# 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.
# flake8: noqa - this is until we take care of the F401 violations with respect to __all__ & sphinx
__all__ = [
"get_version",
"set_environment",
"code_to_function",
"import_function",
"handler",
"ArtifactType",
]
import getpass
from os import environ, path
import dotenv
from .config import config as mlconf
from .datastore import DataItem, store_manager
from .db import get_run_db
from .errors import MLRunInvalidArgumentError, MLRunNotFoundError
from .execution import MLClientCtx
from .model import NewTask, RunObject, RunTemplate, new_task
from .platforms import (
VolumeMount,
auto_mount,
mount_v3io,
mount_v3io_extended,
mount_v3io_legacy,
v3io_cred,
)
from .projects import (
ProjectMetadata,
build_function,
deploy_function,
get_or_create_project,
load_project,
new_project,
pipeline_context,
run_function,
)
from .projects.project import _add_username_to_project_name_if_needed
from .run import (
ArtifactType,
code_to_function,
function_to_module,
get_dataitem,
get_object,
get_or_create_ctx,
get_pipeline,
handler,
import_function,
new_function,
run_local,
run_pipeline,
wait_for_pipeline_completion,
)
from .runtimes import new_model_server
from .utils.version import Version
__version__ = Version().get()["version"]
[docs]def get_version():
"""get current mlrun version"""
return __version__
if "IGZ_NAMESPACE_DOMAIN" in environ:
igz_domain = environ["IGZ_NAMESPACE_DOMAIN"]
kfp_ep = f"https://dashboard.{igz_domain}/pipelines"
environ["KF_PIPELINES_UI_ENDPOINT"] = kfp_ep
mlconf.remote_host = mlconf.remote_host or igz_domain
[docs]def set_environment(
api_path: str = None,
artifact_path: str = "",
project: str = "",
access_key: str = None,
user_project=False,
username: str = None,
):
"""set and test default config for: api path, artifact_path and project
this function will try and read the configuration from the environment/api
and merge it with the user provided project name, artifacts path or api path/access_key.
it returns the configured artifacts path, this can be used to define sub paths.
Note: the artifact path is an mlrun data uri (e.g. `s3://bucket/path`) and can not be used with file utils.
example::
from os import path
project_name, artifact_path = set_environment(project='my-project')
set_environment("http://localhost:8080", artifact_path="./")
set_environment("<remote-service-url>", access_key="xyz", username="joe")
:param api_path: location/url of mlrun api service
:param artifact_path: path/url for storing experiment artifacts
:param project: default project name
:param access_key: set the remote cluster access key (V3IO_ACCESS_KEY)
:param user_project: add the current user name to the provided project name (making it unique per user)
:param username: name of the user to authenticate
:returns:
default project name
actual artifact path/url, can be used to create subpaths per task or group of artifacts
"""
# set before the dbpath (so it will re-connect with the new credentials)
if access_key:
environ["V3IO_ACCESS_KEY"] = access_key
if username:
environ["V3IO_USERNAME"] = username
mlconf.dbpath = mlconf.dbpath or api_path
if not mlconf.dbpath:
raise ValueError("DB/API path was not detected, please specify its address")
# check connectivity and load remote defaults
get_run_db()
if api_path:
environ["MLRUN_DBPATH"] = mlconf.dbpath
project = _add_username_to_project_name_if_needed(project, user_project)
if project:
ProjectMetadata.validate_project_name(project)
mlconf.default_project = project or mlconf.default_project
# We want to ensure the project exists, and verify we're authorized to work on it
# if it doesn't exist this will create it (and obviously if we created it, we're authorized to work on it)
# if it does exist - this will get it, which will fail if we're not authorized to work on it
if project:
get_or_create_project(mlconf.default_project, "./")
if not mlconf.artifact_path and not artifact_path:
raise ValueError("please specify a valid artifact_path")
if artifact_path:
if artifact_path.startswith("./"):
artifact_path = path.abspath(artifact_path)
elif not artifact_path.startswith("/") and "://" not in artifact_path:
raise ValueError(
"artifact_path must refer to an absolute path" " or a valid url"
)
mlconf.artifact_path = artifact_path
return mlconf.default_project, mlconf.artifact_path
def get_current_project(silent=False):
if not pipeline_context.project and not silent:
raise MLRunInvalidArgumentError(
"current project is not initialized, use new, get or load project methods first"
)
return pipeline_context.project
def get_sample_path(subpath=""):
"""
return the url of a sample dataset or model
"""
samples_path = environ.get(
"SAMPLE_DATA_SOURCE_URL_PREFIX", mlconf.default_samples_path
)
if subpath:
samples_path = path.join(samples_path, subpath.lstrip("/"))
return samples_path
def set_env_from_file(env_file: str, return_dict: bool = False):
"""Read and set and/or return environment variables from a file
the env file should have lines in the form KEY=VALUE, comment line start with "#"
example file::
# this is an env file
MLRUN_DBPATH=https://mlrun-api.default-tenant.app.xxx.iguazio-cd1.com
V3IO_USERNAME=admin
V3IO_API=https://webapi.default-tenant.app.xxx.iguazio-cd1.com
V3IO_ACCESS_KEY=MYKEY123
AWS_ACCESS_KEY_ID-XXXX
AWS_SECRET_ACCESS_KEY=YYYY
usage::
# set the env vars from a file + return the results as a dict
env_dict = mlrun.set_env_from_file(env_path, return_dict=True)
:param env_file: path/url to env file
:param return_dict: set to True to return the env as a dict
:return: None or env dict
"""
env_file = path.expanduser(env_file)
if not path.isfile(env_file):
raise MLRunNotFoundError(f"env file {env_file} does not exist")
env_vars = dotenv.dotenv_values(env_file)
if None in env_vars.values():
raise MLRunInvalidArgumentError("env file lines must be in the form key=value")
for key, value in env_vars.items():
environ[key] = value # Load to local environ
mlconf.reload() # reload mlrun configuration
return env_vars if return_dict else None
