Python 3.9: training with CPU fails with double invocation, and no progress #35
Description
Works with Python 3.7 but not 3.9. It launches with double invocation, and no progress:
me:~/src/interscript/rababa/python (main *): python3.9 train.py --model "cbhg" --config config/cbhg.yml
CONFIGURATION CA_MSA.base.cbhg
- session_name : base
- data_directory : data
- data_type : CA_MSA
- log_directory : log_dir
- load_training_data : True
- load_test_data : False
- load_validation_data : True
- n_training_examples : None
- n_test_examples : None
- n_validation_examples : None
- test_file_name : test.csv
- is_data_preprocessed : False
- data_separator : |
- diacritics_separator : *
- text_encoder : ArabicEncoderWithStartSymbol
- text_cleaner : valid_arabic_cleaners
- max_len : 600
- reconcile : True
- max_steps : 2000000
- learning_rate : 0.001
- batch_size : 32
- adam_beta1 : 0.9
- adam_beta2 : 0.999
- use_decay : True
- weight_decay : 0.0
- embedding_dim : 256
- use_prenet : False
- prenet_sizes : [512, 256]
- cbhg_projections : [128, 256]
- cbhg_filters : 16
- cbhg_gru_units : 256
- post_cbhg_layers_units : [256, 256]
- post_cbhg_use_batch_norm : True
- use_mixed_precision : False
- optimizer_type : Adam
- device : cuda
- evaluate_frequency : 5000
- evaluate_with_error_rates_frequency : 5000
- n_predicted_text_tensorboard : 10
- model_save_frequency : 5000
- train_plotting_frequency : 50000000
- n_steps_avg_losses : [100, 500, 1000, 5000]
- error_rates_n_batches : 10000
- test_model_path : None
- train_resume_model_path : None
- len_input_symbols : 44
- len_target_symbols : 17
- optimizer : OptimizerType.Adam
- git_hash : v0.1.0-34-g4e8ffa9
The model has 15413521 trainable parameters parameters
/usr/local/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")
Length of training iterator = 86962
Length of valid iterator = 4676
data loaded
----------------------------------------------------------
Eval: 0%| | 0/4676 [00:00<?, ?it/s]
CONFIGURATION CA_MSA.base.cbhg | 0/4676 [00:00<?, ?it/s]
- session_name : base | 0/2000000 [00:00<?, ?it/s]
- data_directory : data
- data_type : CA_MSA
- log_directory : log_dir
- load_training_data : True
- load_test_data : False
- load_validation_data : True
- n_training_examples : None
- n_test_examples : None
- n_validation_examples : None
- test_file_name : test.csv
- is_data_preprocessed : False
- data_separator : |
- diacritics_separator : *
- text_encoder : ArabicEncoderWithStartSymbol
- text_cleaner : valid_arabic_cleaners
- max_len : 600
- reconcile : True
- max_steps : 2000000
- learning_rate : 0.001
- batch_size : 32
- adam_beta1 : 0.9
- adam_beta2 : 0.999
- use_decay : True
- weight_decay : 0.0
- embedding_dim : 256
- use_prenet : False
- prenet_sizes : [512, 256]
- cbhg_projections : [128, 256]
- cbhg_filters : 16
- cbhg_gru_units : 256
- post_cbhg_layers_units : [256, 256]
- post_cbhg_use_batch_norm : True
- use_mixed_precision : False
- optimizer_type : Adam
- device : cuda
- evaluate_frequency : 5000
- evaluate_with_error_rates_frequency : 5000
- n_predicted_text_tensorboard : 10
- model_save_frequency : 5000
- train_plotting_frequency : 50000000
- n_steps_avg_losses : [100, 500, 1000, 5000]
- error_rates_n_batches : 10000
- test_model_path : None
- train_resume_model_path : None
- len_input_symbols : 44
- len_target_symbols : 17
- optimizer : OptimizerType.Adam
- git_hash : v0.1.0-34-g4e8ffa9
The model has 15413521 trainable parameters parameters
/usr/local/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")
Length of training iterator = 86962
Length of valid iterator = 4676
data loaded
----------------------------------------------------------
Eval: 0%| | 0/4676 [00:00<?, ?it/sTraceback (most recent call last): | 0/4676 [00:00<?, ?it/s]
File "<string>", line 1, in <module> | 0/2000000 [00:00<?, ?it/s]
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
prepare(preparation_data)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 268, in run_path
return _run_module_code(code, init_globals, run_name,
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/Users/me/src/interscript/rababa/python/train.py", line 43, in <module>
trainer.run()
File "/Users/me/src/interscript/rababa/python/trainer.py", line 210, in run
for batch_inputs in repeater(train_iterator):
File "/Users/me/src/interscript/rababa/python/util/utils.py", line 64, in repeater
for data in loader:
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
return self._get_iterator()
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
w.start()
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 42, in _launch
prep_data = spawn.get_preparation_data(process_obj._name)
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "/usr/local/Cellar/[email protected]/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Eval: 0%| | 0/4676 [00:00<?, ?it/s]
WER/DER : : 0%| | 0/4676 [00:00<?, ?it/s]
0%| | 0/2000000 [00:00<?, ?it/s]
Originally posted by @ronaldtse in #22 (comment)