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https://github.com/wassname/pytorch-lightning.git
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coverage increase (#1167)
* fixed docs * Docs (#1164) * fixed docs * fixed docs * fixed docs * fixing Win failed import (#1163) * version * try fix distrib * update try import * fixed docs Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
This commit is contained in:
William Falcon
co-authored by
Jirka Borovec
Jirka Borovec
parent
e461ec0037
commit
c32e3f3ea5
@@ -1,100 +0,0 @@
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"""
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List of default args which mught be useful for all the available flags
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Might need to update with the new flags
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"""
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import os
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def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):
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# training, test, val check intervals
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parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',
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help='true = run test set also')
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parser.add_argument('--check_val_every_n_epoch', default=1, type=int,
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help='check val every n epochs')
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parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,
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help='accumulates gradients k times before applying update.'
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' Simulates huge batch size')
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parser.add_argument('--max_epochs', default=200, type=int,
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help='maximum number of epochs')
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parser.add_argument('--min_epochs', default=2, type=int,
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help='minimum number of epochs')
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parser.add_argument('--train_percent_check', default=1.0, type=float,
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help='how much of training set to check')
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parser.add_argument('--val_percent_check', default=1.0, type=float,
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help='how much of val set to check')
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parser.add_argument('--test_percent_check', default=1.0, type=float,
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help='how much of test set to check')
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parser.add_argument('--val_check_interval', default=0.95, type=float,
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help='how much within 1 epoch to check val')
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parser.add_argument('--log_save_interval', default=100, type=int,
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help='how many batches between log saves')
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parser.add_argument('--row_log_interval', default=100, type=int,
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help='add log every k batches')
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# early stopping
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parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')
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parser.add_argument('--early_stop_metric', default='val_acc', type=str)
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parser.add_argument('--early_stop_mode', default='min', type=str)
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parser.add_argument('--early_stop_patience', default=3, type=int,
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help='number of epochs until stop')
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# gradient handling
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parser.add_argument('--gradient_clip_val', default=-1, type=int)
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parser.add_argument('--track_grad_norm', default=-1, type=int,
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help='if > 0, will track this grad norm')
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# model saving
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parser.add_argument('--model_save_path', default=root_dir + '/model_weights')
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parser.add_argument('--model_save_monitor_value', default='val_acc')
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parser.add_argument('--model_save_monitor_mode', default='max')
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# model paths
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parser.add_argument('--model_load_weights_path', default=None, type=str)
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if possible_model_names is not None:
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parser.add_argument('--model_name', default='', help=','.join(possible_model_names))
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# test_tube settings
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parser.add_argument('-en', '--tt_name', default='pt_test')
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parser.add_argument('-td', '--tt_description', default='pytorch lightning test')
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parser.add_argument('--tt_save_path', default=os.path.join(root_dir, 'test_tube_logs'),
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help='logging dir')
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parser.add_argument('--enable_single_run', dest='single_run', action='store_true')
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parser.add_argument('--nb_hopt_trials', default=1, type=int)
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parser.add_argument('--log_stdout', dest='log_stdout', action='store_true')
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# GPU
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parser.add_argument('--gpus', default=None, type=str)
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parser.add_argument('--single_run_gpu', dest='single_run_gpu', action='store_true')
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parser.add_argument('--default_tensor_type', default='torch.cuda.FloatTensor', type=str)
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parser.add_argument('--use_amp', dest='use_amp', action='store_true')
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parser.add_argument('--check_grad_nans', dest='check_grad_nans', action='store_true')
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parser.add_argument('--amp_level', default='O2', type=str)
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# run on hpc
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parser.add_argument('--on_cluster', dest='on_cluster', action='store_true')
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# FAST training
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# use these settings to make sure network has no bugs without running a full dataset
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parser.add_argument('--fast_dev_run', dest='fast_dev_run', default=False, action='store_true',
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help='runs validation after 1 training step')
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parser.add_argument('--enable_tqdm', dest='enable_tqdm', default=False, action='store_true',
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help='false removes the progress bar')
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parser.add_argument('--overfit', default=-1, type=float,
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help='%% of dataset to use with this option. float, or -1 for none')
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# debug args
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if rand_seed is not None:
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parser.add_argument('--random_seed', default=rand_seed, type=int)
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parser.add_argument('--interactive', dest='interactive', action='store_true',
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help='runs on gpu without cluster')
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parser.add_argument('--debug', dest='debug', action='store_true',
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help='enables/disables test tube')
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parser.add_argument('--local', dest='local', action='store_true',
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help='enables local training')
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# optimizer
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parser.add_argument('--lr_scheduler_milestones', default=None, type=str)
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@@ -1,78 +1,2 @@
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"""
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These flags are useful to help debug a model.
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Fast dev run
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------------
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This flag is meant for debugging a full train/val/test loop.
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It'll activate callbacks, everything but only with 1 training and 1 validation batch.
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Use this to debug a full run of your program quickly
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.. code-block:: python
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# DEFAULT
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trainer = Trainer(fast_dev_run=False)
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Inspect gradient norms
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----------------------
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Looking at grad norms can help you figure out where training might be going wrong.
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.. code-block:: python
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# DEFAULT (-1 doesn't track norms)
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trainer = Trainer(track_grad_norm=-1)
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# track the LP norm (P=2 here)
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trainer = Trainer(track_grad_norm=2)
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Make model overfit on subset of data
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------------------------------------
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A useful debugging trick is to make your model overfit a tiny fraction of the data.
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setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check
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.. code-block:: python
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# DEFAULT don't overfit (ie: normal training)
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trainer = Trainer(overfit_pct=0.0)
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# overfit on 1% of data
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trainer = Trainer(overfit_pct=0.01)
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Print the parameter count by layer
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----------------------------------
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By default lightning prints a list of parameters *and submodules* when it starts training.
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.. code-block:: python
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# DEFAULT print a full list of all submodules and their parameters.
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trainer = Trainer(weights_summary='full')
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# only print the top-level modules (i.e. the children of LightningModule).
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trainer = Trainer(weights_summary='top')
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Print which gradients are nan
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-----------------------------
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This option prints a list of tensors with nan gradients::
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# DEFAULT
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trainer = Trainer(print_nan_grads=False)
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Log GPU usage
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-------------
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Lightning automatically logs gpu usage to the test tube logs.
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It'll only do it at the metric logging interval, so it doesn't slow down training.
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"""
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class MisconfigurationException(Exception):
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pass
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