Remove unused argument --target_platform (#1279)

Signed-off-by: Partho Sarthi <[email protected]>

user_tools/src/spark_rapids_pytools/cloud_api/sp_types.py

@@ -87,12 +87,6 @@ class ClusterState(EnumeratedType):
     UNKNOWN = 'unknown'
 
 
-class TargetPlatform(EnumeratedType):
-    """Determine CostSavings for target platform based on OnPrem cluster configuration"""
-    DATAPROC = 'dataproc'
-    NONE = None
-
-
 class SparkNodeType(EnumeratedType):
     """
     Node type from Spark perspective. We either have a master node or a worker node.

user_tools/src/spark_rapids_pytools/rapids/qualification.py

@@ -275,8 +275,6 @@ def _process_custom_args(self) -> None:
         cuda_arg = self.wrapper_options.get('cuda')
         if cuda_arg is not None:
             cuda = cuda_arg
-        target_platform = self.wrapper_options.get('targetPlatform')
-        self.ctxt.set_ctxt('targetPlatform', target_platform)
         self.ctxt.set_ctxt('gpuPerMachine', gpu_per_machine)
         self.ctxt.set_ctxt('gpuDevice', gpu_device)
         self.ctxt.set_ctxt('cuda', cuda)

user_tools/src/spark_rapids_tools/cmdli/argprocessor.py

@@ -446,15 +446,13 @@ class QualifyUserArgModel(ToolUserArgModel):
     Represents the arguments collected by the user to run the qualification tool.
     This is used as doing preliminary validation against some of the common pattern
     """
-    target_platform: Optional[CspEnv] = None
     filter_apps: Optional[QualFilterApp] = None
     gpu_cluster_recommendation: Optional[QualGpuClusterReshapeType] = None
     estimation_model_args: Optional[Dict] = dataclasses.field(default_factory=dict)
 
     def init_tool_args(self) -> None:
         self.p_args['toolArgs']['platform'] = self.platform
         self.p_args['toolArgs']['savingsCalculations'] = False
-        self.p_args['toolArgs']['targetPlatform'] = self.target_platform
         # check the filter_apps argument
         if self.filter_apps is None:
             self.p_args['toolArgs']['filterApps'] = QualFilterApp.get_default()
@@ -487,22 +485,6 @@ def build_tools_args(self) -> dict:
         # At this point, if the platform is still none, then we can set it to the default value
         # which is the onPrem platform.
         runtime_platform = self.get_or_set_platform()
-        # check the targetPlatform argument
-        if self.p_args['toolArgs']['targetPlatform']:
-            equivalent_pricing_list = runtime_platform.get_equivalent_pricing_platform()
-            if not equivalent_pricing_list:
-                # no target_platform for that runtime environment
-                self.logger.info(
-                    'Argument target_platform does not support the current cluster [%s]', runtime_platform)
-                self.p_args['toolArgs']['targetPlatform'] = None
-            else:
-                if not self.p_args['toolArgs']['targetPlatform'] in equivalent_pricing_list:
-                    target_platform = self.p_args['toolArgs']['targetPlatform']
-                    raise PydanticCustomError(
-                        'invalid_argument',
-                        f'The platform [{target_platform}] is currently '
-                        f'not supported to calculate savings from [{runtime_platform}] cluster\n  Error:')
-
         # process JVM arguments
         self.process_jvm_args()
 
@@ -512,9 +494,7 @@ def build_tools_args(self) -> dict:
             'outputFolder': self.output_folder,
             'platformOpts': {
                 'credentialFile': None,
-                'deployMode': DeployMode.LOCAL,
-                # used to be sent to the scala core java cmd
-                'targetPlatform': self.p_args['toolArgs']['targetPlatform']
+                'deployMode': DeployMode.LOCAL
             },
             'migrationClustersProps': {
                 'cpuCluster': self.cluster,

user_tools/src/spark_rapids_tools/cmdli/tools_cli.py

@@ -39,7 +39,6 @@ def qualification(self,
                       eventlogs: str = None,
                       cluster: str = None,
                       platform: str = None,
-                      target_platform: str = None,
                       output_folder: str = None,
                       filter_apps: str = None,
                       custom_model_file: str = None,
@@ -66,12 +65,6 @@ def qualification(self,
                Name or ID (for databricks platforms) of cluster or path to cluster-properties.
         :param platform: defines one of the following "onprem", "emr", "dataproc", "dataproc-gke",
                "databricks-aws", and "databricks-azure".
-        :param target_platform: Cost savings and speedup recommendation for comparable cluster in
-                target_platform based on on-premises cluster configuration.
-
-                Currently only `dataproc` is supported for target_platform.
-                If not provided, the final report will be limited to GPU speedups only without
-                cost-savings.
         :param output_folder: path to store the output
         :param tools_jar: Path to a bundled jar including Rapids tool. The path is a local filesystem,
                 or remote cloud storage url. If missing, the wrapper downloads the latest rapids-4-spark-tools_*.jar
@@ -97,7 +90,7 @@ def qualification(self,
                 https://docs.nvidia.com/spark-rapids/user-guide/latest/qualification/jar-usage.html#running-the-qualification-tool-standalone-on-spark-event-logs
         """
         platform = Utils.get_value_or_pop(platform, rapids_options, 'p')
-        target_platform = Utils.get_value_or_pop(target_platform, rapids_options, 't')
+        tools_jar = Utils.get_value_or_pop(tools_jar, rapids_options, 't')
         output_folder = Utils.get_value_or_pop(output_folder, rapids_options, 'o')
         filter_apps = Utils.get_value_or_pop(filter_apps, rapids_options, 'f')
         verbose = Utils.get_value_or_pop(verbose, rapids_options, 'v', False)
@@ -117,7 +110,6 @@ def qualification(self,
                                                          eventlogs=eventlogs,
                                                          cluster=cluster,
                                                          platform=platform,
-                                                         target_platform=target_platform,
                                                          output_folder=output_folder,
                                                          tools_jar=tools_jar,
                                                          jvm_heap_size=jvm_heap_size,