Index

Datatailr Scheduler Module

The datatailr.scheduler module provides a framework for scheduling and managing batch jobs.

The main job types are:

Batch: Represents a batch job that can be scheduled and executed. The job can include multiple tasks which can be run in parallel or sequentially.
Service: Represents a service job that runs continuously.
App: Represents a web app or a dashboard, which can be built using one of the supported frameworks, such as Streamlit <https://streamlit.io/>, Dash <https://dash.plotly.com/>, or Panel <https://panel.holoviz.org/>_.
Excel: Represents an Excel add-in.

`App`

Deploy a web application or dashboard to Datatailr.

This supports Streamlit, Dash, or any framework that can be started via a standard entrypoint (for example, streamlit run app.py).

Parameters:

Name	Type	Description	Default
`name`	`str`	App display name.	required
`entrypoint`	`Callable \| str`	Callable that starts the app server.	required
`environment`	`Optional[Environment]`	Target environment for the job.	`DEV`
`image`	`Optional[Image]`	Container image to use for execution.	`None`
`run_as`	`Optional[Union[str, User]]`	User to execute the job as.	`None`
`resources`	`Resources`	Resource requirements for the job.	`Resources()`
`acl`	`Optional[ACL]`	Access control settings for the job.	`None`
`framework`	`Optional[str]`	Framework to use for the app.	`None`
`python_version`	`str`	Python version to use.	`'3.12'`
`python_requirements`	`str \| List[str]`	Requirements to install (string or list).	`''`
`build_script_pre`	`str`	Shell script to run before build.	`''`
`build_script_post`	`str`	Shell script to run after build.	`''`
`env_vars`	`Optional[Dict[str, str \| int \| float \| bool]]`	Environment variables to set.	`None`
`get_existing`	`bool`	Update an existing job with the same name.	`False`

Examples:

Minimal Streamlit app.

# app.py
import streamlit as st

def main():
    st.title("Hello Datatailr App")

if __name__ == "__main__":
    main()

Test locally.

streamlit run app.py

Deploy to Datatailr.

from app import main
from datatailr import App

app = App(
    name="Simple Dashboard App",
    entrypoint=main,
    python_requirements="streamlit")

app.run()

`init(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, framework=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None, app_section='')`

Initialize an App deployment.

Parameters:

Name	Type	Description	Default
`name`	`str`	Display name for the app.	required
`entrypoint`	`Callable \| str`	The callable (function) that starts the application.	required
`environment`	`Optional[Environment]`	Target environment for the deployment.	`DEV`
`image`	`Optional[Image]`	Pre-configured container Image. When `None`, an image is built from python_version, python_requirements, and the build scripts.	`None`
`run_as`	`Optional[Union[str, User]]`	User or username to run the app as. Defaults to the currently signed-in user.	`None`
`resources`	`Resources`	CPU and memory resources for the container.	`Resources()`
`acl`	`Optional[ACL]`	Access control list. Defaults to standard permissions for the current user.	`None`
`framework`	`Optional[str]`	Optional framework name (e.g., 'streamlit', 'dash', 'flask'). If not provided, the framework is inferred from the entrypoint.	`None`
`python_version`	`str`	Python version for the container image.	`'3.12'`
`python_requirements`	`str \| List[str]`	Python dependencies (see `Image`).	`''`
`build_script_pre`	`str`	Dockerfile commands to run before installing requirements.	`''`
`build_script_post`	`str`	Dockerfile commands to run after installing requirements.	`''`
`env_vars`	`Optional[Dict[str, str \| int \| float \| bool]]`	Environment variables passed to the running container.	`None`
`get_existing`	`bool`	If `True`, load and update an existing job definition with the same name instead of creating a new one.	`False`
`app_section`	`str`	The section to which the app belongs. If not provided, the app will be assigned to the default section. This affects the app launcher page.	`''`

`DuplicateJobNameError`

Exception raised when a job with a duplicate name is added to the batch.

`EntryPoint`

Represents an entry point for a Datatailr job. This can be a function or a callable object.

`Environment`

Enum representing different environments for Datatailr jobs.

`ExcelAddin`

Represents an Excel add-in deployment on Datatailr.

An Excel add-in exposes Python functions as Excel worksheet functions, allowing users to call server-side computations directly from Excel spreadsheets.

Example

from datatailr import ExcelAddin
from datatailr.excel import Addin

addin_def = Addin("Options Pricer", "Option pricing functions")

@addin_def.expose(description="Black-Scholes price")
def price_option(spot, strike, vol, rate, expiry):
    ...

def __excel_main__(port=8080, ws_port=8000):
    addin_def.run(port, ws_port)

addin = ExcelAddin(
    name="Options Pricer",
    entrypoint=__excel_main__,
    python_requirements=["numpy", "scipy"],
)
addin.run()

`init(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None, app_section='')`

Initialize an Excel add-in deployment.

Parameters:

Name	Type	Description	Default
`name`	`str`	Display name for the add-in.	required
`entrypoint`	`Callable`	The callable (function) that starts the add-in server.	required
`environment`	`Optional[Environment]`	Target environment for the deployment.	`DEV`
`image`	`Optional[Image]`	Pre-configured container Image.	`None`
`run_as`	`Optional[Union[str, User]]`	User or username to run the add-in as.	`None`
`resources`	`Resources`	CPU and memory resources for the container.	`Resources()`
`acl`	`Optional[ACL]`	Access control list.	`None`
`python_version`	`str`	Python version for the container image.	`'3.12'`
`python_requirements`	`str \| List[str]`	Python dependencies (see `Image`).	`''`
`build_script_pre`	`str`	Dockerfile commands to run before installing requirements.	`''`
`build_script_post`	`str`	Dockerfile commands to run after installing requirements.	`''`
`env_vars`	`Optional[Dict[str, str \| int \| float \| bool]]`	Environment variables passed to the running container.	`None`
`get_existing`	`bool`	If `True`, update an existing job definition.	`False`
`version`	`Optional[str \| int]`	The version of the job to get.	`None`
`app_section`	`str`	The section to which the app belongs. If not provided, the app will be assigned to the default section. This affects the app launcher page.	`''`

`Job`

Base class for all Datatailr compute processes (App, Service, ExcelAddin, and workflows).

A Job encapsulates the container image configuration, resource requirements, access control, and deployment metadata needed to schedule and run work on the Datatailr platform. In most cases you should use the concrete subclasses (App, Service, ExcelAddin) or the @workflow decorator rather than instantiating Job directly.

`id` `property`

Unique identifier for the job.

`__enrich_task_runs__(task_runs, job_version)`

Attach version-specific definition data (name, dependencies, resources) to each task run.

We can't rely on the currently-loaded workflow definition because the workflow might have been modified since the run completed, so the graph structure, task ids and resource sizing could all be stale. Instead we fetch the historical definition that produced this run and use it as the source of truth.

When the historical definition isn't available (e.g. it was pruned from the platform) we fall back to the current workflow's name map so the run details remain at least partially usable.

`__find_missing_python_packages()`

Compare the imports detected in the entry point code with the packages listed in python_requirements. Raise a warning if there are any imported packages that are not listed in python_requirements.

`__get_existing__(job_name, environment, version=None, get_tasks=False)` `classmethod`

Retrieve an existing job instance from the Datatailr platform. Based on the job name and environment. If version is specified, it will retrieve the job with the specified version. Otherwise, it will retrieve the latest version of the job.

`init(name, environment=Environment.DEV, image=None, run_as=None, resources=None, acl=None, python_version='auto', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, type=JobType.UNKNOWN, entrypoint=None, get_existing=False, app_section='', version=None)`

Initialize a Job.

Parameters:

Name	Type	Description	Default
`name`	`str`	Display name for the job.	required
`environment`	`Optional[Environment]`	Target environment (dev / pre / prod).	`DEV`
`image`	`Optional[Image]`	Pre-configured container Image. When `None`, an image is built from python_version, python_requirements, and the build scripts.	`None`
`run_as`	`Optional[Union[str, User]]`	User or username under which the job runs. Defaults to the currently signed-in user.	`None`
`resources`	`Resources \| None`	CPU and memory resources for the container.	`None`
`acl`	`Optional[ACL]`	Access control list. Defaults to standard permissions for the current user.	`None`
`python_version`	`str`	Python version for the container image.	`'auto'`
`python_requirements`	`str \| List[str]`	Python dependencies (see `Image`).	`''`
`build_script_pre`	`str`	Dockerfile commands before pip install.	`''`
`build_script_post`	`str`	Dockerfile commands after pip install.	`''`
`env_vars`	`Dict[str, str \| int \| float \| bool] \| None`	Environment variables passed to the container.	`None`
`type`	`Optional[JobType]`	The kind of job (workflow, service, app, excel).	`UNKNOWN`
`entrypoint`	`Optional[EntryPoint]`	The entry point that starts the job.	`None`
`get_existing`	`bool`	If `True`, load an existing job with the same name and update it instead of creating a new definition.	`False`
`app_section`	`str`	The section to which the app belongs. If not provided, the app will be assigned to the default section. This affects the app launcher page.	`''`

`__resolve_task_id__(task_name, task_id=None)`

Resolve a workflow task id from either an explicit task_id or a task_name.

task_id takes precedence: when provided we use it directly without consulting the currently-loaded workflow definition. This matters for runs that come from older workflow versions whose graph (and therefore task names / ids) may have diverged from the current definition.

Returns None when the job is not a workflow (in which case the callers should address the job itself rather than a child task). Raises ValueError when task_name was given but no task with that name exists in the currently-loaded workflow.

`__run_command__(command, *args, **kwargs)`

Run a command in the context of the job. This is used to execute the job's entry point.

`from_dict(job_dict)`

Populate this Job instance from a dictionary (e.g. a platform API response).

Parameters:

Name	Type	Description	Default
`job_dict`	`dict`	A dictionary containing the serialized job data.	required

`get_schedule_args()`

Returns additional arguments for scheduling the job. Override or extend this method as needed.

`logs(run_id, task_name=None, stderr=False, lines=5000, task_id=None)`

Fetch the captured stdout (or stderr) of a task within a run.

Parameters:

Name	Type	Description	Default
`run_id`	`int`	The id of the run to read logs from.	required
`task_name`	`Optional[str]`	For workflows, the display name of the task whose logs to fetch. For non-workflow jobs (App, Service, ExcelAddin, …) `task_name` is informational only — the job itself is addressed.	`None`
`stderr`	`bool`	If True, return the stderr stream instead of stdout.	`False`
`lines`	`int`	Maximum number of trailing lines to return.	`5000`
`task_id`	`Optional[int]`	For workflows, the numeric task id (as exposed by `run_details(...)['tasks'][i]['id']`). When provided it takes precedence over `task_name` and is used as-is, which is the right choice for runs of older workflow versions whose graph may differ from the current one.	`None`

Returns:

Type	Description
`str`	The captured log output as a string. May be empty when no log
`str`	output is available for that run.

`promote(from_environment=None, version=None)`

Promote the job to the next environment. This method is used to promote a version of the job from one environment to the next one. If none of the environments to promote from are specified, it defaults to promote from all environments.

:param from_environment: The environment to promote from. If None, it will promote from all environments. :param version: The version to promote. If None, it will promote the latest version. :return: A tuple of (success: bool, message: str).

from datatailr import Environment from datatailr.scheduler import Job job = Job(name="my_job", environment=Environment.DEV) job.promote(from_environment=Environment.DEV, version=3)

This will promote version 3 of the job from the DEV environment to the next environment (PRE).

`result(run_id, task_name=None, task_id=None)`

Fetch the cached return value of a task within a run.

Parameters:

Name	Type	Description	Default
`run_id`	`int \| str`	The id of the run to read the result from.	required
`task_name`	`Optional[str]`	For workflows, the display name of the task whose result to load. For non-workflow jobs `task_name` is informational only.	`None`
`task_id`	`Optional[int]`	For workflows, the numeric task id (as exposed by `run_details(...)['tasks'][i]['id']`). When provided it takes precedence over `task_name` and is used as-is, which is the right choice for runs of older workflow versions whose graph may differ from the current one.	`None`

Returns:

Type	Description
	The cached Python object returned by the task, or `None` if
	no result is stored (e.g. the task is still running, did not
	return anything, or its arguments cache entry has expired).

`run()`

Run the job. This is equivalent to running job.save() and then job.start().

`run_details(run_id)`

Get the details of a run.

Each task entry is enriched with workflow-definition data taken from the workflow version that actually produced this run (job_version). This means that historical runs surface the graph structure they were executed with, even if the workflow has since been redefined.

Example output

{'cost': 0.00012762248102453157, 'end_time': datetime.datetime(2026, 4, 29, 11, 46, 32, 14506), 'job_version': 1, 'original_run_id': -1, 'partial_rerun': False, 'scheduled_time': None, 'start_provisioning_time': datetime.datetime(2026, 4, 29, 11, 45, 57, 641051), 'start_time': datetime.datetime(2026, 4, 29, 11, 45, 59, 95784), 'state': 'completed', 'submit_time': datetime.datetime(2026, 4, 29, 11, 45, 57, 652091), 'tried_to_start_time': 0, 'name': 'Simple Data Pipeline', 'run_id': 12, 'tasks': [{'cost': 1.3018850769988e-05, 'end_time': datetime.datetime(2026, 4, 29, 11, 46, 17, 598195), 'scheduled_time': None, 'start_provisioning_time': datetime.datetime(2026, 4, 29, 11, 45, 59, 162721), 'start_time': datetime.datetime(2026, 4, 29, 11, 46, 8, 697732), 'state': 'completed', 'submit_time': datetime.datetime(2026, 4, 29, 11, 45, 57, 652091), 'tried_to_start_time': 0, 'name': 'get_data', 'id': 0, 'dependencies': [], 'resources': Resources(memory='256m', cpu=1.0)}]}

:param run_id: The id of the run to get the details of. :return: A dictionary containing the details of the run.

`runs(start_time=None, end_time=None, refresh=False)`

List all runs of the job. The list includes the top-level data and not the tasks. To retrieve the tasks, use the run_details method.

Example output

[]

:param start_time: The start time of the runs to filter by. :param end_time: The end time of the runs to filter by. :param refresh: If True, it will refresh the cached runs. :return: A list of mother-job run dictionaries.

`save()`

Save the job to the Datatailr platform. If the job already exists, it will be updated. The repository state is verified and an image is prepared for execution.

`start()`

Start the job. This will start the job execution on a schedule for workflows if a schedule was specified. For other types of jobs and for workflows without a schedule the job will be run immediately.

`to_dict()`

Convert the Job instance to a dictionary representation.

`to_json()`

Convert the Job instance to a JSON string representation.

`verify_repo_is_ready()`

Verify if the repository is ready for job execution. The check consists of two parts: 1. Check if there are uncommitted changes in the repository. 2. Check if the local commit matches the remote HEAD (the repo is synced with the remote). Returns a tuple of (branch: str, commit_hash: str).

`versions(environment=None)`

List all versions of the job in the specified environment If no environment is specified, it lists versions across all environments.

`JobType`

Enum representing different types of Datatailr jobs.

`from_str(value)` `classmethod`

Create a JobType from a string while normalizing legacy aliases.

`Resources` `dataclass`

Represents the compute resources allocated to a job container.

Attributes:

Name	Type	Description
`memory`	`str`	Memory limit as a string (e.g. `"256m"`, `"1g"`).
`cpu`	`float`	Number of CPU cores to allocate (e.g. `1`, `0.5`).

`Schedule`

Build a schedule object for batch/workflow jobs using friendly fields.

Parameters:

Name	Type	Description	Default
`cron_expression`	`str`	Raw cron expression to use (string).	`''`
`at_minutes`	`list[int] \| None`	Specific minutes within the hour, e.g. [0, 30].	`None`
`every_minute`	`int \| None`	Run every N minutes.	`None`
`at_hours`	`list[int] \| None`	Specific hours within the day, e.g. [0, 12].	`None`
`every_hour`	`int \| None`	Run every N hours.	`None`
`weekdays`	`list[str] \| None`	Weekdays by name, e.g. ["Mon", "Wed", "Fri"].	`None`
`day_of_month`	`int \| None`	Day of the month (1-31).	`None`
`in_month`	`list[str] \| None`	Months by name, e.g. ["Jan", "Jul"].	`None`
`every_month`	`int \| None`	Run every N months.	`None`
`timezone`	`str \| None`	Time zone name, e.g. "UTC".	`None`
`run_after_job_uuid`	`str \| None`	Job UUID to run after.	`None`
`run_after_job_name`	`str \| None`	Job name to run after.	`None`
`run_after_job_condition`	`str \| None`	Condition for dependency, e.g. "on failure".	`None`

Examples:

schedule = Schedule(at_hours=[0])
schedule = Schedule(at_minutes=[0, 30], weekdays=["Mon", "Wed", "Fri"])

`init(cron_expression='', at_minutes=None, every_minute=None, at_hours=None, every_hour=None, weekdays=None, day_of_month=None, in_month=None, every_month=None, timezone=None, run_after_job_uuid=None, run_after_job_name=None, run_after_job_condition=None)`

Initialize a Schedule.

You can either provide a raw cron_expression or use the human-readable helper parameters (which are compiled into cron syntax automatically).

Parameters:

Name	Type	Description	Default
`cron_expression`	`str`	A raw cron string (e.g. `"0 /2 * *"`). If provided alongside helper parameters, the helpers take precedence.	`''`
`at_minutes`	`list[int] \| None`	Specific minutes past the hour to run (e.g. `[0, 30]` for `":00"` and `":30"`).	`None`
`every_minute`	`int \| None`	Run every N minutes (e.g. `5` for every 5 minutes).	`None`
`at_hours`	`list[int] \| None`	Specific hours of the day to run (0 -- 23).	`None`
`every_hour`	`int \| None`	Run every N hours.	`None`
`weekdays`	`list[str] \| None`	Days of the week to run (e.g. `["mon", "wed", "fri"]`).	`None`
`day_of_month`	`int \| None`	Day of the month to run (1 -- 31).	`None`
`in_month`	`list[str] \| None`	Months to run in (e.g. `["jan", "apr", "jul", "oct"]`).	`None`
`every_month`	`int \| None`	Run every N months.	`None`
`timezone`	`str \| None`	IANA timezone name (e.g. `"America/New_York"`).	`None`
`run_after_job_uuid`	`str \| None`	UUID of a job that must complete before this schedule triggers.	`None`
`run_after_job_name`	`str \| None`	Name of a job that must complete before this schedule triggers.	`None`
`run_after_job_condition`	`str \| None`	Required completion status of the predecessor job (e.g. `"success"`).	`None`

Example

from datatailr import Schedule
# Every weekday at 08:00 and 16:00 UTC
s = Schedule(at_hours=[8, 16], weekdays=["mon","tue","wed","thu","fri"])

`get_cron_string()`

Return the compiled cron string.

Returns:

Type	Description
`str`	Cron string (str).

Examples:

>>> Schedule(
...     at_minutes=[0, 15, 30, 45],
...     at_hours=[0, 12],
...     weekdays=["Mon", "Wed", "Fri"],
...     day_of_month=15,
...     in_month=["Jan", "Jul"],
... ).get_cron_string()
'0 0,15,30,45 0,12 15 1,7 1,3,5'

`Service`

Represents a long-running background service deployment on Datatailr.

A service runs continuously (e.g., an API server, a message consumer, or any always-on process). It is restarted automatically if it exits.

Example

from datatailr import Service

# service.py
from flask import Flask

app = Flask(__name__)

@app.route("/health")
def health_check():
    return "OK"

# Service entrypoints receive the port from Datatailr.
def run_server(port):
    app.run("0.0.0.0", port=int(port), debug=False)

svc = Service(
    name="Simple Service",
    entrypoint=run_server,
    python_requirements=["flask"],
)
svc.run()

`init(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None)`

Initialize a Service deployment.

Parameters:

Name	Type	Description	Default
`name`	`str`	Display name for the service.	required
`entrypoint`	`Callable`	The callable (function) that starts the service.	required
`environment`	`Optional[Environment]`	Target environment for the deployment.	`DEV`
`image`	`Optional[Image]`	Pre-configured container Image.	`None`
`run_as`	`Optional[Union[str, User]]`	User or username to run the service as.	`None`
`resources`	`Resources`	CPU and memory resources for the container.	`Resources()`
`acl`	`Optional[ACL]`	Access control list.	`None`
`python_version`	`str`	Python version for the container image.	`'3.12'`
`python_requirements`	`str \| List[str]`	Python dependencies (see `Image`).	`''`
`build_script_pre`	`str`	Dockerfile commands to run before installing requirements.	`''`
`build_script_post`	`str`	Dockerfile commands to run after installing requirements.	`''`
`env_vars`	`Optional[Dict[str, str \| int \| float \| bool]]`	Environment variables passed to the running container.	`None`
`get_existing`	`bool`	If `True`, update an existing job definition.	`False`
`version`	`Optional[str \| int]`	The version of the job to get.	`None`

`Task`

Represents a job within a batch job.

This class can be extended to define specific configurations for each job in the batch.

`args` `property` `writable`

Returns the arguments for the Task instance.

`id` `property`

Returns the unique identifier of the Task instance.

`call(*args, **kwds)`

Allows the Task instance to be called like a function, returning itself. This is useful for chaining or functional-style programming.

`alias(name)`

Set an alias for the Task instance.

:param name: The alias name to set.

`run()`

Execute the job's entrypoint.

`set_resources(resources=None, memory=None, cpu=None)`

Set the resources for the Task instance.

:param resources: The Resources instance to set.

`to_dict()`

Convert the Task instance to a dictionary representation.

`to_json()`

Convert the Task instance to a JSON string representation.

`translate_dependencies()`

Translate the dependencies of the Task instance into a format suitable for the batch job.

`update_env_vars(env_vars)`

Update the environment variables for the Task instance.

:param env_vars: A dictionary of environment variables to update.

`TaskError`

Exception raised for errors related to workflow tasks.

`Workflow`

Represents a workflow in the scheduler.

Inherits from Job and is used to define workflows with specific configurations.

`next_job_id` `property`

Returns a generator for the next task ID in the workflow.

`add_job(job)`

Adds a task to the workflow.

:param job: The Task instance to add.

`get_env_vars_copy()`

Returns a copy of the environment variables for the Workflow instance.

`rerun(run_id, version=None, full=False)`

Rerun a run id with specific version (latest version by default). If full is true then all the tasks will be rerun, including the ones that already ran successfully.

:param run_id: The run id to rerun. :param version: The version to rerun. If None, it will rerun the latest version. :param full: If True, it will rerun the full workflow, including the ones that already ran successfully. :return: A tuple of (success: bool, message: str).

`reset()`

Reset the Workflow instance to its initial state.

`set_local_run(local_run)`

Set the local run flag for the Workflow instance.

:param local_run: A boolean indicating whether to run locally.

`to_dict()`

Convert the Workflow instance to a dictionary representation.

`to_json()`

Convert the Workflow instance to a JSON string representation.

`task(memory=DEFAULT_TASK_MEMORY, cpu=DEFAULT_TASK_CPU)`

Decorator that marks a function as a workflow task (batch job).

Use this via the public alias @task() to declare individual computational steps inside a @workflow-decorated function.

Parameters:

Name	Type	Description	Default
`memory`	`str`	Memory limit for this task's container (e.g. `"256m"`, `"1g"`).	`DEFAULT_TASK_MEMORY`
`cpu`	`float`	Number of CPU cores to allocate for this task (e.g. `1`, `0.5`).	`DEFAULT_TASK_CPU`

Returns:

Type	Description
	A decorator that wraps the target function so it can participate
	in DAG-based workflow orchestration.

Example

from datatailr import task

@task(memory="512m", cpu=2)
def heavy_computation(x, y):
    return x ** y

Index

Datatailr Scheduler Module

App

__init__(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, framework=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None, app_section='')

DuplicateJobNameError

EntryPoint

Environment

ExcelAddin

__init__(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None, app_section='')

Job

id property

__enrich_task_runs__(task_runs, job_version)

__find_missing_python_packages()

__get_existing__(job_name, environment, version=None, get_tasks=False) classmethod

__init__(name, environment=Environment.DEV, image=None, run_as=None, resources=None, acl=None, python_version='auto', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, type=JobType.UNKNOWN, entrypoint=None, get_existing=False, app_section='', version=None)

__resolve_task_id__(task_name, task_id=None)

__run_command__(command, *args, **kwargs)

from_dict(job_dict)

get_schedule_args()

logs(run_id, task_name=None, stderr=False, lines=5000, task_id=None)

promote(from_environment=None, version=None)

result(run_id, task_name=None, task_id=None)

run()

run_details(run_id)

runs(start_time=None, end_time=None, refresh=False)

save()

start()

to_dict()

to_json()

verify_repo_is_ready()

versions(environment=None)

JobType

from_str(value) classmethod

Resources dataclass

Schedule

__init__(cron_expression='', at_minutes=None, every_minute=None, at_hours=None, every_hour=None, weekdays=None, day_of_month=None, in_month=None, every_month=None, timezone=None, run_after_job_uuid=None, run_after_job_name=None, run_after_job_condition=None)

get_cron_string()

Service

__init__(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None)

Task

args property writable

id property

__call__(*args, **kwds)

alias(name)

run()

set_resources(resources=None, memory=None, cpu=None)

to_dict()

to_json()

translate_dependencies()

update_env_vars(env_vars)

TaskError

Workflow

next_job_id property

add_job(job)

get_env_vars_copy()

rerun(run_id, version=None, full=False)

reset()

set_local_run(local_run)

to_dict()

to_json()

task(memory=DEFAULT_TASK_MEMORY, cpu=DEFAULT_TASK_CPU)

`App`

`init(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, framework=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None, app_section='')`

`DuplicateJobNameError`

`EntryPoint`

`Environment`

`ExcelAddin`

`init(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None, app_section='')`

`Job`

`id` `property`

`__enrich_task_runs__(task_runs, job_version)`

`__find_missing_python_packages()`

`__get_existing__(job_name, environment, version=None, get_tasks=False)` `classmethod`

`init(name, environment=Environment.DEV, image=None, run_as=None, resources=None, acl=None, python_version='auto', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, type=JobType.UNKNOWN, entrypoint=None, get_existing=False, app_section='', version=None)`

`__resolve_task_id__(task_name, task_id=None)`

`__run_command__(command, *args, **kwargs)`

`from_dict(job_dict)`

`get_schedule_args()`

`logs(run_id, task_name=None, stderr=False, lines=5000, task_id=None)`

`promote(from_environment=None, version=None)`

`result(run_id, task_name=None, task_id=None)`

`run()`

`run_details(run_id)`

`runs(start_time=None, end_time=None, refresh=False)`

`save()`

`start()`

`to_dict()`

`to_json()`

`verify_repo_is_ready()`

`versions(environment=None)`

`JobType`

`from_str(value)` `classmethod`

`Resources` `dataclass`

`Schedule`

`init(cron_expression='', at_minutes=None, every_minute=None, at_hours=None, every_hour=None, weekdays=None, day_of_month=None, in_month=None, every_month=None, timezone=None, run_after_job_uuid=None, run_after_job_name=None, run_after_job_condition=None)`

`get_cron_string()`

`Service`

`init(name, entrypoint, environment=Environment.DEV, image=None, run_as=None, resources=Resources(), acl=None, python_version='3.12', python_requirements='', build_script_pre='', build_script_post='', env_vars=None, get_existing=False, version=None)`

`Task`

`args` `property` `writable`

`id` `property`

`call(*args, **kwds)`

`alias(name)`

`run()`

`set_resources(resources=None, memory=None, cpu=None)`

`to_dict()`

`to_json()`

`translate_dependencies()`

`update_env_vars(env_vars)`

`TaskError`

`Workflow`

`next_job_id` `property`

`add_job(job)`

`get_env_vars_copy()`

`rerun(run_id, version=None, full=False)`

`reset()`

`set_local_run(local_run)`

`to_dict()`

`to_json()`

`task(memory=DEFAULT_TASK_MEMORY, cpu=DEFAULT_TASK_CPU)`