remove references to ops from schedule docs

docs/content/concepts/automation/schedules/customizing-executing-timezones.mdx

@@ -35,7 +35,7 @@ This parameter accepts any [`tz` timezone](https://en.wikipedia.org/wiki/List_of
 
 ```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_timezone endbefore=end_timezone
 my_timezone_schedule = ScheduleDefinition(
-    job=my_job, cron_schedule="0 9 * * *", execution_timezone="America/Los_Angeles"
+    job=asset_job, cron_schedule="0 9 * * *", execution_timezone="America/Los_Angeles"
 )
 ```
 

docs/content/concepts/automation/schedules/examples.mdx

@@ -21,14 +21,14 @@ The following examples demonstrate how to define some basic schedules.
 <TabGroup>
   <TabItem name="Using ScheduleDefinition">
 
-This example demonstrates how to define a schedule using <PyObject object="ScheduleDefinition" /> that will run a job every day at midnight. While this example uses [op jobs](/concepts/ops-jobs-graphs/jobs) (<PyObject object="job" decorator />), the same approach will work with [asset jobs](/concepts/assets/asset-jobs) (<PyObject object="define_asset_job" />).
+This example demonstrates how to define a schedule using <PyObject object="ScheduleDefinition" /> that will run a job every day at midnight.
 
-```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_basic_schedule endbefore=end_basic_schedule
-@job
-def my_job(): ...
+```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_basic_asset_schedule endbefore=end_basic_asset_schedule
+from dagster import AssetSelection, ScheduleDefinition, define_asset_job
 
+asset_job = define_asset_job("asset_job", AssetSelection.groups("some_asset_group"))
 
-basic_schedule = ScheduleDefinition(job=my_job, cron_schedule="0 0 * * *")
+basic_schedule = ScheduleDefinition(job=asset_job, cron_schedule="0 0 * * *")
 ```
 
 <table
@@ -202,7 +202,7 @@ def basic_schedule(): ...
 This example demonstrates how to emit log messages from a schedule during its evaluation function. These logs will be visible in the UI when you inspect a tick in the schedule's tick history.
 
 ```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_schedule_logging endbefore=end_schedule_logging
-@schedule(job=my_job, cron_schedule="* * * * *")
+@schedule(job=asset_job, cron_schedule="* * * * *")
 def logs_then_skips(context):
     context.log.info("Logging from a schedule!")
     return SkipReason("Nothing to do")
@@ -276,6 +276,7 @@ from dagster import (
     job,
     RunRequest,
     RunConfig,
+    define_asset_job,
     Definitions,
 )
 from datetime import datetime
@@ -287,8 +288,9 @@ class DateFormatter(ConfigurableResource):
     def strftime(self, dt: datetime) -> str:
         return dt.strftime(self.format)
 
-@job
-def process_data(): ...
+process_data = define_asset_job(
+    "process_data",
+)
 
 @schedule(job=process_data, cron_schedule="* * * * *")
 def process_data_schedule(
@@ -387,14 +389,12 @@ defs = Definitions(
 This example demonstrates how to use run config to vary the behavior of a job based on its scheduled run time.
 
 ```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_run_config_schedule endbefore=end_run_config_schedule
-@op(config_schema={"scheduled_date": str})
-def configurable_op(context: OpExecutionContext):
+@asset(config_schema={"scheduled_date": str})
+def configurable_asset(context: OpExecutionContext):
     context.log.info(context.op_config["scheduled_date"])
 
 
-@job
-def configurable_job():
-    configurable_op()
+configurable_job = define_asset_job("configurable_job", [configurable_asset])
 
 
 @schedule(job=configurable_job, cron_schedule="0 0 * * *")
@@ -403,7 +403,9 @@ def configurable_job_schedule(context: ScheduleEvaluationContext):
     return RunRequest(
         run_key=None,
         run_config={
-            "ops": {"configurable_op": {"config": {"scheduled_date": scheduled_date}}}
+            "ops": {
+                "configurable_asset": {"config": {"scheduled_date": scheduled_date}}
+            }
         },
         tags={"date": scheduled_date},
     )
@@ -456,7 +458,7 @@ This example demonstrates how to customize the timezone a schedule executes in.
 
 ```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_timezone endbefore=end_timezone
 my_timezone_schedule = ScheduleDefinition(
-    job=my_job, cron_schedule="0 9 * * *", execution_timezone="America/Los_Angeles"
+    job=asset_job, cron_schedule="0 9 * * *", execution_timezone="America/Los_Angeles"
 )
 ```
 
@@ -542,7 +544,7 @@ def daily_asset(): ...
 partitioned_asset_job = define_asset_job("partitioned_job", selection=[daily_asset])
 
 
-asset_partitioned_schedule = build_schedule_from_partitioned_job(
+partitioned_asset_schedule = build_schedule_from_partitioned_job(
     partitioned_asset_job,
 )
 ```
@@ -616,16 +618,7 @@ asset_partitioned_schedule = build_schedule_from_partitioned_job(
 This example demonstrates how to construct a schedule for a time-partitioned op job using <PyObject object="build_schedule_from_partitioned_job"/>.
 
 ```python file=/concepts/partitions_schedules_sensors/schedule_from_partitions.py startafter=start_marker endbefore=end_marker
-from dagster import build_schedule_from_partitioned_job, job
-
-
-@job(config=partitioned_config)
-def partitioned_op_job(): ...
-
-
-partitioned_op_schedule = build_schedule_from_partitioned_job(
-    partitioned_op_job,
-)
+No match for startAfter value "start_marker"
 ```
 
 <table
@@ -693,7 +686,7 @@ from dagster import schedule, RunRequest
 @schedule(cron_schedule="0 0 * * *", job=continent_job)
 def continent_schedule():
     for c in CONTINENTS:
-        yield RunRequest(run_key=c, partition_key=c)
+        yield RunRequest(partition_key=c)
 ```
 
 <table

docs/content/concepts/automation/schedules/partitioned-schedules.mdx

@@ -29,15 +29,6 @@ To follow this guide, you need to be familiar with:
 
 For jobs partitioned by time, you can use the <PyObject object="build_schedule_from_partitioned_job"/> to construct a schedule for the job. The schedule's interval will match the spacing of the partitions in the job. For example, if you have a daily partitioned job that fills in a date partition of a table each time it runs, you likely want to run that job every day.
 
-Refer to the following tabs for examples of asset and op-based jobs using <PyObject object="build_schedule_from_partitioned_job"/> to construct schedules:
-
-<TabGroup>
-<TabItem name="Asset jobs">
-
-#### Asset jobs
-
-Asset jobs are defined using <PyObject object="define_asset_job" />. In this example, we created an asset job named `partitioned_job` and then constructed `asset_partitioned_schedule` by using <PyObject object="build_schedule_from_partitioned_job"/>:
-
 ```python file=/concepts/partitions_schedules_sensors/schedule_from_partitions.py startafter=start_partitioned_asset_schedule endbefore=end_partitioned_asset_schedule
 from dagster import (
     asset,
@@ -56,34 +47,11 @@ def daily_asset(): ...
 partitioned_asset_job = define_asset_job("partitioned_job", selection=[daily_asset])
 
 
-asset_partitioned_schedule = build_schedule_from_partitioned_job(
+partitioned_asset_schedule = build_schedule_from_partitioned_job(
     partitioned_asset_job,
 )
 ```
 
-</TabItem>
-<TabItem name="Op jobs">
-
-#### Op jobs
-
-Op jobs are defined using the <PyObject object="job" decorator />. In this example, we created a partitioned job named `partitioned_op_job` and then constructed `partitioned_op_schedule` using <PyObject object="build_schedule_from_partitioned_job"/>:
-
-```python file=/concepts/partitions_schedules_sensors/schedule_from_partitions.py startafter=start_marker endbefore=end_marker
-from dagster import build_schedule_from_partitioned_job, job
-
-
-@job(config=partitioned_config)
-def partitioned_op_job(): ...
-
-
-partitioned_op_schedule = build_schedule_from_partitioned_job(
-    partitioned_op_job,
-)
-```
-
-</TabItem>
-</TabGroup>
-
 ### Customizing schedule timing
 
 The `minute_of_hour`, `hour_of_day`, `day_of_week`, and `day_of_month` parameters of `build_schedule_from_partitioned_job` can be used to control the timing of the schedule.

docs/content/concepts/automation/schedules/testing.mdx

@@ -52,14 +52,12 @@ To test a function decorated by the <PyObject object="schedule" decorator /> dec
 Let's say we want to test the `configurable_job_schedule` in this example:
 
 ```python file=concepts/partitions_schedules_sensors/schedules/schedules.py startafter=start_run_config_schedule endbefore=end_run_config_schedule
-@op(config_schema={"scheduled_date": str})
-def configurable_op(context: OpExecutionContext):
+@asset(config_schema={"scheduled_date": str})
+def configurable_asset(context: OpExecutionContext):
     context.log.info(context.op_config["scheduled_date"])
 
 
-@job
-def configurable_job():
-    configurable_op()
+configurable_job = define_asset_job("configurable_job", [configurable_asset])
 
 
 @schedule(job=configurable_job, cron_schedule="0 0 * * *")
@@ -68,7 +66,9 @@ def configurable_job_schedule(context: ScheduleEvaluationContext):
     return RunRequest(
         run_key=None,
         run_config={
-            "ops": {"configurable_op": {"config": {"scheduled_date": scheduled_date}}}
+            "ops": {
+                "configurable_asset": {"config": {"scheduled_date": scheduled_date}}
+            }
         },
         tags={"date": scheduled_date},
     )
@@ -104,6 +104,7 @@ from dagster import (
     job,
     RunRequest,
     RunConfig,
+    define_asset_job,
     Definitions,
 )
 from datetime import datetime
@@ -115,8 +116,9 @@ class DateFormatter(ConfigurableResource):
     def strftime(self, dt: datetime) -> str:
         return dt.strftime(self.format)
 
-@job
-def process_data(): ...
+process_data = define_asset_job(
+    "process_data",
+)
 
 @schedule(job=process_data, cron_schedule="* * * * *")
 def process_data_schedule(

docs/content/concepts/partitions-schedules-sensors/partitioning-ops.mdx

@@ -119,7 +119,16 @@ def partitioned_op_job():
 Not all jobs are partitioned by time. For example, the following example shows a partitioned job where the partitions are continents:
 
 ```python file=/concepts/partitions_schedules_sensors/static_partitioned_job.py
-from dagster import Config, OpExecutionContext, job, op, static_partitioned_config
+from dagster import (
+    AssetExecutionContext,
+    Config,
+    StaticPartitionsDefinition,
+    asset,
+    define_asset_job,
+    job,
+    op,
+    static_partitioned_config,
+)
 
 CONTINENTS = [
     "Africa",
@@ -132,23 +141,12 @@ CONTINENTS = [
 ]
 
 
-@static_partitioned_config(partition_keys=CONTINENTS)
-def continent_config(partition_key: str):
-    return {"ops": {"continent_op": {"config": {"continent_name": partition_key}}}}
+@asset(partitions_def=StaticPartitionsDefinition(CONTINENTS))
+def continents_info(context: AssetExecutionContext):
+    context.log.info(f"continent name: {context.partition_key}")
 
 
-class ContinentOpConfig(Config):
-    continent_name: str
-
-
-@op
-def continent_op(context: OpExecutionContext, config: ContinentOpConfig):
-    context.log.info(config.continent_name)
-
-
-@job(config=continent_config)
-def continent_job():
-    continent_op()
+continent_job = define_asset_job("continent_job", [continents_info])
 ```
 
 ---