How was Dask slow? about research HOT 2 OPEN

in the view steering model.py file
I found his error (ValueError: bad marshal data (unknown type code)) result when trying to execute the view steering model.py
here is the result from the cmd prompt

Traceback (most recent call last):
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 229, in func_load
raw_code = codecs.decode(code.encode('ascii'), 'base64')
UnicodeEncodeError: 'ascii' codec can't encode character '\xe0' in position 46:
ordinal not in range(128)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "view_steering_model.py", line 94, in
model = model_from_json(json.load(jfile))
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\models.py", line 349,
in model_from_json
return layer_module.deserialize(config, custom_objects=custom_objects)
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\layers_init_.py", l
ine 55, in deserialize
printable_module_name='layer')
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\models.py", line 1349,
in from_config
layer = layer_module.deserialize(conf, custom_objects=custom_objects)
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\layers_init_.py", l
ine 55, in deserialize
printable_module_name='layer')
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\layers\core.py", line
711, in from_config
function = func_load(config['function'], globs=globs)
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 234, in func_load
code = marshal.loads(raw_code)
ValueError: bad marshal data (unknown type code)

from research.

you can concatenate several hdf5 buckets with dask and have a unified numpy like interface. That was pretty cool. But slicing was slower than what we did here.

from research.