gilgameshjw
commented
3 years ago done
Closed ronaldtse closed 3 years ago
gilgameshjw
commented
3 years ago done
ronaldtse
commented
3 years ago I'm facing this double-invocation error:
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/python@3.9/3.9.6/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/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
prepare(preparation_data)
File "/usr/local/Cellar/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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 209, 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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/usr/local/Cellar/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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.should be solved.
ronaldtse
commented
3 years ago @gilgameshjw I'm still seeing the same issue... 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/python@3.9/3.9.6/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/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
prepare(preparation_data)
File "/usr/local/Cellar/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/usr/local/Cellar/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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/python@3.9/3.9.6/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]Works on GitHub Actions, but still failing locally... there seems to be a double invocation still in the logs.
gilgameshjw
commented
3 years ago i think i fixed it yesterday
gilgameshjw
commented
3 years ago Never seen that pbm that I dont understand.
Was python installed with conda, 3.6? That might be the issue!
ronaldtse
commented
3 years ago I just tried Pyenv with 3.7, upgraded pip, and updated setup.py. It seems to work! (Just extremely slow...)
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 : v0.1.0-61-g46102ec
The model has 15413521 trainable parameters parameters
/Users/me/.pyenv/versions/3.7.11/lib/python3.7/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[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads) | 0/2000000 [00:00<?, ?it/s]
[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
/Users/me/.pyenv/versions/3.7.11/lib/python3.7/site-packages/torch/nn/functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1156.)
return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)
Global Step 4: 0%| | 3/2000000 [00:26<4821:01:28, 8.68s/it]
loss: 3.081960916519165 There are some warnings, but doesn't seem to affect training:
[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native
[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
/Users/me/.pyenv/versions/3.7.11/lib/python3.7/site-packages/torch/nn/functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1156.)
return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)Interestingly it works with Python 3.6, 3.7 but not 3.9 (haven't tried 3.8).
ronaldtse
commented
3 years ago Closing this since 3.7 works.
Currently fails with no GPU: