# Copyright 2018 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import re
import time
import warnings
from collections import OrderedDict
from copy import deepcopy
from datetime import datetime
from os import environ
from typing import Dict, List, Optional, Tuple, Union
import mlrun
from .utils import dict_to_json, dict_to_yaml, get_artifact_target
# Changing {run_id} will break and will not be backward compatible.
RUN_ID_PLACE_HOLDER = "{run_id}" # IMPORTANT: shouldn't be changed.
class ModelObj:
_dict_fields = []
@staticmethod
def _verify_list(param, name):
if not isinstance(param, list):
raise ValueError(f"parameter {name} must be a list")
@staticmethod
def _verify_dict(param, name, new_type=None):
if (
param is not None
and not isinstance(param, dict)
and not hasattr(param, "to_dict")
):
raise ValueError(f"parameter {name} must be a dict or object")
if new_type and (isinstance(param, dict) or param is None):
return new_type.from_dict(param)
return param
def to_dict(self, fields=None, exclude=None):
"""convert the object to a python dictionary"""
struct = {}
fields = fields or self._dict_fields
if not fields:
fields = list(inspect.signature(self.__init__).parameters.keys())
for t in fields:
if not exclude or t not in exclude:
val = getattr(self, t, None)
if val is not None and not (isinstance(val, dict) and not val):
if hasattr(val, "to_dict"):
val = val.to_dict()
if val:
struct[t] = val
else:
struct[t] = val
return struct
@classmethod
def from_dict(cls, struct=None, fields=None, deprecated_fields: dict = None):
"""create an object from a python dictionary"""
struct = {} if struct is None else struct
deprecated_fields = deprecated_fields or {}
fields = fields or cls._dict_fields
if not fields:
fields = list(inspect.signature(cls.__init__).parameters.keys())
new_obj = cls()
if struct:
# we are looping over the fields to save the same order and behavior in which the class
# initialize the attributes
for field in fields:
# we want to set the field only if the field exists in struct
if field in struct:
field_val = struct.get(field, None)
if field not in deprecated_fields:
setattr(new_obj, field, field_val)
for deprecated_field, new_field in deprecated_fields.items():
field_value = struct.get(new_field) or struct.get(deprecated_field)
if field_value:
setattr(new_obj, new_field, field_value)
return new_obj
def to_yaml(self):
"""convert the object to yaml"""
return dict_to_yaml(self.to_dict())
def to_json(self):
"""convert the object to json"""
return dict_to_json(self.to_dict())
def to_str(self):
"""convert the object to string (with dict layout)"""
return self.__str__()
def __str__(self):
return str(self.to_dict())
def copy(self):
"""create a copy of the object"""
return deepcopy(self)
# model class for building ModelObj dictionaries
class ObjectDict:
def __init__(self, classes_map, default_kind=""):
self._children = OrderedDict()
self._default_kind = default_kind
self._classes_map = classes_map
def values(self):
return self._children.values()
def keys(self):
return self._children.keys()
def items(self):
return self._children.items()
def __len__(self):
return len(self._children)
def __iter__(self):
yield from self._children.keys()
def __getitem__(self, name):
return self._children[name]
def __setitem__(self, key, item):
self._children[key] = self._get_child_object(item, key)
def __delitem__(self, key):
del self._children[key]
def update(self, key, item):
child = self._get_child_object(item, key)
self._children[key] = child
return child
def to_dict(self):
return {k: v.to_dict() for k, v in self._children.items()}
@classmethod
def from_dict(cls, classes_map: dict, children=None, default_kind=""):
if children is None:
return cls(classes_map, default_kind)
if not isinstance(children, dict):
raise ValueError("children must be a dict")
new_obj = cls(classes_map, default_kind)
for name, child in children.items():
child_obj = new_obj._get_child_object(child, name)
new_obj._children[name] = child_obj
return new_obj
def _get_child_object(self, child, name):
if hasattr(child, "kind") and child.kind in self._classes_map.keys():
child.name = name
return child
elif isinstance(child, dict):
kind = child.get("kind", self._default_kind)
if kind not in self._classes_map.keys():
raise ValueError(f"illegal object kind {kind}")
child_obj = self._classes_map[kind].from_dict(child)
child_obj.name = name
return child_obj
else:
raise ValueError(f"illegal child (should be dict or child kind), {child}")
def to_yaml(self):
return dict_to_yaml(self.to_dict())
def to_json(self):
return dict_to_json(self.to_dict())
def to_str(self):
return self.__str__()
def __str__(self):
return str(self.to_dict())
def copy(self):
return deepcopy(self)
class ObjectList:
def __init__(self, child_class):
self._children = OrderedDict()
self._child_class = child_class
def values(self):
return self._children.values()
def keys(self):
return self._children.keys()
def items(self):
return self._children.items()
def __len__(self):
return len(self._children)
def __iter__(self):
yield from self._children.values()
def __getitem__(self, name):
if isinstance(name, int):
return list(self._children.values())[name]
return self._children[name]
def __setitem__(self, key, item):
self.update(item, key)
def __delitem__(self, key):
del self._children[key]
def to_dict(self):
# method used by ModelObj class to serialize the object to nested dict
return [t.to_dict() for t in self._children.values()]
@classmethod
def from_list(cls, child_class, children=None):
if children is None:
return cls(child_class)
if not isinstance(children, list):
raise ValueError("states must be a list")
new_obj = cls(child_class)
for child in children:
name, child_obj = new_obj._get_child_object(child)
new_obj._children[name] = child_obj
return new_obj
def _get_child_object(self, child):
if isinstance(child, self._child_class):
return child.name, child
elif isinstance(child, dict):
if "name" not in child.keys():
raise ValueError("illegal object no 'name' field")
child_obj = self._child_class.from_dict(child)
return child_obj.name, child_obj
else:
raise ValueError(f"illegal child (should be dict or child kind), {child}")
def update(self, child, name=None):
object_name, child_obj = self._get_child_object(child)
child_obj.name = name or object_name
self._children[child_obj.name] = child_obj
return child_obj
class Credentials(ModelObj):
generate_access_key = "$generate"
secret_reference_prefix = "$ref:"
def __init__(
self,
access_key=None,
):
self.access_key = access_key
class BaseMetadata(ModelObj):
def __init__(
self,
name=None,
tag=None,
hash=None,
namespace=None,
project=None,
labels=None,
annotations=None,
categories=None,
updated=None,
credentials=None,
):
self.name = name
self.tag = tag
self.hash = hash
self.namespace = namespace
self.project = project or ""
self.labels = labels or {}
self.categories = categories or []
self.annotations = annotations or {}
self.updated = updated
self._credentials = None
self.credentials = credentials
@property
def credentials(self) -> Credentials:
return self._credentials
@credentials.setter
def credentials(self, credentials):
self._credentials = self._verify_dict(credentials, "credentials", Credentials)
class ImageBuilder(ModelObj):
"""An Image builder"""
def __init__(
self,
functionSourceCode=None,
source=None,
image=None,
base_image=None,
commands=None,
extra=None,
secret=None,
code_origin=None,
registry=None,
load_source_on_run=None,
origin_filename=None,
with_mlrun=None,
auto_build=None,
):
self.functionSourceCode = functionSourceCode #: functionSourceCode
self.codeEntryType = "" #: codeEntryType
self.codeEntryAttributes = "" #: codeEntryAttributes
self.source = source #: source
self.code_origin = code_origin #: code_origin
self.origin_filename = origin_filename
self.image = image #: image
self.base_image = base_image #: base_image
self.commands = commands or [] #: commands
self.extra = extra #: extra
self.secret = secret #: secret
self.registry = registry #: registry
self.load_source_on_run = load_source_on_run #: load_source_on_run
self.with_mlrun = with_mlrun #: with_mlrun
self.auto_build = auto_build #: auto_build
self.build_pod = None
class HyperParamStrategies:
grid = "grid"
list = "list"
random = "random"
custom = "custom"
@staticmethod
def all():
return [
HyperParamStrategies.grid,
HyperParamStrategies.list,
HyperParamStrategies.random,
HyperParamStrategies.custom,
]
[docs]class HyperParamOptions(ModelObj):
"""Hyper Parameter Options
Parameters:
param_file (str): hyper params input file path/url, instead of inline
strategy (str): hyper param strategy - grid, list or random
selector (str): selection criteria for best result ([min|max.]<result>), e.g. max.accuracy
stop_condition (str): early stop condition e.g. "accuracy > 0.9"
parallel_runs (int): number of param combinations to run in parallel (over Dask)
dask_cluster_uri (str): db uri for a deployed dask cluster function, e.g. db://myproject/dask
max_iterations (int): max number of runs (in random strategy)
max_errors (int): max number of child runs errors for the overall job to fail
teardown_dask (bool): kill the dask cluster pods after the runs
"""
def __init__(
self,
param_file=None,
strategy=None,
selector: HyperParamStrategies = None,
stop_condition=None,
parallel_runs=None,
dask_cluster_uri=None,
max_iterations=None,
max_errors=None,
teardown_dask=None,
):
self.param_file = param_file
self.strategy = strategy
self.selector = selector
self.stop_condition = stop_condition
self.max_iterations = max_iterations
self.max_errors = max_errors
self.parallel_runs = parallel_runs
self.dask_cluster_uri = dask_cluster_uri
self.teardown_dask = teardown_dask
def validate(self):
if self.strategy and self.strategy not in HyperParamStrategies.all():
raise mlrun.errors.MLRunInvalidArgumentError(
f"illegal hyper param strategy, use {','.join(HyperParamStrategies.all())}"
)
if self.max_iterations and self.strategy != HyperParamStrategies.random:
raise mlrun.errors.MLRunInvalidArgumentError(
"max_iterations is only valid in random strategy"
)
[docs]class RunSpec(ModelObj):
"""Run specification"""
def __init__(
self,
parameters=None,
hyperparams=None,
param_file=None,
selector=None,
handler=None,
inputs=None,
outputs=None,
input_path=None,
output_path=None,
function=None,
secret_sources=None,
data_stores=None,
strategy=None,
verbose=None,
scrape_metrics=None,
hyper_param_options=None,
allow_empty_resources=None,
):
self._hyper_param_options = None
self._inputs = inputs
self._outputs = outputs
self.hyper_param_options = hyper_param_options
self.parameters = parameters or {}
self.hyperparams = hyperparams or {}
self.param_file = param_file
self.strategy = strategy
self.selector = selector
self.handler = handler
self.input_path = input_path
self.output_path = output_path
self.function = function
self._secret_sources = secret_sources or []
self._data_stores = data_stores
self.verbose = verbose
self.scrape_metrics = scrape_metrics
self.allow_empty_resources = allow_empty_resources
[docs] def to_dict(self, fields=None, exclude=None):
struct = super().to_dict(fields, exclude=["handler"])
if self.handler and isinstance(self.handler, str):
struct["handler"] = self.handler
return struct
def is_hyper_job(self):
param_file = self.param_file or self.hyper_param_options.param_file
return param_file or self.hyperparams
@property
def inputs(self):
return self._inputs
@inputs.setter
def inputs(self, inputs):
self._inputs = self._verify_dict(inputs, "inputs")
@property
def hyper_param_options(self) -> HyperParamOptions:
return self._hyper_param_options
@hyper_param_options.setter
def hyper_param_options(self, hyper_param_options):
self._hyper_param_options = self._verify_dict(
hyper_param_options, "hyper_param_options", HyperParamOptions
)
@property
def outputs(self):
return self._outputs
@outputs.setter
def outputs(self, outputs):
self._verify_list(outputs, "outputs")
self._outputs = outputs
@property
def secret_sources(self):
return self._secret_sources
@secret_sources.setter
def secret_sources(self, secret_sources):
self._verify_list(secret_sources, "secret_sources")
self._secret_sources = secret_sources
@property
def data_stores(self):
return self._data_stores
@data_stores.setter
def data_stores(self, data_stores):
self._verify_list(data_stores, "data_stores")
self._data_stores = data_stores
@property
def handler_name(self):
if self.handler:
if inspect.isfunction(self.handler):
return self.handler.__name__
else:
return str(self.handler)
return ""
[docs]class RunStatus(ModelObj):
"""Run status"""
def __init__(
self,
state=None,
error=None,
host=None,
commit=None,
status_text=None,
results=None,
artifacts=None,
start_time=None,
last_update=None,
iterations=None,
ui_url=None,
):
self.state = state or "created"
self.status_text = status_text
self.error = error
self.host = host
self.commit = commit
self.results = results
self.artifacts = artifacts
self.start_time = start_time
self.last_update = last_update
self.iterations = iterations
self.ui_url = ui_url
[docs]class RunTemplate(ModelObj):
"""Run template"""
def __init__(self, spec: RunSpec = None, metadata: RunMetadata = None):
self._spec = None
self._metadata = None
self.spec = spec
self.metadata = metadata
@property
def spec(self) -> RunSpec:
return self._spec
@spec.setter
def spec(self, spec):
self._spec = self._verify_dict(spec, "spec", RunSpec)
@property
def metadata(self) -> RunMetadata:
return self._metadata
@metadata.setter
def metadata(self, metadata):
self._metadata = self._verify_dict(metadata, "metadata", RunMetadata)
[docs] def with_params(self, **kwargs):
"""set task parameters using key=value, key2=value2, .."""
self.spec.parameters = kwargs
return self
[docs] def with_hyper_params(
self,
hyperparams,
selector=None,
strategy: HyperParamStrategies = None,
**options,
):
"""set hyper param values and configurations,
see parameters in: :py:class:`HyperParamOptions`
example::
grid_params = {"p1": [2,4,1], "p2": [10,20]}
task = mlrun.new_task("grid-search")
task.with_hyper_params(grid_params, selector="max.accuracy")
"""
self.spec.hyperparams = hyperparams
self.spec.hyper_param_options = options
self.spec.hyper_param_options.selector = selector
self.spec.hyper_param_options.strategy = strategy
self.spec.hyper_param_options.validate()
return self
[docs] def with_param_file(
self,
param_file,
selector=None,
strategy: HyperParamStrategies = None,
**options,
):
"""set hyper param values (from a file url) and configurations,
see parameters in: :py:class:`HyperParamOptions`
example::
grid_params = "s3://<my-bucket>/path/to/params.json"
task = mlrun.new_task("grid-search")
task.with_param_file(grid_params, selector="max.accuracy")
"""
self.spec.hyper_param_options = options
self.spec.hyper_param_options.param_file = param_file
self.spec.hyper_param_options.selector = selector
self.spec.hyper_param_options.strategy = strategy
self.spec.hyper_param_options.validate()
return self
[docs] def with_secrets(self, kind, source):
"""register a secrets source (file, env or dict)
read secrets from a source provider to be used in workflows, example::
task.with_secrets('file', 'file.txt')
task.with_secrets('inline', {'key': 'val'})
task.with_secrets('env', 'ENV1,ENV2')
task.with_secrets('vault', ['secret1', 'secret2'...])
# If using with k8s secrets, the k8s secret is managed by MLRun, through the project-secrets
# mechanism. The secrets will be attached to the running pod as environment variables.
task.with_secrets('kubernetes', ['secret1', 'secret2'])
# If using an empty secrets list [] then all accessible secrets will be available.
task.with_secrets('vault', [])
# To use with Azure key vault, a k8s secret must be created with the following keys:
# kubectl -n <namespace> create secret generic azure-key-vault-secret \\
# --from-literal=tenant_id=<service principal tenant ID> \\
# --from-literal=client_id=<service principal client ID> \\
# --from-literal=secret=<service principal secret key>
task.with_secrets('azure_vault', {
'name': 'my-vault-name',
'k8s_secret': 'azure-key-vault-secret',
# An empty secrets list may be passed ('secrets': []) to access all vault secrets.
'secrets': ['secret1', 'secret2'...]
})
:param kind: secret type (file, inline, env)
:param source: secret data or link (see example)
:returns: The RunTemplate object
"""
if kind == "vault" and isinstance(source, list):
source = {"project": self.metadata.project, "secrets": source}
self.spec.secret_sources.append({"kind": kind, "source": source})
return self
[docs] def set_label(self, key, value):
"""set a key/value label for the task"""
self.metadata.labels[key] = str(value)
return self
def to_env(self):
environ["MLRUN_EXEC_CONFIG"] = self.to_json()
[docs]class RunObject(RunTemplate):
"""A run"""
def __init__(
self,
spec: RunSpec = None,
metadata: RunMetadata = None,
status: RunStatus = None,
):
super().__init__(spec, metadata)
self._status = None
self.status = status
self.outputs_wait_for_completion = True
@classmethod
def from_template(cls, template: RunTemplate):
return cls(template.spec, template.metadata)
@property
def status(self) -> RunStatus:
return self._status
@status.setter
def status(self, status):
self._status = self._verify_dict(status, "status", RunStatus)
[docs] def output(self, key):
"""return the value of a specific result or artifact by key"""
if self.outputs_wait_for_completion:
self.wait_for_completion()
if self.status.results and key in self.status.results:
return self.status.results.get(key)
artifact = self._artifact(key)
if artifact:
return get_artifact_target(artifact, self.metadata.project)
return None
@property
def ui_url(self) -> str:
"""UI URL (for relevant runtimes)"""
self.refresh()
if not self._status.ui_url:
print("UI currently not available (status={})".format(self._status.state))
return self._status.ui_url
@property
def outputs(self):
"""return a dict of outputs, result values and artifact uris"""
outputs = {}
if self.outputs_wait_for_completion:
self.wait_for_completion()
if self.status.results:
outputs = {k: v for k, v in self.status.results.items()}
if self.status.artifacts:
for a in self.status.artifacts:
outputs[a["key"]] = get_artifact_target(a, self.metadata.project)
return outputs
[docs] def artifact(self, key) -> "mlrun.DataItem":
"""return artifact DataItem by key"""
if self.outputs_wait_for_completion:
self.wait_for_completion()
artifact = self._artifact(key)
if artifact:
uri = get_artifact_target(artifact, self.metadata.project)
if uri:
return mlrun.get_dataitem(uri)
return None
def _artifact(self, key):
"""return artifact DataItem by key"""
if self.status.artifacts:
for a in self.status.artifacts:
if a["key"] == key:
return a
return None
[docs] def uid(self):
"""run unique id"""
return self.metadata.uid
[docs] def state(self):
"""current run state"""
if self.status.state in mlrun.runtimes.constants.RunStates.terminal_states():
return self.status.state
self.refresh()
return self.status.state or "unknown"
[docs] def refresh(self):
"""refresh run state from the db"""
db = mlrun.get_run_db()
run = db.read_run(
uid=self.metadata.uid,
project=self.metadata.project,
iter=self.metadata.iteration,
)
if run:
self.status = RunStatus.from_dict(run.get("status", {}))
self.status.from_dict(run.get("status", {}))
return self
[docs] def show(self):
"""show the current status widget, in jupyter notebook"""
db = mlrun.get_run_db()
db.list_runs(uid=self.metadata.uid, project=self.metadata.project).show()
[docs] def logs(self, watch=True, db=None):
"""return or watch on the run logs"""
if not db:
db = mlrun.get_run_db()
if not db:
print("DB is not configured, cannot show logs")
return None
if db.kind == "http":
state = db.watch_log(self.metadata.uid, self.metadata.project, watch=watch)
else:
state, text = db.get_log(self.metadata.uid, self.metadata.project)
if text:
print(text.decode())
if state:
print(f"final state: {state}")
return state
[docs] def wait_for_completion(self, sleep=3, timeout=0, raise_on_failure=True):
"""wait for async run to complete"""
total_time = 0
while True:
state = self.state()
if state in mlrun.runtimes.constants.RunStates.terminal_states():
break
time.sleep(sleep)
total_time += sleep
if timeout and total_time > timeout:
raise mlrun.errors.MLRunTimeoutError(
"Run did not reach terminal state on time"
)
if raise_on_failure and state != mlrun.runtimes.constants.RunStates.completed:
self.logs(watch=False)
raise mlrun.errors.MLRunRuntimeError(
f"task {self.metadata.name} did not complete (state={state})"
)
return state
@staticmethod
def create_uri(project: str, uid: str, iteration: Union[int, str], tag: str = ""):
if tag:
tag = f":{tag}"
iteration = str(iteration)
return f"{project}@{uid}#{iteration}{tag}"
@staticmethod
def parse_uri(uri: str) -> Tuple[str, str, str, str]:
uri_pattern = (
r"^(?P<project>.*)@(?P<uid>.*)\#(?P<iteration>.*?)(:(?P<tag>.*))?$"
)
match = re.match(uri_pattern, uri)
if not match:
raise ValueError(
"Uri not in supported format <project>@<uid>#<iteration>[:tag]"
)
group_dict = match.groupdict()
return (
group_dict["project"],
group_dict["uid"],
group_dict["iteration"],
group_dict["tag"],
)
class EntrypointParam(ModelObj):
def __init__(
self,
name="",
type=None,
default=None,
doc="",
required=None,
choices: list = None,
):
self.name = name
self.type = type
self.default = default
self.doc = doc
self.required = required
self.choices = choices
class FunctionEntrypoint(ModelObj):
def __init__(self, name="", doc="", parameters=None, outputs=None, lineno=-1):
self.name = name
self.doc = doc
self.parameters = [] if parameters is None else parameters
self.outputs = [] if outputs is None else outputs
self.lineno = lineno
# TODO: remove in 0.9.0
[docs]def NewTask(
name=None,
project=None,
handler=None,
params=None,
hyper_params=None,
param_file=None,
selector=None,
strategy=None,
inputs=None,
outputs=None,
in_path=None,
out_path=None,
artifact_path=None,
secrets=None,
base=None,
):
"""Creates a new task - see new_task"""
warnings.warn(
"NewTask will be deprecated in 0.7.0, and will be removed in 0.9.0, use new_task instead",
# TODO: In 0.7.0 and replace NewTask to new_task in examples & demos
PendingDeprecationWarning,
)
return new_task(
name,
project,
handler,
params,
hyper_params,
param_file,
selector,
strategy,
inputs,
outputs,
in_path,
out_path,
artifact_path,
secrets,
base,
)
[docs]def new_task(
name=None,
project=None,
handler=None,
params=None,
hyper_params=None,
param_file=None,
selector=None,
hyper_param_options=None,
inputs=None,
outputs=None,
in_path=None,
out_path=None,
artifact_path=None,
secrets=None,
base=None,
) -> RunTemplate:
"""Creates a new task
:param name: task name
:param project: task project
:param handler: code entry-point/handler name
:param params: input parameters (dict)
:param hyper_params: dictionary of hyper parameters and list values, each
hyper param holds a list of values, the run will be
executed for every parameter combination (GridSearch)
:param param_file: a csv file with parameter combinations, first row hold
the parameter names, following rows hold param values
:param selector: selection criteria for hyper params e.g. "max.accuracy"
:param hyper_param_options: hyper parameter options, see: :py:class:`HyperParamOptions`
:param inputs: dictionary of input objects + optional paths (if path is
omitted the path will be the in_path/key)
:param outputs: dictionary of input objects + optional paths (if path is
omitted the path will be the out_path/key)
:param in_path: default input path/url (prefix) for inputs
:param out_path: default output path/url (prefix) for artifacts
:param artifact_path: default artifact output path
:param secrets: extra secrets specs, will be injected into the runtime
e.g. ['file=<filename>', 'env=ENV_KEY1,ENV_KEY2']
:param base: task instance to use as a base instead of a fresh new task instance
"""
if base:
run = deepcopy(base)
else:
run = RunTemplate()
run.metadata.name = name or run.metadata.name
run.metadata.project = project or run.metadata.project
run.spec.handler = handler or run.spec.handler
run.spec.parameters = params or run.spec.parameters
run.spec.inputs = inputs or run.spec.inputs
run.spec.outputs = outputs or run.spec.outputs or []
run.spec.input_path = in_path or run.spec.input_path
run.spec.output_path = artifact_path or out_path or run.spec.output_path
run.spec.secret_sources = secrets or run.spec.secret_sources or []
run.spec.hyperparams = hyper_params or run.spec.hyperparams
run.spec.hyper_param_options = hyper_param_options or run.spec.hyper_param_options
run.spec.hyper_param_options.param_file = (
param_file or run.spec.hyper_param_options.param_file
)
run.spec.hyper_param_options.selector = (
selector or run.spec.hyper_param_options.selector
)
return run
[docs]class TargetPathObject:
"""Class configuring the target path
This class will take consideration of a few parameters to create the correct end result path:
* run_id - if run_id is provided target will be considered as run_id mode
which require to contain a {run_id} place holder in the path.
* is_single_file - if true then run_id must be the directory containing the output file
or generated before the file name (run_id/output.file).
* base_path - if contains the place holder for run_id, run_id must not be None.
if run_id passed and place holder doesn't exist the place holder will
be generated in the correct place.
"""
def __init__(
self,
base_path=None,
run_id=None,
is_single_file=False,
):
self.run_id = run_id
self.full_path_template = base_path
if run_id is not None:
if RUN_ID_PLACE_HOLDER not in self.full_path_template:
if not is_single_file:
if self.full_path_template[-1] != "/":
self.full_path_template = self.full_path_template + "/"
self.full_path_template = (
self.full_path_template + RUN_ID_PLACE_HOLDER + "/"
)
else:
dir_name_end = len(self.full_path_template)
if self.full_path_template[-1] != "/":
dir_name_end = self.full_path_template.rfind("/") + 1
updated_path = (
self.full_path_template[:dir_name_end]
+ RUN_ID_PLACE_HOLDER
+ "/"
+ self.full_path_template[dir_name_end:]
)
self.full_path_template = updated_path
else:
if self.full_path_template[-1] != "/":
if self.full_path_template.endswith(RUN_ID_PLACE_HOLDER):
self.full_path_template = self.full_path_template + "/"
else:
if RUN_ID_PLACE_HOLDER in self.full_path_template:
raise mlrun.errors.MLRunInvalidArgumentError(
"Error when trying to create TargetPathObject with place holder '{run_id}' but no value."
)
def get_templated_path(self):
return self.full_path_template
def get_absolute_path(self):
if self.run_id:
return self.full_path_template.format(run_id=self.run_id)
else:
return self.full_path_template
[docs]class DataSource(ModelObj):
"""online or offline data source spec"""
_dict_fields = [
"kind",
"name",
"path",
"attributes",
"key_field",
"time_field",
"schedule",
"online",
"workers",
"max_age",
"start_time",
"end_time",
]
kind = None
def __init__(
self,
name: str = None,
path: str = None,
attributes: Dict[str, str] = None,
key_field: str = None,
time_field: str = None,
schedule: str = None,
start_time: Optional[Union[datetime, str]] = None,
end_time: Optional[Union[datetime, str]] = None,
):
self.name = name
self.path = str(path) if path is not None else None
self.attributes = attributes or {}
self.schedule = schedule
self.key_field = key_field
self.time_field = time_field
self.start_time = start_time
self.end_time = end_time
self.online = None
self.max_age = None
self.workers = None
self._secrets = {}
def set_secrets(self, secrets):
self._secrets = secrets
[docs]class DataTargetBase(ModelObj):
"""data target spec, specify a destination for the feature set data"""
_dict_fields = [
"name",
"kind",
"path",
"after_step",
"attributes",
"partitioned",
"key_bucketing_number",
"partition_cols",
"time_partitioning_granularity",
"max_events",
"flush_after_seconds",
"storage_options",
"run_id",
]
# TODO - remove once "after_state" is fully deprecated
[docs] @classmethod
def from_dict(cls, struct=None, fields=None):
return super().from_dict(
struct, fields=fields, deprecated_fields={"after_state": "after_step"}
)
def get_path(self):
if self.path:
is_single_file = hasattr(self, "is_single_file") and self.is_single_file()
return TargetPathObject(self.path, self.run_id, is_single_file)
else:
return None
def __init__(
self,
kind: str = None,
name: str = "",
path=None,
attributes: Dict[str, str] = None,
after_step=None,
partitioned: bool = False,
key_bucketing_number: Optional[int] = None,
partition_cols: Optional[List[str]] = None,
time_partitioning_granularity: Optional[str] = None,
max_events: Optional[int] = None,
flush_after_seconds: Optional[int] = None,
after_state=None,
storage_options: Dict[str, str] = None,
):
if after_state:
warnings.warn(
"The after_state parameter is deprecated. Use after_step instead",
# TODO: In 0.7.0 do changes in examples & demos In 0.9.0 remove
PendingDeprecationWarning,
)
after_step = after_step or after_state
self.name = name
self.kind: str = kind
self.path = path
self.after_step = after_step
self.attributes = attributes or {}
self.last_written = None
self.partitioned = partitioned
self.key_bucketing_number = key_bucketing_number
self.partition_cols = partition_cols
self.time_partitioning_granularity = time_partitioning_granularity
self.max_events = max_events
self.flush_after_seconds = flush_after_seconds
self.storage_options = storage_options
self.run_id = None
[docs]class FeatureSetProducer(ModelObj):
"""information about the task/job which produced the feature set data"""
def __init__(self, kind=None, name=None, uri=None, owner=None, sources=None):
self.kind = kind
self.name = name
self.owner = owner
self.uri = uri
self.sources = sources or {}
[docs]class DataTarget(DataTargetBase):
"""data target with extra status information (used in the feature-set/vector status)"""
_dict_fields = [
"name",
"kind",
"path",
"start_time",
"online",
"status",
"updated",
"size",
"last_written",
"run_id",
]
def __init__(
self,
kind: str = None,
name: str = "",
path=None,
online=None,
):
super().__init__(kind, name, path)
self.status = ""
self.updated = None
self.size = None
self.online = online
self.max_age = None
self.start_time = None
self.last_written = None
self._producer = None
self.producer = {}
@property
def producer(self) -> FeatureSetProducer:
return self._producer
@producer.setter
def producer(self, producer):
self._producer = self._verify_dict(producer, "producer", FeatureSetProducer)
class VersionedObjMetadata(ModelObj):
def __init__(
self,
name: str = None,
tag: str = None,
uid: str = None,
project: str = None,
labels: Dict[str, str] = None,
annotations: Dict[str, str] = None,
updated=None,
):
self.name = name
self.tag = tag
self.uid = uid
self.project = project
self.labels = labels or {}
self.annotations = annotations or {}
self.updated = updated
