:py:mod:`wandb_utils.misc`
==========================

.. py:module:: wandb_utils.misc


Module Contents
---------------

.. py:data:: logger
   

   

.. py:function:: to_csv(df: pandas.DataFrame) -> str


.. py:function:: write_df(df: pandas.DataFrame, output_file: Optional[pathlib.Path], skip_writing: bool) -> None


.. py:function:: read_df(path: pathlib.Path, sep: str = '\t') -> pandas.DataFrame


.. py:function:: all_data_df(entity: str, project: str, sweep: Optional[str] = None, api: Optional[wandb.apis.public.Api] = None, filters: Optional[Dict] = None) -> pandas.DataFrame

   Get the data for all the runs.


.. py:function:: find_best_models_in_sweeps(entity: str, project: str, metric: str, maximum: bool = True, sweep: Optional[str] = None, api: Optional[wandb.apis.public.Api] = None) -> pandas.DataFrame


.. py:function:: get_config_file_for_run(entity: str, project: str, run_id: str, relative_path: str = 'training_dumps/config.json', output_path: str = 'config.json', api: Optional[wandb.apis.public.Api] = None) -> wandb.apis.public.Api


.. py:function:: get_config_file_for_best_run(entity: str, project: str, sweep_id: str, metric: str, maximum: bool = True, relative_path: str = 'training_dumps/config.json', output_path: str = 'config.json', api: Optional[wandb.apis.public.Api] = None) -> str


.. py:data:: multiple_runs_sweep
   

   

.. py:data:: jsonnet_with_seed_template
   

   

.. py:function:: with_fallback(preferred: Dict[str, Any], fallback: Dict[str, Any]) -> Dict[str, Any]

   Deep merge two dicts, preferring values from `preferred`.
   Ref: allennlp/common/params.py


.. py:function:: create_multiple_run_sweep_for_run(entity: str, project: str, run: Optional[str] = None, sweep: Optional[str] = None, metric: Optional[str] = None, maximum: bool = True, relative_path: str = 'training_dumps/config.json', output_path: str = 'config.json', seed_parameters: Optional[List[str]] = None, api: Optional[wandb.apis.public.Api] = None, delete_keys: Optional[List] = None, **sweep_args)

   Example::

       create_multiple_run_sweep_for_run('iesl-boxes','multilabel-learning-datasets',
                                 run='nyre9qr5',
                                 sweep_args=dict(include_packages=['multilabel_learning'],
                                                 sweep_name='test_sweep',
                                                 wandb_tags=['test', 'dryrun'],
                                                 fixed_overrides='{"type": "train_test_log_to_wandb"}'
                                                )
                                )