import copy
import os
import pickle
import sys
import tempfile
import uuid
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Set, Type, Union, cast
import nbformat
import papermill
from dagster import (
In,
OpDefinition,
Out,
Output,
_check as check,
_seven,
)
from dagster._config.pythonic_config import Config, infer_schema_from_config_class
from dagster._config.pythonic_config.type_check_utils import safe_is_subclass
from dagster._core.definitions.events import AssetMaterialization, Failure, RetryRequested
from dagster._core.definitions.metadata import MetadataValue
from dagster._core.definitions.reconstruct import ReconstructableJob
from dagster._core.definitions.utils import normalize_tags
from dagster._core.execution.context.compute import OpExecutionContext
from dagster._core.execution.context.input import build_input_context
from dagster._core.execution.context.system import StepExecutionContext
from dagster._core.execution.plan.outputs import StepOutputHandle
from dagster._serdes import pack_value
from dagster._seven import get_system_temp_directory
from dagster._utils import mkdir_p, safe_tempfile_path
from dagster._utils.error import serializable_error_info_from_exc_info
from papermill.engines import papermill_engines
from papermill.iorw import load_notebook_node, write_ipynb
from .compat import ExecutionError
from .engine import DagstermillEngine
from .errors import DagstermillError
from .translator import DagsterTranslator
def _clean_path_for_windows(notebook_path: str) -> str:
"""In windows, the notebook can't render in the Dagster UI unless the C: prefix is removed.
os.path.splitdrive will split the path into (drive, tail), so just return the tail.
"""
return os.path.splitdrive(notebook_path)[1]
# https://github.com/nteract/papermill/blob/17d4bbb3960c30c263bca835e48baf34322a3530/papermill/parameterize.py
def _find_first_tagged_cell_index(nb, tag):
parameters_indices = []
for idx, cell in enumerate(nb.cells):
if tag in cell.metadata.tags:
parameters_indices.append(idx)
if not parameters_indices:
return -1
return parameters_indices[0]
# This is based on papermill.parameterize.parameterize_notebook
# Typically, papermill injects the injected-parameters cell *below* the parameters cell
# but we want to *replace* the parameters cell, which is what this function does.
def replace_parameters(context, nb, parameters):
"""Assigned parameters into the appropriate place in the input notebook.
Args:
nb (NotebookNode): Executable notebook object
parameters (dict): Arbitrary keyword arguments to pass to the notebook parameters.
"""
check.dict_param(parameters, "parameters")
# Copy the nb object to avoid polluting the input
nb = copy.deepcopy(nb)
# papermill method chooses translator based on kernel_name and language, but we just call the
# DagsterTranslator to generate parameter content based on the kernel_name
param_content = DagsterTranslator.codify(parameters)
newcell = nbformat.v4.new_code_cell(source=param_content)
newcell.metadata["tags"] = ["injected-parameters"]
param_cell_index = _find_first_tagged_cell_index(nb, "parameters")
injected_cell_index = _find_first_tagged_cell_index(nb, "injected-parameters")
if injected_cell_index >= 0:
# Replace the injected cell with a new version
before = nb.cells[:injected_cell_index]
after = nb.cells[injected_cell_index + 1 :]
check.int_value_param(param_cell_index, -1, "param_cell_index")
# We should have blown away the parameters cell if there is an injected-parameters cell
elif param_cell_index >= 0:
# Replace the parameter cell with the injected-parameters cell
before = nb.cells[:param_cell_index]
after = nb.cells[param_cell_index + 1 :]
else:
# Inject to the top of the notebook, presumably first cell includes dagstermill import
context.log.debug(
"Executing notebook with no tagged parameters cell: injecting boilerplate in first "
"cell."
)
before = []
after = nb.cells
nb.cells = before + [newcell] + after
nb.metadata.papermill["parameters"] = _seven.json.dumps(parameters)
return nb
def get_papermill_parameters(
step_context: StepExecutionContext,
inputs: Mapping[str, object],
output_log_path: str,
compute_descriptor: str,
) -> Mapping[str, object]:
check.param_invariant(
isinstance(step_context.run_config, dict),
"step_context",
"StepExecutionContext must have valid run_config",
)
run_id = step_context.run_id
temp_dir = get_system_temp_directory()
marshal_dir = os.path.normpath(os.path.join(temp_dir, "dagstermill", str(run_id), "marshal"))
mkdir_p(marshal_dir)
if not isinstance(step_context.job, ReconstructableJob):
if compute_descriptor == "asset":
raise DagstermillError(
"Can't execute a dagstermill asset that is not reconstructable. "
"Use the reconstructable() function if executing from python"
)
else:
raise DagstermillError(
"Can't execute a dagstermill op from a job that is not reconstructable. "
"Use the reconstructable() function if executing from python"
)
dm_executable_dict = step_context.job.to_dict()
dm_context_dict = {
"output_log_path": output_log_path,
"marshal_dir": marshal_dir,
"run_config": step_context.run_config,
}
dm_node_handle_kwargs = step_context.node_handle._asdict()
dm_step_key = step_context.step.key
parameters = {}
parameters["__dm_context"] = dm_context_dict
parameters["__dm_executable_dict"] = dm_executable_dict
parameters["__dm_pipeline_run_dict"] = pack_value(step_context.dagster_run)
parameters["__dm_node_handle_kwargs"] = dm_node_handle_kwargs
parameters["__dm_instance_ref_dict"] = pack_value(step_context.instance.get_ref())
parameters["__dm_step_key"] = dm_step_key
parameters["__dm_input_names"] = list(inputs.keys())
return parameters
def execute_notebook(
step_context: StepExecutionContext,
name: str,
save_notebook_on_failure: bool,
notebook_path: str,
output_notebook_dir: str,
inputs: Mapping[str, object],
) -> str:
with safe_tempfile_path() as output_log_path:
prefix = str(uuid.uuid4())
parameterized_notebook_path = os.path.join(output_notebook_dir, f"{prefix}-inter.ipynb")
executed_notebook_path = os.path.join(output_notebook_dir, f"{prefix}-out.ipynb")
# Scaffold the registration here
nb = load_notebook_node(notebook_path)
compute_descriptor = "op"
nb_no_parameters = replace_parameters(
step_context,
nb,
get_papermill_parameters(
step_context,
inputs,
output_log_path,
compute_descriptor,
),
)
write_ipynb(nb_no_parameters, parameterized_notebook_path)
try:
papermill_engines.register("dagstermill", DagstermillEngine)
papermill.execute_notebook(
input_path=parameterized_notebook_path,
output_path=executed_notebook_path,
engine_name="dagstermill",
log_output=True,
)
except Exception as ex:
step_context.log.warn(
f"Error when attempting to materialize executed notebook: {serializable_error_info_from_exc_info(sys.exc_info())!s}"
)
if isinstance(ex, ExecutionError):
exception_name = ex.ename # type: ignore
if exception_name in ["RetryRequested", "Failure"]:
step_context.log.warn(
f"Encountered raised {exception_name} in notebook. Use"
" dagstermill.yield_event with RetryRequested or Failure to trigger"
" their behavior."
)
if save_notebook_on_failure:
storage_dir = step_context.instance.storage_directory()
storage_path = os.path.join(storage_dir, f"{prefix}-out.ipynb")
with open(storage_path, "wb") as dest_file_obj:
with open(executed_notebook_path, "rb") as obj:
dest_file_obj.write(obj.read())
step_context.log.info(f"Failed notebook written to {storage_path}")
raise
step_context.log.debug(f"Notebook execution complete for {name} at {executed_notebook_path}.")
return executed_notebook_path
def _handle_events_from_notebook(
step_context: StepExecutionContext, executed_notebook_path: str
) -> Iterable:
# deferred import for perf
import scrapbook
output_nb = scrapbook.read_notebook(executed_notebook_path)
for output_name in step_context.op_def.output_dict.keys():
data_dict = output_nb.scraps.data_dict
if output_name in data_dict:
# read outputs that were passed out of process via io manager from `yield_result`
step_output_handle = StepOutputHandle(
step_key=step_context.step.key,
output_name=output_name,
)
output_context = step_context.get_output_context(step_output_handle)
io_manager = step_context.get_io_manager(step_output_handle)
value = io_manager.load_input(
build_input_context(
upstream_output=output_context, dagster_type=output_context.dagster_type
)
)
yield Output(value, output_name)
for key, value in output_nb.scraps.items():
if key.startswith("event-"):
with open(value.data, "rb") as fd:
event = pickle.loads(fd.read())
if isinstance(event, (Failure, RetryRequested)):
raise event
else:
yield event
def _make_dagstermill_compute_fn(
dagster_factory_name: str,
name: str,
notebook_path: str,
output_notebook_name: Optional[str] = None,
asset_key_prefix: Optional[Sequence[str]] = None,
output_notebook: Optional[str] = None,
save_notebook_on_failure: bool = False,
) -> Callable:
def _t_fn(op_context: OpExecutionContext, inputs: Mapping[str, object]) -> Iterable:
check.param_invariant(
isinstance(op_context.run_config, dict),
"context",
"StepExecutionContext must have valid run_config",
)
step_context = op_context.get_step_execution_context()
with tempfile.TemporaryDirectory() as output_notebook_dir:
executed_notebook_path = execute_notebook(
step_context,
name=name,
inputs=inputs,
save_notebook_on_failure=save_notebook_on_failure,
notebook_path=notebook_path,
output_notebook_dir=output_notebook_dir,
)
if output_notebook_name is not None:
# yield output notebook binary stream as an op output
with open(executed_notebook_path, "rb") as fd:
yield Output(fd.read(), output_notebook_name)
else:
# backcompat
executed_notebook_file_handle = None
try:
# use binary mode when when moving the file since certain file_managers such as S3
# may try to hash the contents
with open(executed_notebook_path, "rb") as fd:
executed_notebook_file_handle = op_context.resources.file_manager.write(
fd, mode="wb", ext="ipynb"
)
executed_notebook_materialization_path = (
executed_notebook_file_handle.path_desc
)
yield AssetMaterialization(
asset_key=[*(asset_key_prefix or []), f"{name}_output_notebook"],
description="Location of output notebook in file manager",
metadata={
"path": MetadataValue.path(executed_notebook_materialization_path),
},
)
except Exception:
# if file manager writing errors, e.g. file manager is not provided, we throw a warning
# and fall back to the previously stored temp executed notebook.
op_context.log.warning(
"Error when attempting to materialize executed notebook using file"
" manager:"
f" {serializable_error_info_from_exc_info(sys.exc_info())}\nNow"
" falling back to local: notebook execution was temporarily materialized"
f" at {executed_notebook_path}\nIf you have supplied a file manager and"
" expect to use it for materializing the notebook, please include"
' "file_manager" in the `required_resource_keys` argument to'
f" `{dagster_factory_name}`"
)
if output_notebook is not None:
yield Output(executed_notebook_file_handle, output_notebook)
yield from _handle_events_from_notebook(step_context, executed_notebook_path)
return _t_fn
[docs]def define_dagstermill_op(
name: str,
notebook_path: str,
ins: Optional[Mapping[str, In]] = None,
outs: Optional[Mapping[str, Out]] = None,
config_schema: Optional[Union[Any, Mapping[str, Any]]] = None,
required_resource_keys: Optional[Set[str]] = None,
output_notebook_name: Optional[str] = None,
asset_key_prefix: Optional[Union[Sequence[str], str]] = None,
description: Optional[str] = None,
tags: Optional[Mapping[str, Any]] = None,
io_manager_key: Optional[str] = None,
save_notebook_on_failure: bool = False,
) -> OpDefinition:
"""Wrap a Jupyter notebook in a op.
Arguments:
name (str): The name of the op.
notebook_path (str): Path to the backing notebook.
ins (Optional[Mapping[str, In]]): The op's inputs.
outs (Optional[Mapping[str, Out]]): The op's outputs. Your notebook should
call :py:func:`~dagstermill.yield_result` to yield each of these outputs.
required_resource_keys (Optional[Set[str]]): The string names of any required resources.
output_notebook_name: (Optional[str]): If set, will be used as the name of an injected output
of type of :py:class:`~dagster.BufferedIOBase` that is the file object of the executed
notebook (in addition to the :py:class:`~dagster.AssetMaterialization` that is always
created). It allows the downstream ops to access the executed notebook via a file
object.
asset_key_prefix (Optional[Union[List[str], str]]): If set, will be used to prefix the
asset keys for materialized notebooks.
description (Optional[str]): If set, description used for op.
tags (Optional[Dict[str, str]]): If set, additional tags used to annotate op.
Dagster uses the tag keys `notebook_path` and `kind`, which cannot be
overwritten by the user.
io_manager_key (Optional[str]): If using output_notebook_name, you can additionally provide
a string key for the IO manager used to store the output notebook.
If not provided, the default key output_notebook_io_manager will be used.
save_notebook_on_failure (bool): If True and the notebook fails during execution, the failed notebook will be
written to the Dagster storage directory. The location of the file will be printed in the Dagster logs.
Defaults to False.
Returns:
:py:class:`~dagster.OpDefinition`
"""
check.str_param(name, "name")
check.str_param(notebook_path, "notebook_path")
check.bool_param(save_notebook_on_failure, "save_notebook_on_failure")
required_resource_keys = set(
check.opt_set_param(required_resource_keys, "required_resource_keys", of_type=str)
)
outs = check.opt_mapping_param(outs, "outs", key_type=str, value_type=Out)
ins = check.opt_mapping_param(ins, "ins", key_type=str, value_type=In)
if output_notebook_name is not None:
io_mgr_key = check.opt_str_param(
io_manager_key, "io_manager_key", default="output_notebook_io_manager"
)
required_resource_keys.add(io_mgr_key)
outs = {
**outs,
cast(str, output_notebook_name): Out(io_manager_key=io_mgr_key),
}
if isinstance(asset_key_prefix, str):
asset_key_prefix = [asset_key_prefix]
asset_key_prefix = check.opt_list_param(asset_key_prefix, "asset_key_prefix", of_type=str)
default_description = f"This op is backed by the notebook at {notebook_path}"
description = check.opt_str_param(description, "description", default=default_description)
user_tags = normalize_tags(tags).tags
if tags is not None:
check.invariant(
"notebook_path" not in tags,
"user-defined op tags contains the `notebook_path` key, but the `notebook_path` key"
" is reserved for use by Dagster",
)
check.invariant(
"kind" not in tags,
"user-defined op tags contains the `kind` key, but the `kind` key is reserved for"
" use by Dagster",
)
default_tags = {"notebook_path": _clean_path_for_windows(notebook_path), "kind": "ipynb"}
if safe_is_subclass(config_schema, Config):
config_schema = infer_schema_from_config_class(cast(Type[Config], config_schema))
return OpDefinition(
name=name,
compute_fn=_make_dagstermill_compute_fn(
"define_dagstermill_op",
name,
notebook_path,
output_notebook_name,
asset_key_prefix=asset_key_prefix,
save_notebook_on_failure=save_notebook_on_failure,
),
ins=ins,
outs=outs,
config_schema=config_schema,
required_resource_keys=required_resource_keys,
description=description,
tags={**user_tags, **default_tags},
)