Fix: spark roll back scheduling disable

aztk/models/__init__.py

@@ -1,22 +1,18 @@
+from .application_log import ApplicationLog
+from .cluster import Cluster
 from .cluster_configuration import ClusterConfiguration
 from .custom_script import CustomScript
-from .file_share import FileShare
-from .toolkit import TOOLKIT_MAP, Toolkit
-from .user_configuration import UserConfiguration
-from .secrets_configuration import (
-    SecretsConfiguration,
-    ServicePrincipalConfiguration,
-    SharedKeyConfiguration,
-    DockerConfiguration,
-)
 from .file import File
+from .file_share import FileShare
+from .node_output import NodeOutput
+from .plugins import *
+# from .scheduling_target import SchedulingTarget
+from .port_forward_specification import PortForwardingSpecification
 from .remote_login import RemoteLogin
+from .secrets_configuration import (DockerConfiguration, SecretsConfiguration, ServicePrincipalConfiguration,
+                                    SharedKeyConfiguration)
+from .software import Software
 from .ssh_log import SSHLog
+from .toolkit import TOOLKIT_MAP, Toolkit
+from .user_configuration import UserConfiguration
 from .vm_image import VmImage
-from .node_output import NodeOutput
-from .software import Software
-from .cluster import Cluster
-from .scheduling_target import SchedulingTarget
-from .port_forward_specification import PortForwardingSpecification
-from .application_log import ApplicationLog
-from .plugins import *

aztk/models/cluster_configuration.py

@@ -5,7 +5,7 @@
 from .custom_script import CustomScript
 from .file_share import FileShare
 from .plugins import PluginConfiguration
-from .scheduling_target import SchedulingTarget
+# from .scheduling_target import SchedulingTarget
 from .toolkit import Toolkit
 from .user_configuration import UserConfiguration
 
@@ -37,7 +37,8 @@ class ClusterConfiguration(Model):
     plugins = fields.List(PluginConfiguration)
     file_shares = fields.List(FileShare)
     user_configuration = fields.Model(UserConfiguration, default=None)
-    scheduling_target = fields.Enum(SchedulingTarget, default=None)
+
+    # scheduling_target = fields.Enum(SchedulingTarget, default=None)
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -73,5 +74,5 @@ def __validate__(self) -> bool:
                 "You must configure a VNET to use AZTK in mixed mode (dedicated and low priority nodes). "
                 "Set the VNET's subnet_id in your cluster.yaml or with a parameter (--subnet-id).")
 
-        if self.scheduling_target == SchedulingTarget.Dedicated and self.size == 0:
-            raise error.InvalidModelError("Scheduling target cannot be Dedicated if dedicated vm size is 0")
+        # if self.scheduling_target == SchedulingTarget.Dedicated and self.size == 0:
+        #     raise error.InvalidModelError("Scheduling target cannot be Dedicated if dedicated vm size is 0")

aztk/node_scripts/install/install.py

@@ -6,7 +6,7 @@
 from core import config
 from install import create_user, pick_master, plugins, spark, spark_container
 
-from .node_scheduling import setup_node_scheduling
+# from .node_scheduling import setup_node_scheduling
 
 
 def read_cluster_config():
@@ -48,7 +48,7 @@ def setup_host(docker_repo: str, docker_run_options: str):
 
     cluster_conf = read_cluster_config()
 
-    setup_node_scheduling(client, cluster_conf, is_master)
+    # setup_node_scheduling(client, cluster_conf, is_master)
 
     # TODO pass azure file shares
     spark_container.start_spark_container(

aztk/node_scripts/install/node_scheduling.py

@@ -1,7 +1,11 @@
 from azure import batch
-from aztk.models import ClusterConfiguration, SchedulingTarget
+
+from aztk.models import ClusterConfiguration
 from core import config, log
 
+# from aztk.models import SchedulingTarget
+SchedulingTarget = "SchedulingTarget"    # this code isn't used anywhere until scheduling_target reenabled
+
 
 def disable_scheduling(batch_client: batch.BatchServiceClient):
     """

aztk/spark/client/cluster/helpers/create.py

@@ -12,19 +12,18 @@
     auto_user=batch_models.AutoUserSpecification(
         scope=batch_models.AutoUserScope.pool, elevation_level=batch_models.ElevationLevel.admin))
 
-
-def _default_scheduling_target(vm_count: int):
-    if vm_count == 0:
-        return models.SchedulingTarget.Any
-    else:
-        return models.SchedulingTarget.Dedicated
+# def _default_scheduling_target(vm_count: int):
+#     if vm_count == 0:
+#         return models.SchedulingTarget.Any
+#     else:
+#         return models.SchedulingTarget.Dedicated
 
 
 def _apply_default_for_cluster_config(configuration: models.ClusterConfiguration):
     cluster_conf = models.ClusterConfiguration()
     cluster_conf.merge(configuration)
-    if cluster_conf.scheduling_target is None:
-        cluster_conf.scheduling_target = _default_scheduling_target(cluster_conf.size)
+    # if cluster_conf.scheduling_target is None:
+    #     cluster_conf.scheduling_target = _default_scheduling_target(cluster_conf.size)
     return cluster_conf
 
 

aztk/spark/client/job/helpers/submit.py

@@ -53,16 +53,16 @@ def generate_job_manager_task(core_job_operations, job, application_tasks):
     return task
 
 
-def _default_scheduling_target(vm_count: int):
-    if vm_count == 0:
-        return models.SchedulingTarget.Any
-    else:
-        return models.SchedulingTarget.Dedicated
+# def _default_scheduling_target(vm_count: int):
+#     if vm_count == 0:
+#         return models.SchedulingTarget.Any
+#     else:
+#         return models.SchedulingTarget.Dedicated
 
 
 def _apply_default_for_job_config(job_conf: models.JobConfiguration):
-    if job_conf.scheduling_target is None:
-        job_conf.scheduling_target = _default_scheduling_target(job_conf.max_dedicated_nodes)
+    # if job_conf.scheduling_target is None:
+    #     job_conf.scheduling_target = _default_scheduling_target(job_conf.max_dedicated_nodes)
 
     return job_conf
 

aztk/spark/models/models.py

@@ -103,7 +103,7 @@ class PluginConfiguration(aztk.models.PluginConfiguration):
     pass
 
 
-SchedulingTarget = aztk.models.SchedulingTarget
+# SchedulingTarget = aztk.models.SchedulingTarget
 
 
 class ClusterConfiguration(aztk.models.ClusterConfiguration):
@@ -198,7 +198,7 @@ def __init__(
             max_dedicated_nodes=0,
             max_low_pri_nodes=0,
             subnet_id=None,
-            scheduling_target: SchedulingTarget = None,
+    # scheduling_target: SchedulingTarget = None,
             worker_on_master=None,
     ):
 
@@ -214,7 +214,7 @@ def __init__(
         self.max_low_pri_nodes = max_low_pri_nodes
         self.subnet_id = subnet_id
         self.worker_on_master = worker_on_master
-        self.scheduling_target = scheduling_target
+        # self.scheduling_target = scheduling_target
 
     def to_cluster_config(self):
         return ClusterConfiguration(
@@ -226,7 +226,7 @@ def to_cluster_config(self):
             subnet_id=self.subnet_id,
             worker_on_master=self.worker_on_master,
             spark_configuration=self.spark_configuration,
-            scheduling_target=self.scheduling_target,
+        # scheduling_target=self.scheduling_target,
         )
 
     def mixed_mode(self) -> bool:
@@ -263,8 +263,8 @@ def validate(self) -> bool:
                 "You must configure a VNET to use AZTK in mixed mode (dedicated and low priority nodes) "
                 "and pass the subnet_id in your configuration..")
 
-        if self.scheduling_target == SchedulingTarget.Dedicated and self.max_dedicated_nodes == 0:
-            raise error.InvalidModelError("Scheduling target cannot be Dedicated if dedicated vm size is 0")
+        # if self.scheduling_target == SchedulingTarget.Dedicated and self.max_dedicated_nodes == 0:
+        #     raise error.InvalidModelError("Scheduling target cannot be Dedicated if dedicated vm size is 0")
 
 
 class JobState:

aztk_cli/config.py

@@ -5,7 +5,9 @@
 import aztk.spark
 from aztk.models import Toolkit
 from aztk.models.plugins.internal import PluginReference
-from aztk.spark.models import (ClusterConfiguration, SchedulingTarget, SecretsConfiguration)
+from aztk.spark.models import ClusterConfiguration, SecretsConfiguration
+
+# from aztk.spark.models import SchedulingTarget
 
 
 def load_aztk_secrets() -> SecretsConfiguration:
@@ -179,7 +181,7 @@ def __init__(self):
         self.core_site_xml = None
         self.subnet_id = None
         self.worker_on_master = None
-        self.scheduling_target = None
+        # self.scheduling_target = None
         self.jars = []
 
     def _merge_dict(self, config):
@@ -198,9 +200,9 @@ def _merge_dict(self, config):
                 self.max_low_pri_nodes = cluster_configuration.get("size_low_priority")
             self.subnet_id = cluster_configuration.get("subnet_id")
             self.worker_on_master = cluster_configuration.get("worker_on_master")
-            scheduling_target = cluster_configuration.get("scheduling_target")
-            if scheduling_target:
-                self.scheduling_target = SchedulingTarget(scheduling_target)
+            # scheduling_target = cluster_configuration.get("scheduling_target")
+            # if scheduling_target:
+            #     self.scheduling_target = SchedulingTarget(scheduling_target)
 
         applications = config.get("applications")
         if applications:

aztk_cli/config/cluster.yaml

@@ -61,8 +61,6 @@ plugins:
 # Allow master node to also be a worker <true/false> (Default: true)
 # worker_on_master: true
 
-# Where do you want to run the driver <dedicated/master/any> (Default: dedicated if at least one dedicated node or any otherwise)
-# scheduling_target: dedicated
 
 # wait: <true/false>
 wait: false

aztk_cli/config/job.yaml

@@ -25,8 +25,6 @@ job:
           # Optional command line options to pass to `docker run`
           # docker_run_options: <additional command line options to pass to `docker run` (for more information, see https://github.com/Azure/aztk/blob/master/docs/12-docker-image.md)>
 
-        # Where do you want to run the driver <dedicated/master/any> (Default: dedicated if at least one dedicated node or any otherwise)
-        # scheduling_target: dedicated
 
     spark_configuration:
         spark_defaults_conf: .aztk/spark-defaults.conf

aztk_cli/spark/endpoints/job/submit.py

@@ -48,7 +48,7 @@ def execute(args: typing.NamedTuple):
         max_low_pri_nodes=job_conf.max_low_pri_nodes,
         subnet_id=job_conf.subnet_id,
         worker_on_master=job_conf.worker_on_master,
-        scheduling_target=job_conf.scheduling_target,
+    # scheduling_target=job_conf.scheduling_target,
     )
 
     # TODO: utils.print_job_conf(job_configuration)

docs/13-configuration.md

@@ -47,12 +47,14 @@ plugins:
 # Allow master node to also be a worker <true/false> (Default: true)
 # worker_on_master: true
 
-# Where do you want to run the driver <dedicated/master/any> (Default: dedicated if at least one dedicated node or any otherwise)
-# scheduling_target: dedicated
 
 # wait: <true/false>
 wait: true
 ```
+<!--- this goes about wait: true
+# Where do you want to run the driver <dedicated/master/any> (Default: dedicated if at least one dedicated node or any otherwise)
+# scheduling_target: dedicated
+-->
 
 Running `aztk spark cluster create` will create a cluster of 4 **Standard\_A2** nodes called 'spark\_cluster' with a linux user named 'spark'. This is equivalent to running the command
 

tests/models/test_cluster_configuration.py

@@ -1,7 +1,8 @@
 import pytest
 
 from aztk.error import InvalidModelError
-from aztk.models import (ClusterConfiguration, SchedulingTarget, Toolkit, UserConfiguration)
+from aztk.models import ClusterConfiguration, Toolkit, UserConfiguration
+# from aztk.models import SchedulingTarget
 from aztk.spark.models.plugins import HDFSPlugin, JupyterPlugin
 
 
@@ -60,15 +61,15 @@ def test_cluster_configuration():
     assert config.plugins[1].name == 'hdfs'
 
 
-def test_scheduling_target_dedicated_with_no_dedicated_nodes_raise_error():
-    with pytest.raises(InvalidModelError, match="Scheduling target cannot be Dedicated if dedicated vm size is 0"):
-        conf = ClusterConfiguration(
-            cluster_id="abc",
-            scheduling_target=SchedulingTarget.Dedicated,
-            vm_size="standard_a2",
-            size=0,
-            size_low_priority=2,
-            toolkit=Toolkit(software="spark", version="1.6.3"),
-        )
+# def test_scheduling_target_dedicated_with_no_dedicated_nodes_raise_error():
+#     with pytest.raises(InvalidModelError, match="Scheduling target cannot be Dedicated if dedicated vm size is 0"):
+#         conf = ClusterConfiguration(
+#             cluster_id="abc",
+#             scheduling_target=SchedulingTarget.Dedicated,
+#             vm_size="standard_a2",
+#             size=0,
+#             size_low_priority=2,
+#             toolkit=Toolkit(software="spark", version="1.6.3"),
+#         )
 
-        conf.validate()
+#         conf.validate()