diff --git a/pytorch_lightning/utilities/arg_parse.py b/pytorch_lightning/utilities/arg_parse.py deleted file mode 100644 index fa2b46fe..00000000 --- a/pytorch_lightning/utilities/arg_parse.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -List of default args which mught be useful for all the available flags -Might need to update with the new flags -""" - -import os - - -def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None): - # training, test, val check intervals - parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true', - help='true = run test set also') - parser.add_argument('--check_val_every_n_epoch', default=1, type=int, - help='check val every n epochs') - parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False, - help='accumulates gradients k times before applying update.' - ' Simulates huge batch size') - parser.add_argument('--max_epochs', default=200, type=int, - help='maximum number of epochs') - parser.add_argument('--min_epochs', default=2, type=int, - help='minimum number of epochs') - parser.add_argument('--train_percent_check', default=1.0, type=float, - help='how much of training set to check') - parser.add_argument('--val_percent_check', default=1.0, type=float, - help='how much of val set to check') - parser.add_argument('--test_percent_check', default=1.0, type=float, - help='how much of test set to check') - - parser.add_argument('--val_check_interval', default=0.95, type=float, - help='how much within 1 epoch to check val') - parser.add_argument('--log_save_interval', default=100, type=int, - help='how many batches between log saves') - parser.add_argument('--row_log_interval', default=100, type=int, - help='add log every k batches') - - # early stopping - parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false') - parser.add_argument('--early_stop_metric', default='val_acc', type=str) - parser.add_argument('--early_stop_mode', default='min', type=str) - parser.add_argument('--early_stop_patience', default=3, type=int, - help='number of epochs until stop') - - # gradient handling - parser.add_argument('--gradient_clip_val', default=-1, type=int) - parser.add_argument('--track_grad_norm', default=-1, type=int, - help='if > 0, will track this grad norm') - - # model saving - parser.add_argument('--model_save_path', default=root_dir + '/model_weights') - parser.add_argument('--model_save_monitor_value', default='val_acc') - parser.add_argument('--model_save_monitor_mode', default='max') - - # model paths - parser.add_argument('--model_load_weights_path', default=None, type=str) - - if possible_model_names is not None: - parser.add_argument('--model_name', default='', help=','.join(possible_model_names)) - - # test_tube settings - parser.add_argument('-en', '--tt_name', default='pt_test') - parser.add_argument('-td', '--tt_description', default='pytorch lightning test') - parser.add_argument('--tt_save_path', default=os.path.join(root_dir, 'test_tube_logs'), - help='logging dir') - parser.add_argument('--enable_single_run', dest='single_run', action='store_true') - parser.add_argument('--nb_hopt_trials', default=1, type=int) - parser.add_argument('--log_stdout', dest='log_stdout', action='store_true') - - # GPU - parser.add_argument('--gpus', default=None, type=str) - parser.add_argument('--single_run_gpu', dest='single_run_gpu', action='store_true') - parser.add_argument('--default_tensor_type', default='torch.cuda.FloatTensor', type=str) - parser.add_argument('--use_amp', dest='use_amp', action='store_true') - parser.add_argument('--check_grad_nans', dest='check_grad_nans', action='store_true') - parser.add_argument('--amp_level', default='O2', type=str) - - # run on hpc - parser.add_argument('--on_cluster', dest='on_cluster', action='store_true') - - # FAST training - # use these settings to make sure network has no bugs without running a full dataset - parser.add_argument('--fast_dev_run', dest='fast_dev_run', default=False, action='store_true', - help='runs validation after 1 training step') - parser.add_argument('--enable_tqdm', dest='enable_tqdm', default=False, action='store_true', - help='false removes the progress bar') - parser.add_argument('--overfit', default=-1, type=float, - help='%% of dataset to use with this option. float, or -1 for none') - - # debug args - if rand_seed is not None: - parser.add_argument('--random_seed', default=rand_seed, type=int) - - parser.add_argument('--interactive', dest='interactive', action='store_true', - help='runs on gpu without cluster') - parser.add_argument('--debug', dest='debug', action='store_true', - help='enables/disables test tube') - parser.add_argument('--local', dest='local', action='store_true', - help='enables local training') - - # optimizer - parser.add_argument('--lr_scheduler_milestones', default=None, type=str) diff --git a/pytorch_lightning/utilities/debugging.py b/pytorch_lightning/utilities/debugging.py index e03feec4..b7f92bca 100644 --- a/pytorch_lightning/utilities/debugging.py +++ b/pytorch_lightning/utilities/debugging.py @@ -1,78 +1,2 @@ -""" -These flags are useful to help debug a model. - -Fast dev run ------------- - -This flag is meant for debugging a full train/val/test loop. - It'll activate callbacks, everything but only with 1 training and 1 validation batch. - Use this to debug a full run of your program quickly - -.. code-block:: python - - # DEFAULT - trainer = Trainer(fast_dev_run=False) - - -Inspect gradient norms ----------------------- - -Looking at grad norms can help you figure out where training might be going wrong. - -.. code-block:: python - - # DEFAULT (-1 doesn't track norms) - trainer = Trainer(track_grad_norm=-1) - - # track the LP norm (P=2 here) - trainer = Trainer(track_grad_norm=2) - - -Make model overfit on subset of data ------------------------------------- - -A useful debugging trick is to make your model overfit a tiny fraction of the data. - -setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check - -.. code-block:: python - - # DEFAULT don't overfit (ie: normal training) - trainer = Trainer(overfit_pct=0.0) - - # overfit on 1% of data - trainer = Trainer(overfit_pct=0.01) - - -Print the parameter count by layer ----------------------------------- - -By default lightning prints a list of parameters *and submodules* when it starts training. - -.. code-block:: python - - # DEFAULT print a full list of all submodules and their parameters. - trainer = Trainer(weights_summary='full') - - # only print the top-level modules (i.e. the children of LightningModule). - trainer = Trainer(weights_summary='top') - -Print which gradients are nan ------------------------------ - -This option prints a list of tensors with nan gradients:: - - # DEFAULT - trainer = Trainer(print_nan_grads=False) - -Log GPU usage -------------- - -Lightning automatically logs gpu usage to the test tube logs. - It'll only do it at the metric logging interval, so it doesn't slow down training. - -""" - - class MisconfigurationException(Exception): pass