We trained “sales_model” previously, to view the training request that we sent, we can all this API. As you will see, ML engine adds some more data points to the request for help
Method : GET
URI : /ml/<schema-name>/<model_name>/request
Example
curl -X GET http://192.168.1.105:18080/ml/website/sales_model/request
Response
{ "schema-name": "website", "training_details": { "training_source": "visitor_upload.txt", "file_size_mb": 1, "target_idx": 5, "input_format": "JSON", "is_src_global": 0, "train_speed": 3, "bucket_name": "ml_bucket_info", "training_source_type": 1, "expected_format": "SVM" }, "algo_type": "SVM", "tune_params": 1, "attr_list": [ { "position": 0, "name": "vid" }, { "position": 1, "name": "prod" }, { "position": 2, "name": "pgid" }, { "position": 3, "name": "catid" }, { "name": "items", "position": 4 }, { "name": "price", "position": 5 } ], "scale": 1, "attr_type": 3, "algo_param": { "probability": 0, "svm_type": 3, "eps_svr": 0.1, "kernel_type": 0, "shrinking": 0, "cost": 100, "termination_criteria": 0.001 }, "model_name": "sales_model", "train_start_ts": 1648550728425224, "train_end_ts": 1648550728434816, "train_req_state": 25, "tuned_algo_params": { "C": 0.0625, "g": 4, "cache_size": 100, "coef0": 0, "degree": 3, "eps": 0.001, "kernel_type": 0, "nr_weight": 0, "nu": 0.5, "p": 0.1, "prob": 0, "shrinking": 0, "svm_type": 3, "train_perf": 704 }, "train_log": { "log": [ "1648550728424954 : received train request", "verification done", "retrieved the training file [ sales_model__website__visitor_upload.txt ] from BRS", "file reformat done", "scaling and tuning the model params, by training many different models", "scaling and tuning done, selected params = 0.062500, 4.000000, 704.514490", "starting training for model [ sales_model__website ]", "1648550728434808 : training successful!" ], "schema-name": "website", "model_name": "sales_model", "algo_type": "SVM", "train_start_ts": 1648550728425224, "train_end_ts": 1648550728434809, "errorcode": 0 } }