Version: Next

Scoring with Feature Store

Do parallelized scoring of a Spark data frame from H2O.ai Feature Store in mini-batches against a MLOps deployment.

Prerequisites

h2o_mlops_scoring_client-*-py3-none-any.whl file or access to the Python Package Index (PyPI)
Java

Setup

Install h2o_mlops_scoring_client and featurestore with pip.

Notes

If running locally, the number of cores used (and thus parallel processes) can be overridden with:

num_cores = 10
h2o_mlops_scoring_client.spark_master = f"local[{num_cores}]"

featurestore will have its own Spark configuration and dependency requirements, depending on how it was installed.

Example Usage

import featurestore
import h2o_mlops_scoring_client
import os

Point the scorer to the conf directory you want to use.

h2o_mlops_scoring_client.spark_conf_dir = os.path.expanduser("~/.h2o_mlops_scoring_client/fs-conf")

Get the Spark session for the scoring client to pass to Feature Store.

spark = h2o_mlops_scoring_client.get_spark_session()

Default Feature Store Spark configuration applied.
:: loading settings :: url = jar:file:/Users/jgranados/miniconda3/envs/h2o/lib/python3.9/site-packages/pyspark/jars/ivy-2.5.0.jar!/org/apache/ivy/core/settings/ivysettings.xml


Ivy Default Cache set to: /Users/jgranados/.ivy2/cache
The jars for the packages stored in: /Users/jgranados/.ivy2/jars
org.apache.hadoop#hadoop-aws added as a dependency
io.delta#delta-core_2.12 added as a dependency
:: resolving dependencies :: org.apache.spark#spark-submit-parent-cc7b6061-aa08-4e2e-ad63-1e457ee799b4;1.0
    confs: [default]
    found org.apache.hadoop#hadoop-aws;3.3.4 in central
    found com.amazonaws#aws-java-sdk-bundle;1.12.262 in central
    found org.wildfly.openssl#wildfly-openssl;1.0.7.Final in central
    found io.delta#delta-core_2.12;2.2.0 in central
    found io.delta#delta-storage;2.2.0 in central
    found org.antlr#antlr4-runtime;4.8 in central
:: resolution report :: resolve 127ms :: artifacts dl 6ms
    :: modules in use:
    com.amazonaws#aws-java-sdk-bundle;1.12.262 from central in [default]
    io.delta#delta-core_2.12;2.2.0 from central in [default]
    io.delta#delta-storage;2.2.0 from central in [default]
    org.antlr#antlr4-runtime;4.8 from central in [default]
    org.apache.hadoop#hadoop-aws;3.3.4 from central in [default]
    org.wildfly.openssl#wildfly-openssl;1.0.7.Final from central in [default]
    ---------------------------------------------------------------------
    |                  |            modules            ||   artifacts   |
    |       conf       | number| search|dwnlded|evicted|| number|dwnlded|
    ---------------------------------------------------------------------
    |      default     |   6   |   0   |   0   |   0   ||   6   |   0   |
    ---------------------------------------------------------------------
:: retrieving :: org.apache.spark#spark-submit-parent-cc7b6061-aa08-4e2e-ad63-1e457ee799b4
    confs: [default]
    0 artifacts copied, 6 already retrieved (0kB/8ms)

Choose the MLOps scoring endpoint.

MLOPS_ENDPOINT_URL = "https://model.internal.dedicated.h2o.ai/981f5909-a1b2-4d1a-831b-e65675036934/model/score"

Get a Spark data frame from Feature Store along with a unique ID column used to identify each score.

fs = featurestore.Client("feature-store-api.internal.dedicated.h2o.ai", secure=True)
fs.auth.login()
project = fs.projects.get("bnpparibas")

DATA_FRAME = project.feature_sets.get("BNPParibas.csv").retrieve().as_spark_frame(spark)
ID_COLUMN = "ID"

17-07-2023 08:35:38 : INFO : Connecting to the server feature-store-api.internal.dedicated.h2o.ai ...
17-07-2023 08:35:40 : INFO : Server version: 0.18.1
17-07-2023 08:35:40 : INFO : Client version: 0.18.1
17-07-2023 08:35:40 : INFO : Opening browser to visit: https://auth.internal.dedicated.h2o.ai/auth/realms/hac/protocol/openid-connect/auth?client_id=hac-feature-store&code_challenge=RqAp-ffJCZAEARbrwjsHrRRHJ_ljTtu66siPzFRm0L0&code_challenge_method=S256&redirect_uri=https://feature-store.internal.dedicated.h2o.ai/Callback&response_type=code&scope=openid%20offline_access&state=pLykY74sux
                                                                                

And now we score.

spark_df = h2o_mlops_scoring_client.score_data_frame(
    mlops_endpoint_url=MLOPS_ENDPOINT_URL,
    id_column=ID_COLUMN,
    data_frame=DATA_FRAME,
)

spark_df.show()

[Stage 10:>                                                         (0 + 1) / 1]

+------+-----------+----------+
|    ID|   target.0|  target.1|
+------+-----------+----------+
|218057|  0.5960199|0.40398008|
|204563|  0.0965969| 0.9034031|
|126602| 0.18404573|0.81595427|
| 31077| 0.14552522| 0.8544748|
|184547|0.050189316| 0.9498107|
|215697|  0.1665029| 0.8334971|
|134328|  0.2938105| 0.7061895|
|174066| 0.72826254|0.27173743|
|100775| 0.20617479| 0.7938252|
|195239| 0.15678006|0.84321994|
|203262|  0.2985583| 0.7014417|
|109920| 0.25398308| 0.7460169|
| 76561| 0.12035632| 0.8796437|
|110243| 0.12437302|  0.875627|
|188865| 0.15772098|  0.842279|
|125723| 0.08976406|0.91023594|
| 25447|  0.5709034|0.42909658|
|175111| 0.09511238| 0.9048876|
|172872| 0.21316737|0.78683263|
|  2957| 0.38719475|0.61280525|
+------+-----------+----------+
only showing top 20 rows



                                                                                

Optionally merge the scores into the original data frame.

DATA_FRAME.join(spark_df, on=ID_COLUMN).toPandas()

	ID	target	v1	v2	v3	v4	v5	v6	v7	v8	...	v125	v126	v127	v128	v129	v130	v131	time_travel_column_auto_generated	target.0	target.1
0	148267	0	NaN	NaN	C	NaN	NaN	NaN	NaN	NaN	...	AC	NaN	NaN	NaN	0	NaN	NaN	2023-07-12 13:51:02	0.413667	0.586333
1	145203	0	1.182451	11.789324	C	4.323275	14.250168	2.579895	3.126089	0.145522	...	Q	1.830652	2.494916	2.307815	0	0.907064	1.803278	2023-07-12 13:51:02	0.390397	0.609603
2	156749	1	NaN	NaN	C	NaN	NaN	NaN	NaN	NaN	...	BM	NaN	NaN	NaN	1	NaN	NaN	2023-07-12 13:51:02	0.027944	0.972056
3	139024	1	0.718955	10.119158	C	3.687911	9.791527	2.246952	2.017312	0.243591	...	BH	1.301143	2.881558	1.942082	1	2.045535	0.753425	2023-07-12 13:51:02	0.100611	0.899389
4	144475	1	1.265389	9.579234	C	4.491259	8.399462	2.681258	2.325580	0.057701	...	AA	1.709107	2.282832	2.295695	0	1.105883	2.127660	2023-07-12 13:51:02	0.347554	0.652446
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
114316	35768	1	1.659937	6.722593	C	6.856028	10.310723	2.763571	3.633916	0.579856	...	AK	1.949568	2.792732	3.539050	0	0.829629	2.380952	2023-07-12 13:51:02	0.178249	0.821751
114317	132428	1	1.089197	6.767900	C	3.708279	NaN	1.836915	2.437444	NaN	...	P	1.643828	4.128643	NaN	0	1.719808	1.123596	2023-07-12 13:51:02	0.127764	0.872236
114318	38816	1	0.289063	7.806124	C	4.981398	8.174613	2.984375	3.164062	1.953577	...	BM	1.935144	4.204101	1.505429	1	0.849383	0.465115	2023-07-12 13:51:02	0.028165	0.971835
114319	147732	1	0.404814	9.926242	C	4.654258	9.132481	2.450765	2.844639	0.033742	...	P	1.616521	1.353939	2.889227	0	0.461538	1.333333	2023-07-12 13:51:02	0.312323	0.687677
114320	86572	0	NaN	NaN	C	NaN	NaN	NaN	NaN	NaN	...	AI	NaN	NaN	NaN	0	NaN	NaN	2023-07-12 13:51:02	0.352657	0.647343

114321 rows × 136 columns

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Scoring with Feature Store

Prerequisites​

Setup​

Notes​

Example Usage​

Prerequisites

Setup

Notes

Example Usage