scvi._settings.ScviConfig#

class scvi._settings.ScviConfig(verbosity=20, progress_bar_style='tqdm', batch_size=128, seed=0, logging_dir='./scvi_log/', dl_num_workers=0, dl_pin_memory_gpu_training=False, jax_preallocate_gpu_memory=False)[source]#

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

Attributes table#

batch_size

Minibatch size for loading data into the model.

dl_num_workers

Number of workers for PyTorch data loaders (Default is 0).

dl_pin_memory_gpu_training

Set pin_memory in data loaders when using a GPU for training.

jax_preallocate_gpu_memory

Jax GPU memory allocation settings.

logging_dir

Directory for training logs (default './scvi_log/').

num_threads

Number of threads PyTorch will use.

progress_bar_style

Library to use for progress bar.

seed

Random seed for torch and numpy.

verbosity

Verbosity level (default logging.INFO).

Methods table#

reset_logging_handler()

Resets "scvi" log handler to a basic RichHandler().

Attributes#

batch_size

ScviConfig.batch_size[source]#

Minibatch size for loading data into the model.

This is only used after a model is trained. Trainers have specific batch_size parameters.

dl_num_workers

ScviConfig.dl_num_workers[source]#

Number of workers for PyTorch data loaders (Default is 0).

dl_pin_memory_gpu_training

ScviConfig.dl_pin_memory_gpu_training[source]#

Set pin_memory in data loaders when using a GPU for training.

jax_preallocate_gpu_memory

ScviConfig.jax_preallocate_gpu_memory[source]#

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.

logging_dir

ScviConfig.logging_dir[source]#

Directory for training logs (default ‘./scvi_log/’).

num_threads

ScviConfig.num_threads[source]#

Number of threads PyTorch will use.

progress_bar_style

ScviConfig.progress_bar_style[source]#

Library to use for progress bar.

seed

ScviConfig.seed[source]#

Random seed for torch and numpy.

verbosity

ScviConfig.verbosity[source]#

Verbosity level (default logging.INFO).

Methods#

reset_logging_handler

ScviConfig.reset_logging_handler()[source]#

Resets “scvi” log handler to a basic RichHandler().

This is useful if piping outputs to a file.