Migration Guide =============== Migration guide between Sparkling Water versions. From 3.30 to 3.32 ----------------- - In PySparkling, the ``H2OConf`` no longer accepts any arguments. To create new ``H2OConf``, please just call ``conf = H2OConf()``. Also the ``H2OContext.getOrCreate`` method no longer accepts the spark argument. You can start H2OContext as ``H2OContext.getOrCreate()`` or ``H2OContext.getOrCreate(conf)`` - In RSparkling, the ``H2OConf`` no longer accepts any arguments. To create new ``H2OConf``, please just call ``conf <- H2OConf()``. Also the ``H2OContext.getOrCreate`` method no longer accepts the spark argument. You can start H2OContext as ``H2OContext.getOrCreate()`` or ``H2OContext.getOrCreate(conf)`` - In Scala, ``H2OConf`` can be created as ``new H2OConf()`` or ``new H2OConf(sparkConf)``. Other constructor variants have been removed. Also, ``H2OContext`` can be created as ``H2OContext.getOrCreate()`` or ``H2OContext.getOrCreate(conf)``. The other variants of this method have been removed. - The ``setH2OCluster(ip, port)`` method on ``H2OConf`` in all APIs doesn't implicitly set the external backend anymore. The method ``setExternalClusterMode()`` must be called explicitly. - The method ``classify`` in the ``hex.ModelUtils`` object is removed. Please use Sparkling Water algorithm API to train and score H2O models. This removal affects only Scala API as other APIs don't have such functionality. - The method ``DLModel`` in ``water.support.DeepLearningSupport`` is removed. Please use ``H2ODeepLearning`` instead. The same holds for method ``GBMModel`` in ``water.support.GBMSupport``. Please use ``H2OGBM`` instead. The classes wrapping these methods are removed as well. This removal affects only Scala API as other APIs don't have such functionality. - The method ``splitFrame`` and ``split`` in ``water.support.H2OFrameSupport`` is removed. Please use ``ai.h2o.sparkling.H2OFrame(frameKeyString).split(ratios)`` instead. - The method ``withLockAndUpdate`` in ``water.support.H2OFrameSupport`` is removed. Please use ``ai.h2o.sparkling.backend.utils.H2OClientUtils.withLockAndUpdate`` instead. - The method ``allStringVecToCategorical`` and ``split`` in ``water.support.H2OFrameSupport`` is removed. Please use ``ai.h2o.sparkling.H2OFrame(frameKeyString).convertAllStringColumnsToCategorical`` instead. - The methods ``columnsToCategorical`` with both indices and column names argument in ``water.support.H2OFrameSupport`` are removed. Please use ``ai.h2o.sparkling.H2OFrame(frameKeyString).convertColumnsToCategorical`` instead. - Method ``modelMetrics`` in ``water.support.ModelMetricsSupport`` is removed. Please use methods ``getTrainingMetrics``, ``getValidationMetrics`` or ``getCrossValidationMetrics`` on the ``H2OMOJOModel``. You can also use method ``getCurrentMetrics``, which returns cross validation metrics if nfolds was specified and higher than 0, validation metrics if validation frame has been specified ( splitRatio was set and lower than 1 ) and nfolds was 0 and training metrics otherwise ( splitRatio is 1 and nfolds is 0). - The whole trait ``ModelSerializationSupport`` in Scala is removed. The MOJO is a first class citizen in Sparkling Water and most code works with our Spark MOJO wrapper. Please use the following approaches to migrate from previous methods in the model serialization support: To create Spark MOJO wrapper in Sparkling Water, you can load it from H2O-3 as: .. code-block:: scala val mojoModel = H2OMOJOModel.createFromMojo(path) or train model using Sparkling Water API, such as .. code-block:: scala val gbm = H2OGBM().setLabelCol("label") val mojoModel = gbm.fit(data) In this case the ``mojoModel`` is Spark wrapper around the H2O's mojo providing Spark friendly API. This also means that the such model can be embedded into Spark pipelines without any additional work. To export it as, please call: .. code-block:: scala mojoModel.write.save("path") The advantage is that this variant is H2O-version independent and when such model is loaded, H2O run-time is not required. You can load the exported model from Sparkling Water as: .. code-block:: scala val mojoModel = H2OMOJOModel.read.load("path") For additional information about how to load MOJO into Sparkling Water, please see `Loading MOJOs into Sparkling Water `_. - The methods ``join``, ``innerJoin``, ``outerJoin``, ``leftJoin`` and ``rightJoin`` in ``water.support.JoinSupport`` are removed together with their encapsulating class. The enum ``water.support.munging.JoinMethod`` is also removed. In order to perform joins, please use the following methods: - Inner join: ``ai.h2o.sparkling.H2OFrame(idOfLeftFrame).innerJoin(rightFrame)`` - Outer join: ``ai.h2o.sparkling.H2OFrame(idOfLeftFrame).outerJoin(rightFrame)`` - Left join: ``ai.h2o.sparkling.H2OFrame(idOfLeftFrame).leftJoin(rightFrame)`` - Right join: ``ai.h2o.sparkling.H2OFrame(idOfLeftFrame).rightJoin(rightFrame)`` The ``JoinMethod`` enum is removed as it is no longer required. - Since the method ``asH2OFrame`` of ``H2OContext`` converts strings to categorical columns automatically according to the heuristic from H2O parsers, the methods ``getAllStringColumnsToCategorical`` and ``setAllStringColumnsToCategorical`` have been removed from all SW API algorithms in Python and Scala API. - Methods ``setH2ONodeLogLevel`` and ``setH2OClientLogLevel`` are removed on ``H2OContext``. Please use ``setH2OLogLevel`` instead. - Methods ``asDataFrame`` on Scala ``H2OContext`` has been replaced by methods ``asSparkFrame`` with same arguments. This was done to ensure full consistency between Scala, Python and R APIs. - JavaH2OContext is removed. Please use ``org.apache.spark.h2o.H2OContext`` instead. - When using H2O as Spark data source, the approach ``val df = spark.read.h2o(key)`` has been removed. Please use ``val df = spark.read.format("h2o").load(key)`` instead. The same holds for ``spark.write.h2o(key)``. Please use ``df.write.format("h2o").save("new_key")`` instead. From 3.28.1 to 3.30 ------------------- - It is now required to explicitly create ``H2OContext`` before you run any of our exposed algorithms. Previously, the algorithm would create the H2OContext on demand. - It is no longer possible to disable web (REST API endpoints) on the worker nodes in the internal client as we require the endpoints to be available. In particular, the methods ``setH2ONodeWebEnabled``, ``setH2ONodeWebDisabled`` and ``h2oNodeWebEnabled`` are removed without replacement. Also the option ``spark.ext.h2o.node.enable.web`` does not have any effect anymore. - It is no longer possible to disable web (REST API endpoints) on the client node as we require the Rest API to be available. In particular, the methods ``setClientWebEnabled``, ``setClientWebDisabled`` and ``clientWebEnabled`` are removed without replacement. Also the option ``spark.ext.h2o.client.enable.web`` does not have any effect anymore. Removal of Deprecated Methods and Classes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - On PySparkling, passing authentication on ``H2OContext`` via ``auth`` param is removed in favor of methods ``setUserName`` and ``setPassword`` ond the ``H2OConf`` or via the Spark options ``spark.ext.h2o.user.name`` and ``spark.ext.h2o.password`` directly. - On Pysparkling, passing ``verify_ssl_certificates`` parameter as H2OContext argument is removed in favor of method ``setVerifySslCertificates`` on ``H2OConf`` or via the spark option ``spark.ext.h2o.verify_ssl_certificates``. - On RSparkling, the method ``h2o_context`` is removed. To create H2OContext, please call ``hc <- H2OContext.getOrCreate()``. Also the methods ``h2o_flow``, ``as_h2o_frame`` and ``as_spark_dataframe`` are removed. Please use the methods available on the ``H2OContext`` instance created via ``hc <- H2OContext.getOrCreate()``. Instead of ``h2o_flow``, use ``hc$openFlow``, instead of ``as_h2o_frame``, use ``asH2OFrame`` and instead of ``as_spark_dataframe`` use ``asSparkFrame``. Also the ``H2OContext.getOrCreate()`` does not have ``username`` and ``password`` arguments anymore. The correct way how to pass authentication details to ``H2OContext`` is via ``H2OConf`` class, such as: .. code-block:: r conf <- H2OConf() conf$setUserName(username) conf$setPassword(password) hc <- H2OContext.getOrCreate(conf) The Spark options ``spark.ext.h2o.user.name`` and ``spark.ext.h2o.password`` correspond to these setters and can be also used directly. - In ``H2OContext`` Python API, the method ``as_spark_frame`` is replaced by the method ``asSparkFrame`` and the method ``as_h2o_frame`` is replaced by ``asH2OFrame``. - In ``H2OXGBoost`` Scala And Python API, the methods ``getNEstimators`` and ``setNEstimators`` are removed. Please use ``getNtrees`` and ``setNtrees`` instead. - In Scala and Python API for tree-based algorithms, the method ``getR2Stopping`` is removed in favor of ``getStoppingRounds``, ``getStoppingMetric``, ``getStoppingTolerance`` methods and the method ``setR2Stopping`` is removed in favor of ``setStoppingRounds``, ``setStoppingMetric``, ``setStoppingTolerance`` methods. - Method ``download_h2o_logs`` on PySparkling ``H2OContext`` is removed in favor of the ``downloadH2OLogs`` method. - Method ``get_conf`` on PySparkling ``H2OContext`` is removed in favor of the ``getConf`` method. - On Python and Scala ``H2OGLM`` API, the methods ``setExactLambdas`` and ``getExactLambdas`` are removed without replacement. - On H2OConf Python API, the following methods have been renamed to be consistent with the Scala counterparts: - ``h2o_cluster`` -> ``h2oCluster`` - ``h2o_cluster_host`` -> ``h2oClusterHost`` - ``h2o_cluster_port`` -> ``h2oClusterPort`` - ``cluster_size`` -> ``clusterSize`` - ``cluster_start_timeout`` -> ``clusterStartTimeout`` - ``cluster_config_file`` -> ``clusterInfoFile`` - ``mapper_xmx`` -> ``mapperXmx`` - ``hdfs_output_dir`` -> ``HDFSOutputDir`` - ``cluster_start_mode`` -> ``clusterStartMode`` - ``is_auto_cluster_start_used`` -> ``isAutoClusterStartUsed`` - ``is_manual_cluster_start_used`` -> ``isManualClusterStartUsed`` - ``h2o_driver_path`` -> ``h2oDriverPath`` - ``yarn_queue`` -> ``YARNQueue`` - ``is_kill_on_unhealthy_cluster_enabled`` -> ``isKillOnUnhealthyClusterEnabled`` - ``kerberos_principal`` -> ``kerberosPrincipal`` - ``kerberos_keytab`` -> ``kerberosKeytab`` - ``run_as_user`` -> ``runAsUser`` - ``set_h2o_cluster`` -> ``setH2OCluster`` - ``set_cluster_size`` -> ``setClusterSize`` - ``set_cluster_start_timeout`` -> ``setClusterStartTimeout`` - ``set_cluster_config_file`` -> ``setClusterInfoFile`` - ``set_mapper_xmx`` -> ``setMapperXmx`` - ``set_hdfs_output_dir`` -> ``setHDFSOutputDir`` - ``use_auto_cluster_start`` -> ``useAutoClusterStart`` - ``use_manual_cluster_start`` -> ``useManualClusterStart`` - ``set_h2o_driver_path`` -> ``setH2ODriverPath`` - ``set_yarn_queue`` -> ``setYARNQueue`` - ``set_kill_on_unhealthy_cluster_enabled`` -> ``setKillOnUnhealthyClusterEnabled`` - ``set_kill_on_unhealthy_cluster_disabled`` -> ``setKillOnUnhealthyClusterDisabled`` - ``set_kerberos_principal`` -> ``setKerberosPrincipal`` - ``set_kerberos_keytab`` -> ``setKerberosKeytab`` - ``set_run_as_user`` -> ``setRunAsUser`` - ``num_h2o_workers`` -> ``numH2OWorkers`` - ``drdd_mul_factor`` -> ``drddMulFactor`` - ``num_rdd_retries`` -> ``numRddRetries`` - ``default_cloud_size`` -> ``defaultCloudSize`` - ``subseq_tries`` -> ``subseqTries`` - ``h2o_node_web_enabled`` -> ``h2oNodeWebEnabled`` - ``node_iced_dir`` -> ``nodeIcedDir`` - ``set_num_h2o_workers`` -> ``setNumH2OWorkers`` - ``set_drdd_mul_factor`` -> ``setDrddMulFactor`` - ``set_num_rdd_retries`` -> ``setNumRddRetries`` - ``set_default_cloud_size`` -> ``setDefaultCloudSize`` - ``set_subseq_tries`` -> ``setSubseqTries`` - ``set_h2o_node_web_enabled`` -> ``setH2ONodeWebEnabled`` - ``set_h2o_node_web_disabled`` -> ``setH2ONodeWebDisabled`` - ``set_node_iced_dir`` -> ``setNodeIcedDir`` - ``backend_cluster_mode`` -> ``backendClusterMode`` - ``cloud_name`` -> ``cloudName`` - ``is_h2o_repl_enabled`` -> ``isH2OReplEnabled`` - ``scala_int_default_num`` -> ``scalaIntDefaultNum`` - ``is_cluster_topology_listener_enabled`` -> ``isClusterTopologyListenerEnabled`` - ``is_spark_version_check_enabled`` -> ``isSparkVersionCheckEnabled`` - ``is_fail_on_unsupported_spark_param_enabled`` -> ``isFailOnUnsupportedSparkParamEnabled`` - ``jks_pass`` -> ``jksPass`` - ``jks_alias`` -> ``jksAlias`` - ``hash_login`` -> ``hashLogin`` - ``ldap_login`` -> ``ldapLogin`` - ``kerberos_login`` -> ``kerberosLogin`` - ``login_conf`` -> ``loginConf`` - ``ssl_conf`` -> ``sslConf`` - ``auto_flow_ssl`` -> ``autoFlowSsl`` - ``h2o_node_log_level`` -> ``h2oNodeLogLevel`` - ``h2o_node_log_dir`` -> ``h2oNodeLogDir`` - ``cloud_timeout`` -> ``cloudTimeout`` - ``node_network_mask`` -> ``nodeNetworkMask`` - ``stacktrace_collector_interval`` -> ``stacktraceCollectorInterval`` - ``context_path`` -> ``contextPath`` - ``flow_scala_cell_async`` -> ``flowScalaCellAsync`` - ``max_parallel_scala_cell_jobs`` -> ``maxParallelScalaCellJobs`` - ``internal_port_offset`` -> ``internalPortOffset`` - ``mojo_destroy_timeout`` -> ``mojoDestroyTimeout`` - ``node_base_port`` -> ``nodeBasePort`` - ``node_extra_properties`` -> ``nodeExtraProperties`` - ``flow_extra_http_headers`` -> ``flowExtraHttpHeaders`` - ``is_internal_secure_connections_enabled`` -> ``isInternalSecureConnectionsEnabled`` - ``flow_dir`` -> ``flowDir`` - ``client_ip`` -> ``clientIp`` - ``client_iced_dir`` -> ``clientIcedDir`` - ``h2o_client_log_level`` -> ``h2oClientLogLevel`` - ``h2o_client_log_dir`` -> ``h2oClientLogDir`` - ``client_base_port`` -> ``clientBasePort`` - ``client_web_port`` -> ``clientWebPort`` - ``client_verbose_output`` -> ``clientVerboseOutput`` - ``client_network_mask`` -> ``clientNetworkMask`` - ``ignore_spark_public_dns`` -> ``ignoreSparkPublicDNS`` - ``client_web_enabled`` -> ``clientWebEnabled`` - ``client_flow_baseurl_override`` -> ``clientFlowBaseurlOverride`` - ``client_extra_properties`` -> ``clientExtraProperties`` - ``runs_in_external_cluster_mode`` -> ``runsInExternalClusterMode`` - ``runs_in_internal_cluster_mode`` -> ``runsInInternalClusterMode`` - ``client_check_retry_timeout`` -> ``clientCheckRetryTimeout`` - ``set_internal_cluster_mode`` -> ``setInternalClusterMode`` - ``set_external_cluster_mode`` -> ``setExternalClusterMode`` - ``set_cloud_name`` -> ``setCloudName`` - ``set_nthreads`` -> ``setNthreads`` - ``set_repl_enabled`` -> ``setReplEnabled`` - ``set_repl_disabled`` -> ``setReplDisabled`` - ``set_default_num_repl_sessions`` -> ``setDefaultNumReplSessions`` - ``set_cluster_topology_listener_enabled`` -> ``setClusterTopologyListenerEnabled`` - ``set_cluster_topology_listener_disabled`` -> ``setClusterTopologyListenerDisabled`` - ``set_spark_version_check_disabled`` -> ``setSparkVersionCheckDisabled`` - ``set_fail_on_unsupported_spark_param_enabled`` -> ``setFailOnUnsupportedSparkParamEnabled`` - ``set_fail_on_unsupported_spark_param_disabled`` -> ``setFailOnUnsupportedSparkParamDisabled`` - ``set_jks`` -> ``setJks`` - ``set_jks_pass`` -> ``setJksPass`` - ``set_jks_alias`` -> ``setJksAlias`` - ``set_hash_login_enabled`` -> ``setHashLoginEnabled`` - ``set_hash_login_disabled`` -> ``setHashLoginDisabled`` - ``set_ldap_login_enabled`` -> ``setLdapLoginEnabled`` - ``set_ldap_login_disabled`` -> ``setLdapLoginDisabled`` - ``set_kerberos_login_enabled`` -> ``setKerberosLoginEnabled`` - ``set_kerberos_login_disabled`` -> ``setKerberosLoginDisabled`` - ``set_login_conf`` -> ``setLoginConf`` - ``set_ssl_conf`` -> ``setSslConf`` - ``set_auto_flow_ssl_enabled`` -> ``setAutoFlowSslEnabled`` - ``set_auto_flow_ssl_disabled`` -> ``setAutoFlowSslDisabled`` - ``set_h2o_node_log_level`` -> ``setH2ONodeLogLevel`` - ``set_h2o_node_log_dir`` -> ``setH2ONodeLogDir`` - ``set_cloud_timeout`` -> ``setCloudTimeout`` - ``set_node_network_mask`` -> ``setNodeNetworkMask`` - ``set_stacktrace_collector_interval`` -> ``setStacktraceCollectorInterval`` - ``set_context_path`` -> ``setContextPath`` - ``set_flow_scala_cell_async_enabled`` -> ``setFlowScalaCellAsyncEnabled`` - ``set_flow_scala_cell_async_disabled`` -> ``setFlowScalaCellAsyncDisabled`` - ``set_max_parallel_scala_cell_jobs`` -> ``setMaxParallelScalaCellJobs`` - ``set_internal_port_offset`` -> ``setInternalPortOffset`` - ``set_node_base_port`` -> ``setNodeBasePort`` - ``set_mojo_destroy_timeout`` -> ``setMojoDestroyTimeout`` - ``set_node_extra_properties`` -> ``setNodeExtraProperties`` - ``set_flow_extra_http_headers`` -> ``setFlowExtraHttpHeaders`` - ``set_internal_secure_connections_enabled`` -> ``setInternalSecureConnectionsEnabled`` - ``set_internal_secure_connections_disabled`` -> ``setInternalSecureConnectionsDisabled`` - ``set_flow_dir`` -> ``setFlowDir`` - ``set_client_ip`` -> ``setClientIp`` - ``set_client_iced_dir`` -> ``setClientIcedDir`` - ``set_h2o_client_log_level`` -> ``setH2OClientLogLevel`` - ``set_h2o_client_log_dir`` -> ``setH2OClientLogDir`` - ``set_client_port_base`` -> ``setClientBasePort`` - ``set_client_web_port`` -> ``setClientWebPort`` - ``set_client_verbose_enabled`` -> ``setClientVerboseEnabled`` - ``set_client_verbose_disabled`` -> ``setClientVerboseDisabled`` - ``set_client_network_mask`` -> ``setClientNetworkMask`` - ``set_ignore_spark_public_dns_enabled`` -> ``setIgnoreSparkPublicDNSEnabled`` - ``set_ignore_spark_public_dns_disabled`` -> ``setIgnoreSparkPublicDNSDisabled`` - ``set_client_web_enabled`` -> ``setClientWebEnabled`` - ``set_client_web_disabled`` -> ``setClientWebDisabled`` - ``set_client_flow_baseurl_override`` -> ``setClientFlowBaseurlOverride`` - ``set_client_check_retry_timeout`` -> ``setClientCheckRetryTimeout`` - ``set_client_extra_properties`` -> ``setClientExtraProperties`` - In ``H2OAutoML`` Python and Scala API, the member ``leaderboard()``/``leaderboard`` is replaced by the method ``getLeaderboard()``. - The method ``setClusterConfigFile`` was removed from ``H2OConf`` in Scala API. The replacement method is ``setClusterInfoFile`` on ``H2OConf``. - The method ``setClientPortBase`` was removed from ``H2OConf`` in Scala API. The replacement method is ``setClientBasePort`` on ``H2OConf``. - In ``H2OGridSearch`` Python API, the methods: ``get_grid_models``, ``get_grid_models_params`` and `` get_grid_models_metrics`` are removed and replaced by ``getGridModels``, ``getGridModelsParams`` and `` getGridModelsMetrics``. - On ``H2OXGboost`` Scala and Python API, the methods ``getInitialScoreIntervals``, ``setInitialScoreIntervals``, ``getScoreInterval`` and ``setScoreInterval`` are removed without replacement. They correspond to an internal H2O argument which should not be exposed. - On ``H2OXGboost`` Scala and Python API, the methods ``getLearnRateAnnealing`` and ``setLearnRateAnnealing`` are removed without replacement as this parameter is currently not exposed in H2O. - The methods ``ignoreSparkPublicDNS``, ``setIgnoreSparkPublicDNSEnabled`` and ``setIgnoreSparkPublicDNSDisabled`` are removed without replacement as they are no longer required. Also the option ``spark.ext.h2o.client.ignore.SPARK_PUBLIC_DNS`` does not have any effect anymore. From 3.28.0 to 3.28.1 --------------------- - On ``H2OConf`` Python API, the methods ``external_write_confirmation_timeout`` and ``set_external_write_confirmation_timeout`` are removed without replacement. On ``H2OConf`` Scala API, the methods ``externalWriteConfirmationTimeout`` and ``setExternalWriteConfirmationTimeout`` are removed without replacement. Also the option ``spark.ext.h2o.external.write.confirmation.timeout`` does not have any effect anymore. - The environment variable ``H2O_EXTENDED_JAR`` specifying path to an extended driver jar was entirely replaced with ``H2O_DRIVER_JAR``. The ``H2O_DRIVER_JAR`` should contain a path to a plain H2O driver jar without any extensions. For more details, see :ref:`external-backend`. - The location of Sparkling Water assembly JAR has changed inside the Sparkling Water distribution archive which you can download from our `download page `_. It has been moved from ``assembly/build/libs`` to just ``jars``. - ``H2OSVM`` has been removed from the Scala API. We have removed this API as it was just wrapping Spark SVM and complicated the future development. If you still need to use ``SVM``, please use `Spark SVM `__ directly. All the parameters remain the same. We are planning to expose proper H2O's SVM implementation in Sparkling Water in the following major releases. - In case of binomial predictions on H2O MOJOs, the fields ``p0`` and ``p1`` in the detailed prediction column are replaced by a single field ``probabilities`` which is a map from label to predicted probability. The same is done for the fields ``p0_calibrated`` and ``p1_calibrated``. These fields are replaced by a single field ``calibratedProbabilities`` which is a map from label to predicted calibrated probability. - In case of multinomial predictions on H2O MOJOs, the type of field ``probabilities`` in the detailed prediction column is changed from array of probabilities to a map from label to predicted probability. - In case of ordinal predictions on H2O MOJOs, the type of field ``probabilities`` in the detailed prediction column is changed from array of probabilities to a map from label to predicted probability. - On ``H2OConf`` in all clients, the methods ``externalCommunicationBlockSizeAsBytes``, ``externalCommunicationBlockSize`` and ``setExternalCommunicationBlockSize`` have been removed as they are no longer needed. - Method ``Security.enableSSL`` in Scala API has been removed. Please use ``setInternalSecureConnectionsEnabled`` on H2OConf to secure your cluster. This setter is available on Scala, Python and R clients. - For the users of the manual backend we have simplified the configuration and there is no need to specify a cluster size anymore in advance. Sparkling Water automatically discovers the cluster size. In particular ``spark.ext.h2o.external.cluster.size`` does not have any effect anymore. From 3.26 To 3.28.0 ------------------- Passing Authentication in Scala ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The users of Scala who set up any form of authentication on the backend side are now required to specify credentials on the ``H2OConf`` object via ``setUserName`` and ``setPassword``. It is also possible to specify these directly as Spark options ``spark.ext.h2o.user.name`` and ``spark.ext.h2o.password``. Note: Actually only users of external backend need to specify these options at this moment as the external backend is using communication via REST api but all our documentation is using these options already as the internal backend will start using the REST api soon as well. String instead of enums in Sparkling Water Algo API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - In scala, setters of the pipeline wrappers for H2O algorithms now accepts strings in places where they accepted enum values before. Before, we called, for example: .. code-block:: scala import hex.genmodel.utils.DistributionFamily val gbm = H2OGBM() gbm.setDistribution(DistributionFamily.multinomial) Now, the correct code is: .. code-block:: scala val gbm = H2OGBM() gbm.setDistribution("multinomial") which makes the Python and Scala APIs consistent. Both upper case and lower case values are valid and if a wrong input is entered, warning is printed out with correct possible values. Switch to Java 1.8 on Spark 2.1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Sparkling Water for Spark 2.1 now requires Java 1.8 and higher. DRF exposed into Sparkling Water Algorithm API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DRF is now exposed in the Sparkling Water. Please see our documentation to learn how to use it :ref:`drf`. Also we can run our Grid Search API on DRF. Change Default Name of Prediction Column ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The default name of the prediction column has been changed from ``prediction_output`` to ``prediction``. Single value in prediction column ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The prediction column contains directly the predicted value. For example, before this change, the prediction column contained another struct field called ``value`` (in case of regression issue), which contained the value. From now on, the predicted value is always stored directly in the prediction column. In case of regression issue, the predicted numeric value and in case of classification, the predicted label. If you are interested in more details created during the prediction, please make sure to set ``withDetailedPredictionCol`` to ``true`` via the setters on both PySparkling and Sparkling Water. When enabled, additional column named ``detailed_prediction`` is created which contains additional prediction details, such as probabilities, contributions and so on. In manual mode of external backend always require a specification of cluster location ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In previous versions, H2O client was able to discover nodes using the multicast search. That is now removed and IP:Port of any node of external cluster to which we need to connect is required. This also means that in the users of multicast cloud up in case of external H2O backend in manual standalone (no Hadoop) mode now need to pass the flatfile argument external H2O. For more information, please see :ref:`external-backend-manual-standalone`. Removal of Deprecated Methods and Classes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - ``getColsampleBytree`` and ``setColsampleBytree`` methods are removed from the XGBoost API. Please use the new ``getColSampleByTree`` and ``setColSampleByTree``. - Removal of deprecated option ``spark.ext.h2o.external.cluster.num.h2o.nodes`` and corresponding setters. Please use ``spark.ext.h2o.external.cluster.size`` or the corresponding setter ``setClusterSize``. - Removal of deprecated algorithm classes in package ``org.apache.spark.h2o.ml.algos``. Please use the classes from the package ``ai.h2o.sparkling.ml.algos``. Their API remains the same as before. This is the beginning of moving Sparkling Water classes to our distinct package ``ai.h2o.sparkling`` - Removal of deprecated option ``spark.ext.h2o.external.read.confirmation.timeout`` and related setters. This option is removed without a replacement as it is no longer needed. - Removal of deprecated parameter ``SelectBestModelDecreasing`` on the Grid Search API. Related getters and setters have been also removed. This method is removed without replacement as we now internally sort the models with the ordering meaningful to the specified sort metric. - TargetEncoder transformer now accepts the ``outputCols`` parameter which can be used to override the default output column names. - On PySparkling ``H2OGLM`` API, we removed deprecated parameter ``alpha`` in favor of ``alphaValue`` and ``lambda_`` in favor of ``lambdaValue``. On Both PySparkling and Sparkling Water ``H2OGLM`` API, we removed methods ``getAlpha`` in favor of ``getAlphaValue``, ``getLambda`` in favor of ``getLambdaValue``, ``setAlpha`` in favor of ``setAlphaValue`` and ``setLambda`` in favor of ``setLambdaValue``. These changes ensure the consistency across Python and Scala APIs. - In Sparkling Water ``H2OConf`` API, we removed method ``h2oDriverIf`` in favor of ``externalH2ODriverIf`` and ``setH2ODriverIf`` in favor of ``setExternalH2ODriverIf``. In PySparkling ``H2OConf`` API, we removed method ``h2o_driver_if`` in favor of ``externalH2ODriverIf`` and ``set_h2o_driver_if`` in favor of ``setExternalH2ODriverIf``. - On PySparkling ``H2OConf`` API, the method ``user_name`` has been removed in favor of the ``userName`` method and method ``set_user_name`` had been removed in favor of the ``setUserName`` method. - The configurations ``spark.ext.h2o.external.kill.on.unhealthy.interval``, ``spark.ext.h2o.external.health.check.interval`` and ``spark.ext.h2o.ui.update.interval`` have been removed and were replaced by a single option ``spark.ext.h2o.backend.heartbeat.interval``. On ``H2OConf`` Scala API, the methods ``backendHeartbeatInterval`` and ``setBackendHeartbeatInterval`` were added and the following methods were removed: ``uiUpdateInterval``, ``setUiUpdateInterval``, ``killOnUnhealthyClusterInterval``, ``setKillOnUnhealthyClusterInterval``, ``healthCheckInterval`` and ``setHealthCheckInterval``. On ``H2OConf`` Python API, the methods ``backendHeartbeatInterval`` and ``setBackendHeartbeatInterval`` were added and the following methods were removed: ``ui_update_interval``, ``set_ui_update_interval``, ``kill_on_unhealthy_cluster_interval``, ``set_kill_on_unhealthy_cluster_interval``, ``get_health_check_interval`` and ``set_health_check_interval``. The added methods are used to configure single interval which was previously specified by these 3 different methods. - The configuration ``spark.ext.h2o.cluster.client.connect.timeout`` is removed without replacement as it is no longer needed. on ``H2OConf`` Scala API, the methods ``clientConnectionTimeout`` and ``setClientConnectionTimeout`` were removed and on ``H2OConf`` Python API, the methods ``set_client_connection_timeout`` and ``set_client_connection_timeout`` were removed. Change of Versioning Scheme ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Version of Sparkling Water is changed to the following pattern: ``H2OVersion-SWPatchVersion-SparkVersion``, where: ``H2OVersion`` is full H2O Version which is integrated to Sparkling Water. ``SWPatchVersion`` is used to specify a patch version and ``SparkVersion`` is a Spark version. This change of scheme allows us to do releases of Sparkling Water without the need of releasing H2O if there is only change on the Sparkling Water side. In that case, we just increment the ``SWPatchVersion``. The new version therefore looks, for example, like ``3.26.0.9-2-2.4``. This version tells us this Sparkling Water is integrating H2O ``3.26.0.9``, it is the second release with ``3.26.0.9`` version and is for Spark ``2.4``. Renamed Property for Passing Extra HTTP Headers for Flow UI ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The configuration property ``spark.ext.h2o.client.flow.extra.http.headers`` was renamed to to ``spark.ext.h2o.flow.extra.http.headers`` since Flow UI can also run on H2O nodes and the value of the property is also propagated to H2O nodes since the major version ``3.28.0.1-1``. External Backend now keeps H2O Flow accessible on worker nodes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The option ``spark.ext.h2o.node.enable.web`` does not have any effect anymore for automatic mode of external backend as we required H2O Flow to be accessible on the worker nodes. The associated getters and setters do also not have any effect in this case. It is also required that the users of manual mode of external backend keep REST api available on all worker nodes. In particular, the H2O option ``-disable_web`` can't be specified when starting H2O. Default Values of Some AutoML Parameters Have Changed ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The default values of the following AutoML parameters have changed across all APIs. +------------------------------------+------------+---------------------+ | Parameter Name | Old Value | New Value | +====================================+============+=====================+ | ``maxRuntimeSecs`` | ``3600.0`` | ``0.0`` (unlimited) | +------------------------------------+------------+---------------------+ | ``keepCrossValidationPredictions`` | ``true`` | ``false`` | +------------------------------------+------------+---------------------+ | ``keepCrossValidationModels`` | ``true`` | ``false`` | +------------------------------------+------------+---------------------+ From any previous version to 3.26.11 ------------------------------------ - Users of Sparkling Water external cluster in manual mode on Hadoop need to update the command the external cluster is launched with. A new parameter ``-sw_ext_backend`` needs to be added to the h2odriver invocation.