- medl.apps.automl package
- Submodules
- medl.apps.automl.base_mmar_exec module
- medl.apps.automl.constants module
- medl.apps.automl.dummy_controller module
- medl.apps.automl.dummy_handler module
- medl.apps.automl.fake_clara_train module
- medl.apps.automl.mmar_exec module
- medl.apps.automl.mmar_handler module
- medl.apps.automl.mmar_stats_handler module
- medl.apps.automl.mmar_trainer module
- medl.apps.automl.ngc_exec module
- medl.apps.automl.ssu module
- medl.apps.automl.test_rec module
- medl.apps.automl.test_ssu module
- medl.apps.automl.test_train module
- medl.apps.automl.train module
- Module contents
- medl.apps.fed_learn package
- class EngineSpec
Bases:
object- abort()
Call to terminate the current running train / validate job. Returns:
- close()
Call to terminate and close the engine. Returns:
- evaluate()
Call to evaluate the current model. Returns:
- train()
Call the engine to train model. Returns:
- validate()
Call to validate the current model. Returns:
- class EvalConfiger(mmar_root: str, wf_config_file_name=None, env_config_file_name=None, log_config_file_name=None, kv_list=None, debug_pre_transform=False, logging_config=True)
Bases:
dlmed.utils.clara_conf.ClaraConfiger- close()
- finalize_config(config_ctx: dlmed.utils.wfconf.ConfigContext)
- process_args(args: dict)
- process_config_element(config_ctx: dlmed.utils.wfconf.ConfigContext, node: dlmed.utils.json_scanner.Node)
- start_config(config_ctx: dlmed.utils.wfconf.ConfigContext)
- main()
- class StoreShape(option_strings, dest, nargs=None, const=None, default=None, type=None, choices=None, required=False, help=None, metavar=None)
Bases:
argparse.Action
- main()
- to_numpy(tensor)
- class ExportConfiger(args)
Bases:
dlmed.utils.clara_conf.ClaraConfiger- finalize_config(config_ctx: dlmed.utils.wfconf.ConfigContext)
- process_config_element(config_ctx: dlmed.utils.wfconf.ConfigContext, node: dlmed.utils.json_scanner.Node)
- class MMAREvaluator(mmar_root: str, wf_config_file_name=None, env_config_file_name=None, log_config_file_name=None, kv_list=None, debug_pre_transform=False, logging_config=True)
Bases:
medl.apps.engine_spec.EngineSpec- close()
Call to terminate and close the engine. Returns:
- configure()
- evaluate() → Dict
Call to evaluate the current model. Returns:
- class MMARTrainer(mmar_root: str, wf_config_file_name=None, env_config_file_name=None, log_config_file_name=None, kv_list=None, debug_pre_transform=False, logging_config=True)
Bases:
medl.apps.engine_spec.EngineSpec- abort()
Call to terminate the current running train / validate job. Returns:
- close()
Call to terminate and close the engine. Returns:
- configure()
- full_local_train() → Dict
- train()
Call the engine to train model. Returns:
- validate()
Call to validate the current model. Returns:
- evaluate_mmar(args)
- train_mmar(args)
- main()
- class TrainConfiger(mmar_root: str, wf_config_file_name=None, env_config_file_name=None, log_config_file_name=None, kv_list=None, debug_pre_transform=False, base_pkgs=['medl', 'monai', 'ignite.metrics', 'torch.optim', 'torch.nn'], module_names=['.'], logging_config=True)
Bases:
dlmed.utils.clara_conf.ClaraConfiger- close()
- create_element_from_ref(refs, element)
- finalize_config(config_ctx: dlmed.utils.wfconf.ConfigContext)
- process_args(args: dict)
- process_config_element(config_ctx: dlmed.utils.wfconf.ConfigContext, node: dlmed.utils.json_scanner.Node)
- process_first_pass(node: dlmed.utils.json_scanner.Node)
- process_second_pass(node: dlmed.utils.json_scanner.Node)
- start_config(config_ctx: dlmed.utils.wfconf.ConfigContext)
- class ComposePrepareBatch(prepare_batch: Sequence)
Bases:
objectUtility class to compose a list of prepare_batch components as a callable object.
- class NetworkSummary(network, kwargs: Optional[dict] = None)
Bases:
objectPrints network summary.
Use Torchinfo: https://github.com/TylerYep/torchinfo
network: PyTorch network module. kwargs: arguments to be passed into TorchInfo summary
- class TrainJSONConfig(config: Optional[Dict] = None)
Bases:
objectRecord train config of the JSON file as state dict object, then we can save it in the checkpoint.
- load_state_dict(state_dict: Dict) → None
- state_dict()
- add_custom_pythonpath(mmar_root: str, pathname: str = 'custom')
Add the path of BYOC custom folder to PYTHONPATH.
- sample_weights_by_classes(items_list, label_key: str = 'label')
Calculates sample weights based on item count per class.
- set_determinism_benchmark(seed: Optional[int] = None, benchmark: Optional[bool] = None, use_deterministic_algorithms: Optional[int] = None)
Utility to set determinism parameters and torch.backends.cudnn.benchmark. If seed is not None, will enable determinism, otherwise, will disable determinism. Note that benchmark=True can’t work together with determinism. If benchmark is None, will not set value to torch.backends.cudnn.benchmark.
- set_tensorboard_writer(log_dir, existing_writers)
Search existing writers with same log directory, if can’t find, create a new writer.
- set_tf32(tf32: bool)
Utility to enable/disable TF32 on Ampere GPUs, it’s supported from PyTorch 1.7. It will set value to both torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32. For more details, please check: https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices