Comments (2)
If I create a model using jpmml-tensorflow can I score it using openscoring?
You can score any PMML document with Openscoring. You can generate this PMML document using the JPMML-TensorFlow library, or any other JPMML conversion library (eg. JPMML-R, JPMML-SkLearn, JPMML-SparkML, JPMML-XGBoost etc).
If there is no tensorflow serving integration yet
There is no need for TensorFlow integration. All PMML documents are self-contained (ie. zero runtime dependencies).
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@vruusmann will try it out and let you know the perf compared to tensorflow serving...
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Related Issues (20)
- Excess use of memory by pmml.gz files HOT 1
- Openscoring war deployment on Tomcat- admin role authorization HOT 2
- get the predictions results HOT 1
- [Question] Default folder for loading models? HOT 1
- Client side throw an exception that java.lang.IllegalStateException: InjectionManagerFactory not found. HOT 2
- .pmml not reconize on upload
- Is there a configuration for enabling/disabling REST endpoints HOT 1
- Error: SEVERE: Failed to evaluate java.lang.NumberFormatException: For input string: "X" HOT 2
- feature request: add deploy method - pip/docker HOT 2
- How to enable CORS for the Server HOT 1
- the lastest jar version 2.0 "xgbValue" is not defined HOT 1
- Missing field when trying to score lightgbm model HOT 9
- IllegalArgumentException: Attribute 'GradientBoostingClassifier.loss_' has an unsupported value (Python class BinomialDeviance) HOT 1
- Service should log information about exceptional conditions before raising an exception HOT 6
- "message" : "Attribute with value RegressionTable@targetCategory=1 is not valid" HOT 4
- How to define the model.version? HOT 1
- openscoring-docker -- PMML 4.4 not supported HOT 1
- Missing derived field declaration HOT 2
- Secured requests HOT 1
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