def get_context(self, solid_config=None, mode_def=None, run_config=None):
"""Get a dagstermill execution context for interactive exploration and development.
Args:
solid_config (Optional[Any]): If specified, this value will be made available on the
context as its ``solid_config`` property.
mode_def (Optional[:class:`dagster.ModeDefinition`]): If specified, defines the mode to
use to construct the context. Specify this if you would like a context constructed
with specific ``resource_defs`` or ``logger_defs``. By default, an ephemeral mode
with a console logger will be constructed.
run_config(Optional[dict]): The environment config dict with which to construct
the context.
Returns:
:py:class:`~dagstermill.DagstermillExecutionContext`
"""
check.opt_inst_param(mode_def, "mode_def", ModeDefinition)
run_config = check.opt_dict_param(run_config,
"run_config",
key_type=str)
# If we are running non-interactively, and there is already a context reconstituted, return
# that context rather than overwriting it.
if self.context is not None and isinstance(
self.context, DagstermillRuntimeExecutionContext):
return self.context
if not mode_def:
mode_def = ModeDefinition(
logger_defs={"dagstermill": colored_console_logger})
run_config["loggers"] = {"dagstermill": {}}
solid_def = SolidDefinition(
name="this_solid",
input_defs=[],
compute_fn=lambda *args, **kwargs: None,
output_defs=[],
description=
"Ephemeral solid constructed by dagstermill.get_context()",
required_resource_keys=mode_def.resource_key_set,
)
pipeline_def = PipelineDefinition(
[solid_def],
mode_defs=[mode_def],
name="ephemeral_dagstermill_pipeline")
run_id = make_new_run_id()
# construct stubbed PipelineRun for notebook exploration...
# The actual pipeline run during pipeline execution will be serialized and reconstituted
# in the `reconstitute_pipeline_context` call
pipeline_run = PipelineRun(
pipeline_name=pipeline_def.name,
run_id=run_id,
run_config=run_config,
mode=mode_def.name,
step_keys_to_execute=None,
status=PipelineRunStatus.NOT_STARTED,
tags=None,
)
self.in_pipeline = False
self.solid_def = solid_def
self.pipeline = pipeline_def
execution_plan = create_execution_plan(self.pipeline,
run_config,
mode=mode_def.name)
with scoped_pipeline_context(
execution_plan,
run_config,
pipeline_run,
DagsterInstance.ephemeral(),
scoped_resources_builder_cm=self._setup_resources,
) as pipeline_context:
self.context = DagstermillExecutionContext(
pipeline_context=pipeline_context,
solid_config=solid_config,
resource_keys_to_init=get_required_resource_keys_to_init(
execution_plan,
pipeline_context.intermediate_storage_def,
),
solid_name=solid_def.name,
)
return self.context
def reconstitute_pipeline_context(
self,
output_log_path=None,
marshal_dir=None,
environment_dict=None,
handle_kwargs=None,
pipeline_run_dict=None,
solid_subset=None,
solid_handle_kwargs=None,
instance_ref_dict=None,
):
'''Reconstitutes a context for dagstermill-managed execution.
You'll see this function called to reconstruct a pipeline context within the ``injected
parameters`` cell of a dagstermill output notebook. Users should not call this function
interactively except when debugging output notebooks.
Use :func:`dagstermill.get_context` in the ``parameters`` cell of your notebook to define a
context for interactive exploration and development. This call will be replaced by one to
:func:`dagstermill.reconstitute_pipeline_context` when the notebook is executed by
dagstermill.
'''
check.opt_str_param(output_log_path, 'output_log_path')
check.opt_str_param(marshal_dir, 'marshal_dir')
environment_dict = check.opt_dict_param(environment_dict,
'environment_dict',
key_type=str)
check.dict_param(pipeline_run_dict, 'pipeline_run_dict')
check.dict_param(handle_kwargs, 'handle_kwargs')
check.opt_list_param(solid_subset, 'solid_subset', of_type=str)
check.dict_param(solid_handle_kwargs, 'solid_handle_kwargs')
check.dict_param(instance_ref_dict, 'instance_ref_dict')
try:
handle = load_handle.handle_for_pipeline_cli_args(
handle_kwargs, use_default_repository_yaml=False)
except (check.CheckError, load_handle.UsageError) as err:
six.raise_from(
DagstermillError(
'Cannot invoke a dagstermill solid from an in-memory pipeline that was not loaded '
'from an ExecutionTargetHandle. Run this pipeline using dagit, the dagster CLI, '
'through dagster-graphql, or in-memory after loading it through an '
'ExecutionTargetHandle.'),
err,
)
try:
instance_ref = unpack_value(instance_ref_dict)
instance = DagsterInstance.from_ref(instance_ref)
except Exception as err: # pylint: disable=broad-except
six.raise_from(
DagstermillError(
'Error when attempting to resolve DagsterInstance from serialized InstanceRef'
),
err,
)
pipeline_def = check.inst_param(
handle.build_pipeline_definition(),
'pipeline_def (from handle {handle_dict})'.format(
handle_dict=handle.data._asdict()),
PipelineDefinition,
).build_sub_pipeline(solid_subset)
solid_handle = SolidHandle.from_dict(solid_handle_kwargs)
solid_def = pipeline_def.get_solid(solid_handle).definition
pipeline_run = unpack_value(pipeline_run_dict)
self.marshal_dir = marshal_dir
self.in_pipeline = True
self.solid_def = solid_def
self.pipeline_def = pipeline_def
execution_plan = create_execution_plan(self.pipeline_def,
environment_dict, pipeline_run)
with scoped_pipeline_context(
self.pipeline_def,
environment_dict,
pipeline_run,
instance,
execution_plan,
scoped_resources_builder_cm=self._setup_resources,
) as pipeline_context:
self.context = DagstermillExecutionContext(
pipeline_context=pipeline_context,
solid_config=None,
resource_keys_to_init=get_required_resource_keys_to_init(
execution_plan, pipeline_context.system_storage_def),
)
return self.context
def reconstitute_pipeline_context(
self,
output_log_path=None,
marshal_dir=None,
run_config=None,
executable_dict=None,
pipeline_run_dict=None,
solid_handle_kwargs=None,
instance_ref_dict=None,
):
"""Reconstitutes a context for dagstermill-managed execution.
You'll see this function called to reconstruct a pipeline context within the ``injected
parameters`` cell of a dagstermill output notebook. Users should not call this function
interactively except when debugging output notebooks.
Use :func:`dagstermill.get_context` in the ``parameters`` cell of your notebook to define a
context for interactive exploration and development. This call will be replaced by one to
:func:`dagstermill.reconstitute_pipeline_context` when the notebook is executed by
dagstermill.
"""
check.opt_str_param(output_log_path, "output_log_path")
check.opt_str_param(marshal_dir, "marshal_dir")
run_config = check.opt_dict_param(run_config,
"run_config",
key_type=str)
check.dict_param(pipeline_run_dict, "pipeline_run_dict")
check.dict_param(executable_dict, "executable_dict")
check.dict_param(solid_handle_kwargs, "solid_handle_kwargs")
check.dict_param(instance_ref_dict, "instance_ref_dict")
pipeline = ReconstructablePipeline.from_dict(executable_dict)
pipeline_def = pipeline.get_definition()
try:
instance_ref = unpack_value(instance_ref_dict)
instance = DagsterInstance.from_ref(instance_ref)
except Exception as err: # pylint: disable=broad-except
six.raise_from(
DagstermillError(
"Error when attempting to resolve DagsterInstance from serialized InstanceRef"
),
err,
)
pipeline_run = unpack_value(pipeline_run_dict)
solid_handle = SolidHandle.from_dict(solid_handle_kwargs)
solid_def = pipeline_def.get_solid(solid_handle).definition
self.marshal_dir = marshal_dir
self.in_pipeline = True
self.solid_def = solid_def
self.pipeline = pipeline
execution_plan = create_execution_plan(
self.pipeline,
run_config,
mode=pipeline_run.mode,
step_keys_to_execute=pipeline_run.step_keys_to_execute,
)
with scoped_pipeline_context(
execution_plan,
run_config,
pipeline_run,
instance,
scoped_resources_builder_cm=self._setup_resources,
# Set this flag even though we're not in test for clearer error reporting
raise_on_error=True,
) as pipeline_context:
self.context = DagstermillRuntimeExecutionContext(
pipeline_context=pipeline_context,
solid_config=run_config.get("solids",
{}).get(solid_def.name,
{}).get("config"),
resource_keys_to_init=get_required_resource_keys_to_init(
execution_plan,
pipeline_context.intermediate_storage_def,
),
solid_name=solid_def.name,
)
return self.context
def get_context(self,
solid_config=None,
mode_def=None,
environment_dict=None):
'''Get a dagstermill execution context for interactive exploration and development.
Args:
solid_config (Optional[Any]): If specified, this value will be made available on the
context as its ``solid_config`` property.
mode_def (Optional[:class:`dagster.ModeDefinition`]): If specified, defines the mode to
use to construct the context. Specify this if you would like a context constructed
with specific ``resource_defs`` or ``logger_defs``. By default, an ephemeral mode
with a console logger will be constructed.
environment_dict(Optional[dict]): The environment config dict with which to construct
the context.
Returns:
:class:`dagstermill.DagstermillExecutionContext`
'''
check.opt_inst_param(mode_def, 'mode_def', ModeDefinition)
environment_dict = check.opt_dict_param(environment_dict,
'environment_dict',
key_type=str)
if not mode_def:
mode_def = ModeDefinition(
logger_defs={'dagstermill': colored_console_logger})
environment_dict['loggers'] = {'dagstermill': {}}
solid_def = SolidDefinition(
name='this_solid',
input_defs=[],
compute_fn=lambda *args, **kwargs: None,
output_defs=[],
description=
'Ephemeral solid constructed by dagstermill.get_context()',
required_resource_keys=mode_def.resource_key_set,
)
pipeline_def = PipelineDefinition(
[solid_def],
mode_defs=[mode_def],
name='ephemeral_dagstermill_pipeline')
run_id = make_new_run_id()
# construct stubbed PipelineRun for notebook exploration...
# The actual pipeline run during pipeline execution will be serialized and reconstituted
# in the `reconstitute_pipeline_context` call
pipeline_run = PipelineRun(
pipeline_name=pipeline_def.name,
run_id=run_id,
environment_dict=environment_dict,
mode=mode_def.name,
selector=None,
step_keys_to_execute=None,
status=PipelineRunStatus.NOT_STARTED,
tags=None,
)
self.in_pipeline = False
self.solid_def = solid_def
self.pipeline_def = pipeline_def
execution_plan = create_execution_plan(self.pipeline_def,
environment_dict, pipeline_run)
with scoped_pipeline_context(
self.pipeline_def,
environment_dict,
pipeline_run,
DagsterInstance.ephemeral(),
execution_plan,
scoped_resources_builder_cm=self._setup_resources,
) as pipeline_context:
self.context = DagstermillExecutionContext(
pipeline_context=pipeline_context,
solid_config=solid_config,
resource_keys_to_init=get_required_resource_keys_to_init(
execution_plan, pipeline_context.system_storage_def),
)
return self.context
