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Answered
For The Frameworks Which Are Supported In Built, Trains Stores The Trained Model As Output Model E.G. For Xgboost Here

for the frameworks which are supported in built, trains stores the trained model as output model e.g. for xgboost here https://github.com/allegroai/trains/blob/65a4aa7aa9/examples/frameworks/xgboost/xgboost_sample.py#L46 how can i do the same for other frameworks which are not supported in built e.g. lightgbm, catboost etc..

  
  
Posted 4 years ago
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Answers 12


so just under models dir rather than artifact... any way to achieve this or i should just treat it as artifact ?

  
  
Posted 4 years ago

seems like setting to fileserver_url did the trick

  
  
Posted 4 years ago

thanks

  
  
Posted 4 years ago

AgitatedDove14 Morning... so what should the value of "upload_uri" to set to, fileserver_url e.g. http://localhost:8081 ?

  
  
Posted 4 years ago

AgitatedDove14 it seems uploading artifact and uploading models are two different things when it comes to treating fileserver... as when i upload artifact it works as expected but when uploading model using outputmodel class, it wants output_uri path.. wondering how can i as it to store it under the fileserver like artifacts LightGBM.1104445eca4749f89962669200481397/artifacts/Model%20object/model.pkl

  
  
Posted 4 years ago

so i was expecting that uploaded model will be for example LightGBM.1104445eca4749f89962669200481397/models/Model%20object/model.pkl

  
  
Posted 4 years ago

AgitatedDove14 when using OutputModel(task, name='LightGBM model', framework='LightGBM').update_weights(f"{args.out}/model.pkl") i am seeing this in the logs No output storage destination defined, registering local model /tmp/model.pkl when i got to trains UI.. i see the model name and details but when i try to download it point to the path file:///tmp/model.pkl which is incorrect wondering how to fix it

  
  
Posted 4 years ago

PompousParrot44
you can always manually store/load models, example: https://github.com/allegroai/trains/blob/65a4aa7aa90fc867993cf0d5e36c214e6c044270/examples/reporting/model_config.py#L35 Sure, you can patch any frame work with something similar to what we do in xgboost, any such PR will be greatly appreciated! https://github.com/allegroai/trains/blob/master/trains/binding/frameworks/xgboost_bind.py

  
  
Posted 4 years ago

looking at the code https://github.com/allegroai/trains/blob/65a4aa7aa90fc867993cf0d5e36c214e6c044270/trains/model.py#L1146 this happens when storage_uri is not defined where as i have this under trains.conf so task should have it ?

  
  
Posted 4 years ago

thanks AgitatedDove14 for the links.. seems like i might try first one if it works out .. before going the route to create a full framework support as in our case team uses multiple different frameworks

  
  
Posted 4 years ago

so what should the value of "upload_uri" to set to, 

fileserver_url

 e.g. 

 ?

yes, that would work.

  
  
Posted 4 years ago

PompousParrot44 the fundamental difference is that artifacts are uploaded manually (i.e. a user will specifically "ask" to upload an artifact), models are logged automatically and a user might not want them uploaded (imagine debugging sessions, or testing).
By adding the 'upload_uri' arguments, you can specify to trains that you want all models to be automatically uploaded (not just logged).
Now here is the nice thing, when running using the trains-agent, you can have:
Always upload the model by configuring the https://github.com/allegroai/trains-agent/blob/699d13bbb34649c7e5337b4187cda59b7fa6fd33/docs/trains.conf#L262 In the Web UI under the execution tab, set the "output destination". It is equivalent to setting the output_uri

  
  
Posted 4 years ago
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