Since Yesterday I get the following error when I am running my python scripts accessing the API. The error will happen randomly during the script execution. I am blocked since the scripts are updating important meta data for my application. We use the SaaS solution.
File “C:\Python27\lib\site-packages\KalturaClient\Plugins\Core.py”, line 52627, in list
resultNode = self.client.doQueue()
File “C:\Python27\lib\site-packages\KalturaClient\Client.py”, line 324, in doQueue
postResult = self.doHttpRequest(url, params, files)
File “C:\Python27\lib\site-packages\KalturaClient\Client.py”, line 274, in doHttpRequest
f = self.openRequestUrl(url, params, files, self.requestHeaders)
File “C:\Python27\lib\site-packages\KalturaClient\Client.py”, line 237, in openRequestUrl
raise KalturaClientException(e, KalturaClientException.ERROR_CONNECTION_FAILED)
KalturaClient.Base.KalturaClientException: [Errno 10054] An existing connection was forcibly closed by the remote host (-4)
However, do share your code and I’ll try to reproduce.
Also, are you working over HTTPs or HTTP?
In addition, what version of the Python clientlibs are you using?
Hi Jess,
I am using http can you tell me how I can force https to try?
I do not know how to find out the version of the clientlibs so please let me know how to find out or how to update to the latest.
Regards,
Petros
As to how to use HTTPs, a simple matter of passing https://www.kaltura.com instead of http://www.kaltura.com.
I’d recommend doing so for security reasons but I doubt it is the cause of your issue.
If you’re still having issues with the version I posted above, please share your code.
You are getting this error because you did not specify any inputs to your model, and Keras is trying to set them on calling model.fit() . The assertion is there because each model wrapped in a Sequential container should take only one input.
To implement what you want, you probably want to go for Keras’ Python Functional API instead of the Sequential API. Something along these lines:
from keras.models import Model
from keras.layers import Concatenate, Input, Dense
# First model
first_input = Input((2, ))
first_dense = Dense(128)(first_input)
# Second model
second_input = Input((10, ))
second_dense = Dense(64)(second_input)
# Concatenate both
merged = Concatenate()([first_dense, second_dense])
output_layer = Dense(1)(merged)
model = Model(inputs=[first_input, second_input], outputs=output_layer)
model.compile(optimizer='sgd', loss='mse')