklevel(self, stacklevel: int):
"""Stacklevel for warnings."""
self._warnings_stacklevel = stacklevel
def reset_logging_handler(self):
"""Resets "scvi" log handler to a basic RichHandler().
This is useful if piping outputs to a file.
"""
scvi_logger.removeHandler(scvi_logger.handlers[0])
ch = RichHandler(level=self._verbosity, show_path=False, show_time=False)
formatter = logging.Formatter("%(message)s")
ch.setFormatter(formatter)
scvi_logger.addHandler(ch)
@property
def jax_preallocate_gpu_memory(self):
"""Jax GPU memory allocation settings.
If False, Jax will ony preallocate GPU memory it needs.
If float in (0, 1), Jax will preallocate GPU memory to that
fraction of the GPU memory.
"""
return self._jax_gpu
@jax_preallocate_gpu_memory.setter
def jax_preallocate_gpu_memory(self, value: Union[float, bool]):
# see https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html#gpu-memory-allocation
if value is False:
os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
elif isinstance(value, float):
if value >= 1 or value <= 0:
raise ValueError("Need to use a value between 0 and 1")
# format is ".XX"
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = str(value)[1:4]
else:
raise ValueError("value not understood, need bool or float in (0, 1)")
self._jax_gpu = value
settings = ScviConfig()