Package: ezdl
EzDLInstallationRecommended to create a Python virtual environmentpipinstallezdlUsageezdl<ACTION>mandatory argumentsaction:	Choose the action to do perform: 
		experiment, resume_run, complete, manipulate, appoptional arguments:-h, --help            show this help message and exit
--resume              Resume the experiment
-d DIR, --dir DIR     Set the local tracking directory
-f FILE, --file FILE  Set the config file
--grid GRID           Select the first grid to start from
--run RUN             Select the run in grid to start from
--filters FILTERS     Filters to query in the resuming mode
-s STAGE, --stage STAGE
                      Stages to execute in the resuming mode
-p PATH, --path PATH  Path to the tracking url in the resuming mode
--subset SUBSET       Subset chosen for preprocessingParameter fileYAML file that contains all parameters necessary to the exepriment to be run.It must have 3 top keys:experimentparametersother_gridsLet's see the CIFAR10 example:experiment:# It contains all the about the grids and the group of runs:name:Classification# name of the logger platform experimentgroup:FirstGroup# name of group of experiments for the logger platformcontinue_with_errors:False# continue with other runs even if a run failsstart_from_grid:0# skip grids in the grid searchstart_from_run:0# skip runs from the selected gridlogger:clearml# logger platform to usetracking_dir:'./examplesExp'# dir where results will be savedentity:null# Wandb entity (username)excluded_files:null# glob of files to not upload to Wandbparameters:# Contains the parameters to build the grid.# Each value should be a dict or a listtags:[[mytag1,mytag2]]# list of tags to attach to the run in logger platformphases:[[train,test]]# list of phasesdataset_interface:[examples/cifar10/Cifar10]# Path to the dataset interface classtrain_params:loss:name:[cross_entropy]# class loss nameparams:seed:[42]# random seed to setmax_epochs:[1,2]initial_lr:[0.0001]optimizer:[Adam]zero_weight_decay_on_bias_and_bn:[True]average_best_models:[False]greater_metric_to_watch_is_better:[False]metric_to_watch:[loss]freeze_pretrained:[False]# freeze the loaded pretrained weights# Other parameters relative to Super-Gradients (see their docs)early_stopping:patience:[10]# number of epochs before stoppingmonitor:[loss]# metric to monitormode:[min]# metric to be minimized or maximizedtrain_metrics:# list of metrics to load from PyTorch metrics# where the values are their parameters used for trainingf1:average:[macro]num_classes:[10]mdmc_average:[global]test_metrics:# list of metrics to load from PyTorch metrics# where the values are their parameters used for validation and testf1:num_classes:[10]average:[macro]mdmc_average:[global]precision:average:[macro]num_classes:[10]mdmc_average:[global]recall:average:[macro]num_classes:[10]mdmc_average:[global]model:name:[resnet18]# path to model class or model name contained in EzDL or super-gradientsparams:# model parameterspretrained_weights:[imagenet]num_classes:[10]dataset:# parameters depending on the class you defined for the datasetchannels:[["R","G","B"]]num_classes:[10]trainset:testset:trainloader:batch_size:[8]num_workers:[0]testloader:batch_size:[8]num_workers:[0]other_grids:# List of supplementary grids (can be empty) in which the parameters defined will override the first grid.