fdclient 3.1.2

fastDeploy python client

from fdclient import FDClient

client = FDClient('http://localhost:8080') # optional compression=False to disable zstd compression

# infer
response = client.infer([obj_1, obj_2, ...]) # optional unique_id='some_id' to specify a unique id for the request

# infer in background
response_future = client.infer_background([obj_1, obj_2, ...]) # optional unique_id='some_id' to specify a unique id for the request
response = response_future.result() # wait for the response and get it

• By default fdclient communicates with fastDeploy server via pickles
• pickle is very useful and makes sense when using fastDeploy server as a micro service internally i.e: all requests to fastDeploy originate from code you have writtem
• PICKLE is secure if all the inputs to fastDeploy are originating from your code and not direct external user's pickles
• PICKLE is unsecure if you are passing external user inputs to fastDeploy directly without validation in between
• start fastDeploy serve with --config "allow_pickle=false" if the fastDeploy APIs are exposed to outside
• allow_pickle=false config on server side makes fdclient use msgpack if available or json if msgpack not available.

If pickle is unsecure, why use it at all?

• pickle is great to send or receive arbitary inputs and outputs
• if allow_pickle=true (default) your inputs and outputs can be any python objects, eg: np arrays, pd dataframes, float32 anything ....
• pickle is only unsecure if you are unpickling objects pickled by others (since they can insert malicious code)
• If fastDeploy is being used only for internal microservices, pickle is the best way so enabled by default