Yes, sure. Ignite ML algorithms actively use data sending between nodes and in several cases uses per class loading mechanism. I want to exclude failures when algorithms use unserializable data or try to send lambdas with big context etc. >From this point of view we can just run ML examples on a little cluster where servers is started from binary build.
сб, 2 мар. 2019 г. в 08:39, Павлухин Иван <vololo...@gmail.com>: > Hi Alexey, > > Could you please share some background? What problem are you solving > with running tests against binary builds? Perhaps, we need something > similar for other Ignite sub-projects as well. > > пт, 1 мар. 2019 г. в 19:04, Алексей Платонов <aplaton...@gmail.com>: > > > > Hello, Igniters! > > I would like to create several tests for ML algorithms using binary > builds. > > These tests should work in this way: > > 1) Get last master (or user-defined branch) from git repository; > > 2) Build Ignite with a release profile and create binary build; > > 3) Run several Ignite instances from binary build; > > 4) Run examples or synthetic tests with a training of ML algorithms and > > inference; > > 5) Accumulate fails statistics on some board. > > > > Currently, I'm working with own open repository in git that contains > > scripts for Docker and Travis as the prototype. I want to complete these > > tests and contribute them to Ignite. > > > > Should I adapt such tests for TC after prototype complete or Travis can > be > > reused? Maybe such a process was created for other Ignite modules and I > can > > use it for ML. What do you think? > > > > Best regards > > Alexey Platonov. > > > > -- > Best regards, > Ivan Pavlukhin >
