{output_dir}/code/hydra # Store hydra's config breakdown here for debugging searchpath: # Only <exp_dir> in these paths are discoverable - pkg://nuplan.planning.script.config.common - pkg://nuplan.planning.script.experiments # Put experiments configs in script/experiments/<exp_dir> ...
verbose:bool=False,**kwargs,)->Arch:# Function for instantiating a modulus architecture with hydraasserthasattr(cfg,"arch_type"),"Model configs are required to have an arch_type defined.\Improper architecture supplied, please
[docs]@dataclassclassSRResConf(ModelConf):arch_type:str="super_res"large_kernel_size:int=7small_kernel_size:int=3conv_layer_size:int=32n_resid_blocks:int=8scaling_factor:int=8activation_fn:str="prelu" [docs]defregister_arch_configs()->None:# Information regarding multiple config groups# ...
I know multiple ways to hack around this but I would rather invest time to get a cleaner solution inhydraitself. I feel the current defaults feel inconsistent. When selecting multiple configs we replace the respective line in the defaults with the content of the referenced file and then merge ...
The command above executes all experiments from configs/experiment/.Execute run for multiple different seeds python train.py -m seed=1,2,3,4,5 trainer.deterministic=True logger=csv tags=["benchmark"] Note: trainer.deterministic=True makes pytorch more deterministic but impacts the performance....
Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks. - hav4ik/Hydra
Additionally you can choose to break up your configs by creating a directory structure in the same location as your main config file, with the names of the top-level fields (such as "model", "dataset", etc), and placing config files with meaningful names that would populate that specific ...
What do we follow? One obvious thing was that we want to usePyTorch Lightning, pl for short. We wanted to train our models on both single and multiple gpus, while being able to develop and test code locally on cpu. We knew PyTorch Lightning was capable of that andmuch more. ...
"""Supported optimizer configs"""importtorchfromdataclassesimportdataclass,fieldfromhydra.core.config_storeimportConfigStorefromtypingimportList,AnyfromomegaconfimportMISSING [docs]@dataclassclassOptimizerConf:_target_=MISSING_params_:Any=field(default_factory=lambda:{"compute_gradients":"adam_compute_gradients...
catch func allows you to handle Promise's errors (with multiple promises you may also have a single errors entry point and reduce the complexity).getImage(url).then(.main, { image in myImageView.image = image }).catch(.main, { error in print("Something bad occurred: \(error)") })...