[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[ https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Cyanny updated SPARK-22872: --- Description: Hi all, I have a project about model transformation with PMML, it needs to transform models with different types to pmml files. Moreover, JPMML(https://github.com/jpmml) has provided tools to do that，such as jpmml-sklearn, jpmml-xgboost etc. Our transformation API parameters must be concise and simple, in other words the less the better. I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model file, including schema info and model data info. but Spark PipelineModel only export a model file in parquet, there is no schema info in the model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel *Can spark PipelineModel include input data schema as metadata when do export? * The situations about machine learning libraries to jpmml are as the attached image, only xgboost and spark can't include schema info in exported model file. was: Hi all, I have a project about model transformation with PMML, it needs to transform models with different types to pmml files. Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools to do that. I need to provide a uniform API for user, the API parameters must be concise and simple, in other words the less the better. I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model file, including schema info and model data info. but Spark PipelineModel only export a model file in parquet, there is no schema info in the model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel *Can spark PipelineModel include input data schema as metadata when do export? * The situations about machine learning libraries to jpmml are as the attached img, only xgboost and spark can't include schema info in exported model file. > Spark ML Pipeline Model Persistent Support Save Schema Info > --- > > Key: SPARK-22872 > URL: https://issues.apache.org/jira/browse/SPARK-22872 > Project: Spark > Issue Type: IT Help > Components: ML >Affects Versions: 2.2.0 >Reporter: Cyanny >Priority: Minor > Attachments: jpmml-research.jpg > > > Hi all, > I have a project about model transformation with PMML, it needs to transform > models with different types to pmml files. > Moreover, JPMML(https://github.com/jpmml) has provided tools to do that，such > as jpmml-sklearn, jpmml-xgboost etc. Our transformation API parameters must > be concise and simple, in other words the less the better. > I came with a issue that, sklearn, tensorflow, and lightgbm can produce only > one model file, including schema info and model data info. > but Spark PipelineModel only export a model file in parquet, there is no > schema info in the model file. However, JPMML-SPARK converter needs two > arguments: Data Schema and PipelineModel > *Can spark PipelineModel include input data schema as metadata when do > export? * > The situations about machine learning libraries to jpmml are as the attached > image, only xgboost and spark can't include schema info in exported model > file. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[ https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Cyanny updated SPARK-22872: --- Description: Hi all, I have a project about model transformation with PMML, it needs to transform models with different types to pmml files. Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools to do that. I need to provide a uniform API for user, the API parameters must be concise and simple, in other words the less the better. I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model file, including schema info and model data info. but Spark PipelineModel only export a model file in parquet, there is no schema info in the model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel *Can spark PipelineModel include input data schema as metadata when do export? * The situations about machine learning libraries to jpmml are as the attached img, only xgboost and spark can't include schema info in exported model file. was: Hi all, I recently did a research about pmml, and our project needs to transform many models with different types to pmml files. Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools to do that. I need to provide a uniform API for user, the API parameters must be concise and simple, in other words the less the better. I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model file, including schema info and model data info. but Spark PipelineModel only export a model file in parquet, there is no schema info in the model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel *Can spark PipelineModel include input data schema as metadata when do export? * The situations about machine learning libraries to jpmml are as the attached img, only xgboost and spark can't include schema info in exported model file. > Spark ML Pipeline Model Persistent Support Save Schema Info > --- > > Key: SPARK-22872 > URL: https://issues.apache.org/jira/browse/SPARK-22872 > Project: Spark > Issue Type: IT Help > Components: ML >Affects Versions: 2.2.0 >Reporter: Cyanny >Priority: Minor > Attachments: jpmml-research.jpg > > > Hi all, > I have a project about model transformation with PMML, it needs to transform > models with different types to pmml files. > Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many > tools to do that. I need to provide a uniform API for user, the API > parameters must be concise and simple, in other words the less the better. > I came with a issue that, sklearn, tensorflow, and lightgbm can produce only > one model file, including schema info and model data info. > but Spark PipelineModel only export a model file in parquet, there is no > schema info in the model file. However, JPMML-SPARK converter needs two > arguments: Data Schema and PipelineModel > *Can spark PipelineModel include input data schema as metadata when do > export? * > The situations about machine learning libraries to jpmml are as the attached > img, only xgboost and spark can't include schema info in exported model file. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and our project needs to transform many
models with different types to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API parameters must
be concise and simple, in other words the less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema as metadata when do export? *
The situations about machine learning libraries to jpmml are as the attached
img, only xgboost and spark can't include schema info in exported model file.
was:
Hi all,
I recently did a research about pmml, and our project needs to transform many
models with different types to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API parameters must
be concise and simple, in other words the less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and our project needs to transform many
> models with different types to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API
> parameters must be concise and simple, in other words the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. However, JPMML-SPARK converter needs two
> arguments: Data Schema and PipelineModel
> *Can spark PipelineModel include input data schema as metadata when do
> export? *
> The situations about machine learning libraries to jpmml are as the attached
> img, only xgboost and spark can't include schema info in exported model file.
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and our project needs to transform many
models with different types to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API parameters must
be concise and simple, in other words the less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API parameters must
be concise and simple, in other words the less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and our project needs to transform many
> models with different types to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API
> parameters must be concise and simple, in other words the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. However, JPMML-SPARK converter needs two
> arguments: Data Schema and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as the attached
> img
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API parameters must
be concise and simple, in other words the less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API
> parameters must be concise and simple, in other words the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. However, JPMML-SPARK converter needs two
> arguments: Data Schema and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as the attached
> img
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. However, JPMML-SPARK converter needs two arguments:
Data Schema and PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. However, JPMML-SPARK converter needs two
> arguments: Data Schema and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as the attached
> img
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as the attached img
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!jpmml-research.jpg|thumbnail!
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as the attached
> img
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Attachment: jpmml-research.jpg
[^jpmml-research.jpg]
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !jpmml-research.jpg|thumbnail!
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Attachment: (was: jpmml-research.jpg)
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !jpmml-research.jpg|thumbnail!
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!jpmml-research.jpg|thumbnail!
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!jpmml-research.png|thumbnail!
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !jpmml-research.jpg|thumbnail!
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!jpmml-research.png|thumbnail!
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!jpmml-research.jpg|thumbnail!
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !jpmml-research.png|thumbnail!
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Attachment: jpmml-research.jpg
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !attachment-name.jpg|thumbnail!
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!jpmml-research.jpg|thumbnail!
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!attachment-name.jpg|thumbnail!
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
> Attachments: jpmml-research.jpg
>
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !jpmml-research.jpg|thumbnail!
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Attachment: ppmml-design2.jpg
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !attachment-name.jpg|thumbnail!
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Attachment: (was: ppmml-design2.jpg)
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !attachment-name.jpg|thumbnail!
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!attachment-name.jpg|thumbnail!
was:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> !attachment-name.jpg|thumbnail!
--
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[jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
[
https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cyanny updated SPARK-22872:
---
Description:
Hi all,
I recently did a research about pmml, and my project needs to transform many
models with different type to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools
to do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
```dataframe.limit(1).write.format("parquet").save("./model.schema")```
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
was:
Hi all,
I recently did a research about pmml, and my project need to transform many
model types to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) provides many tools to
do that. I need to provide a uniform API for user, the API arguments are the
less the better.
I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
one model file, including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema
info in the model file. And JPMML-SPARK needs two arguments: Schema and
PipelineModel
*Can spark PipelineModel include input data schema when export to a file? *
I found a solution, use dataframe API to export schema:
dataframe.limit(1).write.format("parquet").save("./model.schema")
*Are there any solutions to get the PipelineModel input schema?*
The situations about machine learning libraries to jpmml are as follows:
!http://i65.tinypic.com/2q1dxtd.jpg!
> Spark ML Pipeline Model Persistent Support Save Schema Info
> ---
>
> Key: SPARK-22872
> URL: https://issues.apache.org/jira/browse/SPARK-22872
> Project: Spark
> Issue Type: IT Help
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Cyanny
>Priority: Minor
>
> Hi all,
> I recently did a research about pmml, and my project needs to transform many
> models with different type to pmml files.
> Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many
> tools to do that. I need to provide a uniform API for user, the API arguments
> are the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only
> one model file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no
> schema info in the model file. And JPMML-SPARK needs two arguments: Schema
> and PipelineModel
> *Can spark PipelineModel include input data schema when export to a file? *
> I found a solution, use dataframe API to export schema:
> ```dataframe.limit(1).write.format("parquet").save("./model.schema")```
> *Are there any solutions to get the PipelineModel input schema?*
> The situations about machine learning libraries to jpmml are as follows:
> 
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