Saravanan666/pypi-org huggingface dataset:
Package: h2o-drive
h2o-driveH2O Drive Python Client.Installationpipinstallh2o-driveUsageConnecting to H2O Driveimporth2o_drivedrive=awaith2o_drive.Drive()When used within theH2O AI Cloudenvironment or locally with theH2O AI Cloud CLIconfigured, no further configuration is needed.h2o_drive.Drive()can be configured via its optional parameters:token: Token or token provider to use. If not specified, it's determined automatically.endpoint_url: URL of the Drive endpoint. If not specified, it's determined automatically.sts_endpoint_url: URL of the STS endpoint. If not specified, it's determined automatically.environment: URL of the H2O.ai cloud environment. Used only when determining a missing token or URL.config_path: Path to the H2O.ai CLI config. Used only when determining a missing token or URL.session_ttl_seconds: The duration, in seconds, of the role session.read_timeout_seconds: The time in seconds until a timeout exception is thrown when reading from Drive.Object OperationsTo perform object operations, first navigate to the user's personal drive bucket:my_bucket=drive.my_bucket()Continue operating at the root of the user's bucket (generally not needed) or navigate into a particular space (a.k.a. a directory path) with one of the following.  Thehomespace is a good place to start.my_home_space=my_bucket.home()# ormy_other_space=my_bucket.workspace("my_other_space")Once in a space within a user's bucket (or the root of the bucket), the following operations are possible:upload_fileuploads a local file to the user's Drive. It takes two arguments:file_name: The file to upload.object_name: The name to give to the uploaded file once it becomes an object in our Drive bucket.list_objectslists objects in the user's drive. It takes one optional argument:prefix: When set, only objects whose names start with the specified prefix are returned.download_filedownloads an object as a local file. It takes two arguments:object_name: The name of the object (including prefix) to download.file_name: Path to where the object should be saved as a local file.delete_objectDeletes an object from the user's drive. It takes a single argument:object_name: The name of the object (including prefix) to delete.generate_presigned_urlgenerates a presigned URL through which an object in the drive can be accessed. It takes two arguments:object_name: The name of the object (including prefix) to generate a presigned URL for.ttl_seconds: (Optional) How long, in seconds, the URL should be good for.ExamplesExample: Connect to thehomespace of a user's drive bucket, write a local file, and then list the contentsimporth2o_drivedrive=awaith2o_drive.Drive()my_home_space=drive.my_bucket().home()withopen("test-file.txt","w")asf:f.write("Hello, world!")awaitmy_home_space.upload_file("test-file.txt","uploaded-object-name.txt")objects=awaitmy_home_space.list_objects()print(objects)
Package: h2o-featurestore
Feature Store ClientFeature Store is used to manage and provide easy access for curated features. The goal is to abstract the underlying storage of the features and also provide a consistent and unified storage for the features meta-data. The features should be versioned so data scientist can be sure that the model trained on specific combination of features will always lead to the same results.Feature Store Client provides a simple, high-level API for integrating Python environments with H2O.ai's Feature Store.
Package: h2ogpte
H2O H2OGPTE Python ClientInstallpipinstallh2ogpteUsagefromh2ogpteimportH2OGPTEclient=H2OGPTE(address='https://h2ogpte.genai.h2o.ai',api_key='sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',)# Create a new collectioncollection_id=client.create_collection(name='Contracts',description='Paper clip supply contracts',)# Create documents# Note: Done for demonstration purposes only (not usually needed)withopen('dunder_mifflin.txt','w')asf:f.write('There were 55 paper clips shipped, 22 to Scranton and 33 to Filmer.')withopen('initech.txt','w')asf:f.write('David Brent did not sign any contract with Initech.')# Upload documents# Many file types are supported: text/image/audio documents and archiveswithopen('dunder_mifflin.txt','rb')asf:dunder_mifflin=client.upload('Dunder Mifflin.txt',f)withopen('initech.txt','rb')asf:initech=client.upload('IniTech.txt',f)# Ingest documents (Creates previews, chunks and embeddings)client.ingest_uploads(collection_id,[dunder_mifflin,initech])# Create a chat sessionchat_session_id=client.create_chat_session(collection_id)# Query the collectionwithclient.connect(chat_session_id)assession:reply=session.query('How many paper clips were shipped to Scranton?',timeout=60,)print(reply.content)reply=session.query('Did David Brent co-sign the contract with Initech?',timeout=60,)print(reply.content)# Summarize each documentdocuments=client.list_documents_in_collection(collection_id,offset=0,limit=99)fordocindocuments:summary=client.summarize_document(document_id=doc.id,timeout=60,)print(summary.content)
Package: h2o-helium
H2O Helium Python ClientInstallpipinstallh2o-heliumUsagefromh2o_heliumimportHeliumhelium=Helium(address='https://helium.h2o.ai',api_key='sk-xxxxxx')# Create a new collectioncollection_id=helium.create_collection(name='Contracts',description='Paper clip supply contracts')# Upload documentswithopen('/path/to/dunder_mifflin.pdf','rb')asf:dunder_mifflin=helium.upload('Dunder Mifflin.pdf',f)withopen('/path/to/wernham_hogg.pdf','rb')asf:initech=helium.upload('Wernham Hogg.pdf',f)# Ingest documentshelium.ingest_uploads(collection_id,[dunder_mifflin,initech])# Create a chat sessionchat_session_id=helium.create_chat_session(collection_id)# Query the collectionwithhelium.connect(chat_session_id)assession:reply=session.query('How many paper clips were shipped to Scranton?',timeout=10)print(reply.content)reply=session.query('Did David Brent co-sign the contract with Initech?',timeout=10)print(reply.content)
Package: h2o_kernel
h2o_kernelis a simple h2o wrapper to Jupyter kernel.InstallationTo installh2o_kernelfrom PyPI:pip install h2o_kernel
python -m h2o_kernel.installUsing the H2o kernelNotebook: TheNewmenu in the notebook should show an option for an H2o notebook.Console frontends: To use it with the console frontends, add--kernelh2oto
their command line arguments.H2O Config: You can use config file or evn to config h2o cluster addr, all configs are the same with h2o.connect() parameters, priority/etc/h2okernelrc<$HOME/.h2okernelrc<env,
environment only can changes url(by H2O_URL), ip(by H2O_IP), port(by H2O_PORT).
Package: h2o-mlops-scoring-client
H2O MLOps Scoring ClientA Python client library to simplify robust mini-batch scoring against an H2O MLOps scoring endpoint. It can run on your local PC, a stand alone server, Databricks, or a Spark 3 cluster.Scoring Pandas data frames is as easy as:pipinstallh2o-mlops-scoring-clientimporth2o_mlops_scoring_clientscores_df=h2o_mlops_scoring_client.score_data_frame(mlops_endpoint_url="https://.../model/score",id_column="ID",data_frame=df,)Scoring from a source to a sink is also possible throughpyspark:pipinstallh2o-mlops-scoring-client[PYSPARK]importh2o_mlops_scoring_clienth2o_mlops_scoring_client.score_source_sink(mlops_endpoint_url="https://.../model/score",id_column="ID",source_data="s3a://...",source_format=h2o_mlops_scoring_client.Format.CSV,sink_location="s3a://...",sink_format=h2o_mlops_scoring_client.Format.PARQUET,sink_write_mode=h2o_mlops_scoring_client.WriteMode.OVERWRITE)InstallationRequirementsLinux or Mac OS (Windows is not supported)Java (only required forpysparkinstalls)Python 3.8 or greaterInstall from PyPIpipinstallh2o-mlops-scoring-clientpysparkis no longer included in a default install. To includepyspark:pipinstallh2o-mlops-scoring-client[PYSPARK]FAQWhen should I use the MLOps Scoring Client?Use when batch scoring processing (authenticating and connecting to source or sink, file/data processing or conversions, etc.) can happen external to H2O AI Cloud but you want to stay within the H2O MLOps workflow (projects, scoring, registry, monitoring, etc.).Where does scoring take place?As the batch scoring processing occurs, the data is sent to an H2O MLOps deployment for scoring. The scores are then returned for the batch scoring processing to complete.What Source/Sinks are supported?The MLOps scoring client can support many source/sinks, including:ADLS Gen 2Databases with a JDBC driverLocal file systemGBQS3SnowflakeWhat file types are supported?The MLOps scoring client can read and write:CSVParquetORCBigQuery tablesJDBC queriesJDBC tablesSnowflake queriesSnowflake tablesIf there's a file type you would like to see supported, please let us know.I want model monitoring for batch scoring, can I do that?Yes. The MLOps Scoring Client uses MLOps scoring endpoints which are automatically monitored.Is a Spark installation required?No. If you're scoring Pandas data frames, then no extra Spark install or configuration is needed. If you want to connect to an external source or sink, you'll need to installpysparkand do a small amount of configuration.
Package: h2o-nitro-altair
Altair plugin for H2O NitroThis plugin lets you useAltairvisualizations inNitroapps.DemoView source.Installpip install h2o-nitro-altairUsageImportaltair_pluginandaltair_boxfromh2o_nitro_altair.Addaltair_plugin()to your Nitro app.Usealtair_box(chart)to render Altair charts.importaltairasaltfromvega_datasetsimportdatafromh2o_nitroimportView,web_directoryfromh2o_nitro_altairimportaltair_plugin,altair_boxdefmain(view:View):view(altair_box(make_plot()))nitro=View(main,title='Nitro + Altair',caption='A minimal example',plugins=[altair_plugin()],# Include the Altair plugin)defmake_plot():returnalt.Chart(data.cars()).mark_point().encode(x='Horsepower',y='Miles_per_Gallon',color='Origin',).interactive()Advanced UsageYou can passVega Embed Optionstoaltair_box()for
more control.Example:altair_box(chart,options=dict(renderer='svg',scaleFactor=2))
Package: h2o-nitro-bokeh
Bokeh plugin for H2O NitroThis plugin lets you useBokehvisualizations inNitroapps.DemoView source.Installpip install h2o-nitro-bokehUsageImport the plugin:fromh2o_nitro_bokehimportbokeh_plugin,bokeh_boxRegister the plugin:nitro=View(main,title='My App',caption='v1.0',plugins=[bokeh_plugin()])Use the plugin:# Make a plotx=[1,2,3,4,5]y=[6,7,2,4,5]p=figure(title="Simple line example",x_axis_label="x",y_axis_label="y")p.line(x,y,legend_label="Temp.",line_width=2)# Display the plotview(bokeh_box(p))Change Logv0.2.1 - Jun 09, 2022FixedDon't return value from plots.v0.2.0 - May 39, 2022PerfQuicker loadingv0.1.0 - May 25, 2022Initial Version
Package: h2o-nitro-matplotlib
Matplotlib plugin for H2O NitroThis plugin lets you useMatplotlibandSeabornvisualizations inNitroapps.Warning: Try to avoid pyplot in web apps!pyplot maintains references to the opened figures to make show() work, but
this will cause memory leaks unless the figures are properly closed[^1].[^1]: SeeMatplotlib docs on embeddingDemoMatplotlibView source.SeabornView source.Installpip install h2o-nitro-matplotlibUsageImport the plugin:fromh2o_nitro_matplotlibimportmatplotlib_plugin,matplotlib_boxRegister the plugin:nitro=View(main,title='My App',caption='v1.0',plugins=[matplotlib_plugin()])Use the plugin:# Make a figure:x=np.linspace(0,2,100)# Sample data.fig=Figure()ax=fig.subplots()ax.plot(x,x,label='linear')# Plot some data on the axes.ax.plot(x,x**2,label='quadratic')# Plot more data on the axes...ax.plot(x,x**3,label='cubic')# ... and some more.ax.set_xlabel('x label')# Add an x-label to the axes.ax.set_ylabel('y label')# Add a y-label to the axes.ax.legend()# Add a legend.# Display the figure:view(matplotlib_box(fig))Change Logv0.2.1 - Jun 09, 2022FixedDon't return value from plots.v0.2.0 - May 30, 2022AddedUse global pyplot if a figure is not passed (for Seaborn support).v0.1.0 - May 29, 2022Initial Version
Package: h2o-nitro-plotly
Plotly plugin for H2O NitroThis plugin lets you usePlotlyvisualizations inNitroapps.DemoView source.Installpip install h2o-nitro-plotlyUsageImport the plugin:fromh2o_nitro_plotlyimportplotly_plugin,plotly_boxRegister the plugin:nitro=View(main,title='My App',caption='v1.0',plugins=[plotly_plugin()])Use the plugin:# Make a plotdf=px.data.iris()fig=px.scatter(df,x="sepal_width",y="sepal_length",color="species")# Display the plotview(plotly_box(fig))Change Logv0.1.0 - Dec 1, 2022Initial Version
Package: h2o-notebook-theme
h2o-notebook-themeRequirementsJupyterLab >= 3.0Installpipinstallh2o-notebook-themeContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the h2o-notebook-theme directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallh2o-notebook-theme
jupyterlabextensionuninstallh2o-notebook-theme
Package: h2o-pysparkling-2.1
This package contains complete functionality for training and scoring Sparkling Water/H20-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.htmlFor Spark 2.2 -http://docs.h2o.ai/sparkling-water/2.2/latest-stable/doc/pysparkling.htmlFor Spark 2.1 -http://docs.h2o.ai/sparkling-water/2.1/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-2.2
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.htmlFor Spark 2.2 -http://docs.h2o.ai/sparkling-water/2.2/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-2.3
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-2.4
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-3.0
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-3.1
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-3.2
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-3.3
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-3.4
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-3.5
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible
to use this package for scoring with Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.5 -http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.htmlFor Spark 3.4 -http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.htmlFor Spark 3.3 -http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.htmlFor Spark 3.2 -http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.htmlFor Spark 3.1 -http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.htmlFor Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
Package: h2o-pysparkling-scoring-2.1
This package contains just functionality for scoring with Sparkling Water, H20-3 and Driverless AI MOJO models.PySparkling Documentation is hosted at our documentation page:For Spark 3.0 -http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.htmlFor Spark 2.4 -http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.htmlFor Spark 2.3 -http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.htmlFor Spark 2.2 -http://docs.h2o.ai/sparkling-water/2.2/latest-stable/doc/pysparkling.htmlFor Spark 2.1 -http://docs.h2o.ai/sparkling-water/2.1/latest-stable/doc/pysparkling.html