mlops-139/140 pydantic docs for pipelines and jobs

docs/tutorial/job.md

@@ -10,12 +10,12 @@ date: 2022-04-06
 # WANNA Job
 
 ::: wanna.core.models.training_custom_job.BaseCustomJobModel
-    options:
-      members: false
+    :docstring:
 
 ## Hyper-parameter tuning
 
 ::: wanna.core.models.training_custom_job.HyperparameterTuning
+    :docstring:
 
 A custom job can be simply converted to a hyper-parameter tuning job just by adding 
 one extra parameter called `hp_tuning`. This will start a series of jobs (instead of just one job) 

docs/tutorial/pipeline.md

@@ -193,6 +193,7 @@ You can now see that you have a self contained component and testable, ready to
 ## Pipeline
 
 ::: wanna.core.models.pipeline.PipelineModel
+    :docstring:
 
 WANNA template creates two files that allow to put together the Kubeflow V2 pipeline that will then be deployed to GCP Vertex AI Pipelines.
 

src/wanna/core/models/pipeline.py

@@ -8,6 +8,27 @@
 
 
 class PipelineModel(BaseInstanceModel):
+    """
+    - `name`- [str] Custom name for this instance
+    - `project_id' - [str] (optional) Overrides GCP Project ID from the `gcp_profile` segment
+    - `zone` - [str] (optional) Overrides zone from the `gcp_profile` segment
+    - `region` - [str] (optional) Overrides region from the `gcp_profile` segment
+    - `labels`- [Dict[str, str]] (optional) Custom labels to apply to this instance
+    - `service_account` - [str] (optional) Overrides service account from the `gcp_profile` segment
+    - `network` - [str] (optional) Overrides network from the `gcp_profile` segment
+    - `tags`- [Dict[str, str]] (optional) Tags to apply to this instance
+    - `metadata`- [str] (optional) Custom metadata to apply to this instance
+    - `pipeline_file` - [str] Path to a Kubeflow Python script
+    - `pipeline_function` - [str] (optional) Path to a cloud function
+    - `pipeline_params` - [str] (optional) Path to params.yaml file
+    - `docker_image_ref` - [List[str]] - List of names of docker images
+    - `schedule` - [str] (optional) - Scheduler using a cron syntax
+    - `tensorboard_ref` - [str] (optional) - Name of the Vertex AI Experiment
+    - `notification_channels_ref` - [List[str]] (optional) List of names of notificartins channel described
+    by a model: (name: str, type: Literal["email"], emails: List[EmailStr])
+    - `sla_hours` - [float] (optional) Time after which the running pipeline gets stopped
+    """
+
     name: str = Field(min_length=3, max_length=63, to_lower=True, regex="^[a-z][a-z0-9-]*[a-z0-9]$")
     zone: str
     pipeline_file: str

src/wanna/core/models/training_custom_job.py

@@ -86,6 +86,17 @@ class DiscreteParameter(BaseModel, extra=Extra.forbid):
 
 
 class HyperparameterTuning(BaseModel):
+    """
+    - `metrics` - Dictionary of type [str, Literal["minimize", "maximize"]]
+    - `parameters` - List[HyperParamater] defined per var_name, type, min, max, scale
+    - `max_trial_count` - [int] defaults to 15
+    - `parallel_trial_count` - [int] defaults to 3
+    - `search_algorithm` - [str] (optional) Can be "grid" or "random"
+    - `encryption_spec` - [str] (optional) The Cloud KMS resource identifier. Has the form:
+    projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key
+    The key needs to be in the same region as where the compute resource is created
+    """
+
     metrics: Dict[str, Literal["minimize", "maximize"]]
     parameters: List[HyperParamater]
     max_trial_count: int = 15
@@ -95,6 +106,27 @@ class HyperparameterTuning(BaseModel):
 
 
 class BaseCustomJobModel(BaseInstanceModel):
+    """
+    - `name` - [str] Custom name for this instance
+    - `project_id' - [str] (optional) Overrides GCP Project ID from the `gcp_profile` segment
+    - `zone` - [str] (optional) Overrides zone from the `gcp_profile` segment
+    - `region` - [str] (optional) Overrides region from the `gcp_profile` segment
+    - `labels`- [Dict[str, str]] (optional) Custom labels to apply to this instance
+    - `service_account` - [str] (optional) Overrides service account from the `gcp_profile` segment
+    - `network` - [str] (optional) Overrides network from the `gcp_profile` segment
+    - `tags`- [Dict[str, str]] (optional) Tags to apply to this instance
+    - `metadata`- [str] (optional) Custom metadata to apply to this instance
+    - `enable_web_access` - [bool] Whether you want Vertex AI to enable interactive shell access
+    to training containers. Default is False
+    - `bucket` - [str] Overrides bucket from the `gcp_profile` segment
+    - `base_output_directory` - [str] (optional) Path to where outputs will be saved
+    - `tensorboard_ref` - [str] (optional) Name of the Vertex AI Experiment
+    - `timeout_seconds` - [int] Job timeout. Defaults to 60 * 60 * 24 s = 24 hours
+    - `encryption_spec`- [str] (optional) The Cloud KMS resource identifier. Has the form:
+    projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key
+    The key needs to be in the same region as where the compute resource is created
+    """
+
     region: str
     enable_web_access: bool = False
     bucket: str