sdannels
commented
1 year ago Hi Jon,
I ran this in Google Colab notebooks, which run on Ubuntu. The multiprocessing start method "fork" is supported on Linux but not on Windows according this article which gives a lot more details: https://superfastpython.com/multiprocessing-pool-context/.
That article also gives ways to overwrite the default start method. You could try using that to switch the start method to "spawn", but I don't fully know what effect that would have on the rest of the code in terms of replication.
An easier solution may just be to run the code in a Colab notebook. They are free with a Google account, and they offer free GPU run times, which were necessary for some of the training tasks.
Hello, Trying to do an initial run of the code and getting the following at the bottom of the below error message:
ValueError: multiprocessing_context option should specify a valid start method in ['spawn'], but got multiprocessing_context='fork'
Can you please advise how/where I should be setting the 'multiprocessing_context' = 'spawn' so this will run? Thanks! Jon
ValueError Traceback (most recent call last) Cell In[9], line 19 2 model = DGAN(DGANConfig(
3 # length of training examples and of generated synthetic data 4 max_sequence_len=features.shape[1], (...) 15 epochs=2000, 16 )) 18 # train model ---> 19 model.train_numpy( 20 attributes = attributes, 21 features = features, 22 feature_types= [OutputType.CONTINUOUS] features.shape[2], 23 attribute_types = [OutputType.DISCRETE] attributes.shape[1], 24 )
File c:\Users\noble\anaconda3\envs\py3_9\lib\site-packages\gretel_synthetics\timeseries_dgan\dgan.py:343, in DGAN.train_numpy(self, features, feature_types, attributes, attribute_types, progress_callback) 335 raise ValueError(f"NaN found in internal attributes. {NAN_ERROR_MESSAGE}") 337 dataset = TensorDataset( 338 torch.Tensor(internal_attributes), 339 torch.Tensor(internal_additional_attributes), 340 torch.Tensor(internal_features), 341 ) --> 343 self._train(dataset, progress_callback=progress_callback) ... 404 'multiprocessing_context={!r}').format(valid_start_methods, multiprocessing_context)) 405 multiprocessing_context = multiprocessing.get_context(multiprocessing_context) 407 if not isinstance(multiprocessing_context, python_multiprocessing.context.BaseContext):
ValueError: multiprocessing_context option should specify a valid start method in ['spawn'], but got multiprocessing_context='fork'