def load(bento_service_cls, path=None):
# TODO: add model.env.verify() to check dependencies and python version etc
if bento_service_cls._bento_module_path is not None:
# When calling load from pip installled bento model, use installed
# python package for loading and the same path for '/artifacts'
# TODO: warn user that 'path' parameter is ignored if it's not None here
path = bento_service_cls._bento_module_path
artifacts_path = path
else:
if path is None:
raise BentoMLException("Loading path is required for BentoArchive: {}.".format(
bento_service_cls.name()))
# When calling load on generated archive directory, look for /artifacts
# directory under module sub-directory
if is_s3_url(path):
temporary_path = tempfile.mkdtemp()
download_from_s3(path, temporary_path)
# Use loacl temp path for the following loading operations
path = temporary_path
artifacts_path = os.path.join(path, bento_service_cls.name())
bentoml_config = load_bentoml_config(path)
bento_service = bento_service_cls.load(artifacts_path)
bento_service._version = bentoml_config['service_version']
return bento_service
def resolve_bundle_path(bento, pip_installed_bundle_path):
if pip_installed_bundle_path:
assert (
bento is None
), "pip installed BentoService commands should not have Bento argument"
return pip_installed_bundle_path
if os.path.isdir(bento) or is_s3_url(bento):
# bundler already support loading local and s3 path
return bento
elif ":" in bento:
# assuming passing in BentoService in the form of Name:Version tag
yatai_client = YataiClient()
name, version = bento.split(':')
get_bento_result = yatai_client.repository.get(name, version)
if get_bento_result.status.status_code != status_pb2.Status.OK:
error_code, error_message = status_pb_to_error_code_and_message(
get_bento_result.status)
raise BentoMLException(
f'BentoService {name}:{version} not found - '
f'{error_code}:{error_message}')
return get_bento_result.bento.uri.uri
else:
raise BentoMLException(
f'BentoService "{bento}" not found - either specify the file path of '
f'the BentoService saved bundle, or the BentoService id in the form of '
f'"name:version"')
def resolve_bundle_path(bento, pip_installed_bundle_path, yatai_url=None):
from bentoml.yatai.client import get_yatai_client
from bentoml.exceptions import BentoMLException
if pip_installed_bundle_path:
assert (
bento is None
), "pip installed BentoService commands should not have Bento argument"
return pip_installed_bundle_path
if os.path.isdir(bento) or is_s3_url(bento) or is_gcs_url(bento):
# saved_bundle already support loading local, s3 path and gcs path
return bento
elif ":" in bento:
# assuming passing in BentoService in the form of Name:Version tag
yatai_client = get_yatai_client(yatai_url)
bento_pb = yatai_client.repository.get(bento)
if bento_pb.uri.s3_presigned_url:
# Use s3 presigned URL for downloading the repository if it is presented
return bento_pb.uri.s3_presigned_url
if bento_pb.uri.gcs_presigned_url:
return bento_pb.uri.gcs_presigned_url
else:
return bento_pb.uri.uri
else:
raise BentoMLException(
f'BentoService "{bento}" not found - either specify the file path of '
f"the BentoService saved bundle, or the BentoService id in the form of "
f'"name:version"')
def _resolve_remote_bundle_path(bundle_path):
if is_s3_url(bundle_path):
import boto3
parsed_url = urlparse(bundle_path)
bucket_name = parsed_url.netloc
object_name = parsed_url.path.lstrip('/')
s3 = boto3.client('s3')
fileobj = io.BytesIO()
s3.download_fileobj(bucket_name, object_name, fileobj)
fileobj.seek(0, 0)
elif _is_http_url(bundle_path):
import requests
response = requests.get(bundle_path)
fileobj = io.BytesIO()
fileobj.write(response.content)
fileobj.seek(0, 0)
else:
raise BentoMLException(
f"Saved bundle path: '{bundle_path}' is not supported")
with tarfile.open(mode="r:gz", fileobj=fileobj) as tar:
with tempfile.TemporaryDirectory() as tmpdir:
filename = tar.getmembers()[0].name
tar.extractall(path=tmpdir)
yield os.path.join(tmpdir, filename)
def load_bento_service_class(archive_path):
"""
Load a BentoService class from saved archive in given path
:param archive_path: A BentoArchive path generated from BentoService.save call
or the path to pip installed BentoArchive directory
:return: BentoService class
"""
if is_s3_url(archive_path):
tempdir = tempfile.mkdtemp()
download_from_s3(archive_path, tempdir)
archive_path = tempdir
config = load_bentoml_config(archive_path)
# Load target module containing BentoService class from given path
module_file_path = os.path.join(archive_path, config['service_name'],
config['module_file'])
if not os.path.isfile(module_file_path):
# Try loading without service_name prefix, for loading from a installed PyPi
module_file_path = os.path.join(archive_path, config['module_file'])
if not os.path.isfile(module_file_path):
raise BentoMLException(
'Can not locate module_file {} in archive {}'.format(
config['module_file'], archive_path))
# Prepend archive_path to sys.path for loading extra python dependencies
sys.path.insert(0, archive_path)
module_name = config['module_name']
if module_name in sys.modules:
# module already loaded, TODO: add warning
module = sys.modules[module_name]
elif sys.version_info >= (3, 5):
import importlib.util
spec = importlib.util.spec_from_file_location(module_name,
module_file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
elif sys.version_info >= (3, 3):
from importlib.machinery import SourceFileLoader
# pylint:disable=deprecated-method
module = SourceFileLoader(module_name,
module_file_path).load_module(module_name)
# pylint:enable=deprecated-method
else:
import imp
module = imp.load_source(module_name, module_file_path)
# Remove archive_path from sys.path to avoid import naming conflicts
sys.path.remove(archive_path)
model_service_class = module.__getattribute__(config['service_name'])
# Set _bento_archive_path, which tells BentoService where to load its artifacts
model_service_class._bento_archive_path = archive_path
# Set cls._version, service instance can access it via svc.version
model_service_class._bento_service_version = config['service_version']
return model_service_class
def resolve_bundle_path(
bento: str,
pip_installed_bundle_path: Optional[str] = None,
yatai_url: Optional[str] = None,
) -> str:
from bentoml.exceptions import BentoMLException
from bentoml.yatai.client import get_yatai_client
if pip_installed_bundle_path:
assert (
bento is None
), "pip installed BentoService commands should not have Bento argument"
return pip_installed_bundle_path
if os.path.isdir(bento) or is_s3_url(bento) or is_gcs_url(bento):
# saved_bundle already support loading local, s3 path and gcs path
return bento
elif ":" in bento:
# assuming passing in BentoService in the form of Name:Version tag
yatai_client = get_yatai_client(yatai_url)
bento_pb = yatai_client.repository.get(bento)
return resolve_bento_bundle_uri(bento_pb)
else:
raise BentoMLException(
f'BentoService "{bento}" not found - either specify the file path of '
f"the BentoService saved bundle, or the BentoService id in the form of "
f'"name:version"')
def from_archive(cls, path):
from bentoml.archive import load_bentoml_config
# TODO: add model.env.verify() to check dependencies and python version etc
if cls._bento_archive_path is not None and cls._bento_archive_path != path:
raise BentoMLException(
"Loaded BentoArchive(from {}) can't be loaded again from a different"
"archive path {}".format(cls._bento_archive_path, path))
if is_s3_url(path):
temporary_path = tempfile.mkdtemp()
download_from_s3(path, temporary_path)
# Use loacl temp path for the following loading operations
path = temporary_path
artifacts_path = path
# For pip installed BentoService, artifacts directory is located at
# 'package_path/artifacts/', but for loading from BentoArchive, it is
# in 'path/{service_name}/artifacts/'
if not os.path.isdir(os.path.join(path, 'artifacts')):
artifacts_path = os.path.join(path, cls.name())
bentoml_config = load_bentoml_config(path)
# TODO: check archive type and allow loading archive only
if bentoml_config['service_name'] != cls.name():
raise BentoMLException(
'BentoService name does not match with BentoML Archive in path: {}'
.format(path))
artifacts = ArtifactCollection.load(artifacts_path,
cls._artifacts_spec)
svc = cls(artifacts)
return svc
def save(bento_service, dst, version=None):
"""
Save given BentoService along with all artifacts to target path
"""
if version is None:
version = _generate_new_version_str()
_validate_version_str(version)
if bento_service._version_major is not None and bento_service._version_minor is not None:
# BentoML uses semantic versioning for BentoService distribution
# when user specified the MAJOR and MINOR version number along with
# the BentoService class definition with '@ver' decorator.
# The parameter version(or auto generated version) here will be used as
# PATCH field in the final version:
version = '.'.join(
[str(bento_service._version_major),
str(bento_service._version_minor), version])
# Full path containing saved BentoArchive, it the dst path with service name
# and service version as prefix. e.g.:
# - s3://my-bucket/base_path => s3://my-bucket/base_path/service_name/version/
# - /tmp/my_bento_archive/ => /tmp/my_bento_archive/service_name/version/
full_saved_path = os.path.join(dst, bento_service.name, version)
if is_s3_url(dst):
with TempDirectory() as tempdir:
_save(bento_service, tempdir, version)
upload_to_s3(full_saved_path, tempdir)
else:
_save(bento_service, dst, version)
return full_saved_path
def __init__(self, base_url=None):
if base_url is None:
base_url = config().get('default_repository_base_url')
if is_s3_url(base_url):
self._repo = _S3BentoRepository(base_url)
else:
self._repo = _LocalBentoRepository(base_url)
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("-o",
"--output",
default="str",
choices=["str", "json"])
parser.add_argument(
"--orient",
default=self.orient,
choices=PANDAS_DATAFRAME_TO_DICT_ORIENT_OPTIONS,
)
parser.add_argument(
"--output_orient",
default=self.output_orient,
choices=PANDAS_DATAFRAME_TO_DICT_ORIENT_OPTIONS,
)
parsed_args = parser.parse_args(args)
orient = parsed_args.orient
output_orient = parsed_args.output_orient
cli_input = parsed_args.input
if os.path.isfile(cli_input) or is_s3_url(cli_input) or is_url(
cli_input):
if cli_input.endswith(".csv"):
df = pd.read_csv(cli_input)
elif cli_input.endswith(".json"):
df = pd.read_json(cli_input,
orient=orient,
typ=self.typ,
dtype=False)
else:
raise BadInput(
"Input file format not supported, BentoML cli only accepts .json "
"and .csv file")
else:
# Assuming input string is JSON format
try:
df = pd.read_json(cli_input,
orient=orient,
typ=self.typ,
dtype=False)
except ValueError as e:
raise BadInput(
"Unexpected input format, BentoML DataframeHandler expects json "
"string as input: {}".format(e))
if self.typ == "frame" and self.input_dtypes is not None:
_check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
if parsed_args.output == 'json':
result = api_func_result_to_json(
result, pandas_dataframe_orient=output_orient)
else:
result = str(result)
print(result)
def yatai_service_start(
db_url,
repo_base_url,
grpc_port,
ui_port,
ui,
web_prefix_path,
repository_type,
file_system_directory,
s3_url,
s3_endpoint_url,
gcs_url,
):
from bentoml.utils.s3 import is_s3_url
from bentoml.utils.gcs import is_gcs_url
if repo_base_url:
logger.warning(
"Option --repo-base-url has been deprecated but is still supported "
"in the current release. Consider using --repository-type and its "
"corresponding options in the upcoming releases. ")
if is_s3_url(repo_base_url):
repository_type = YATAI_REPOSITORY_S3
s3_url = repo_base_url
elif is_gcs_url(repo_base_url):
repository_type = YATAI_REPOSITORY_GCS
gcs_url = repo_base_url
else:
repository_type = YATAI_REPOSITORY_FILE_SYSTEM
file_system_directory = repo_base_url
if repository_type == YATAI_REPOSITORY_S3 and s3_url is None:
logger.error("'--s3-url' must be specified for S3 repository type")
return
elif repository_type == YATAI_REPOSITORY_GCS and gcs_url is None:
logger.error(
"'--gcs-url' must be specified for GCS repository type")
return
elif (repository_type == YATAI_REPOSITORY_FILE_SYSTEM
and file_system_directory is None):
logger.error(
"'--file-system-directory' must be specified for file system "
"repository type")
return
else:
start_yatai_service_grpc_server(
db_url=db_url,
grpc_port=grpc_port,
ui_port=ui_port,
with_ui=ui,
base_url=web_prefix_path,
repository_type=repository_type,
file_system_directory=file_system_directory,
s3_url=s3_url,
s3_endpoint_url=s3_endpoint_url,
gcs_url=gcs_url,
)
def load_model_service(model_path):
if is_s3_url(model_path):
temp_dir = tempfile.mkdtemp()
download_from_s3(model_path, temp_dir)
model_path = temp_dir
else:
if not os.path.isabs(model_path):
model_path = os.path.abspath(model_path)
model_service = load(model_path)
return model_service
def __init__(self, base_url=None, s3_endpoint_url=None):
"""
:param base_url: either a local file system path or a s3-compatible path such as
s3://my-bucket/some-prefix/
:param s3_endpoint_url: configuring S3Repository to talk to a specific s3
endpoint
"""
if base_url is None:
base_url = config().get('default_repository_base_url')
if is_s3_url(base_url):
self._repo = S3Repository(base_url, s3_endpoint_url)
else:
self._repo = LocalRepository(base_url)
def save(bento_service, dst, version=None):
"""Save given BentoService along with all its artifacts, source code and
dependencies to target path
Args:
bento_service (bentoml.service.BentoService): a Bento Service instance
dst (str): Destination of where the bento service will be saved. It could
be a local file path or a s3 path
version (:obj:`str`, optional): version text to use for saved archive
Returns:
string: The complete path of saved Bento service.
"""
if version is None:
version = _generate_new_version_str()
_validate_version_str(version)
if (bento_service._version_major is not None
and bento_service._version_minor is not None):
# BentoML uses semantic versioning for BentoService distribution
# when user specified the MAJOR and MINOR version number along with
# the BentoService class definition with '@ver' decorator.
# The parameter version(or auto generated version) here will be used as
# PATCH field in the final version:
version = ".".join([
str(bento_service._version_major),
str(bento_service._version_minor),
version,
])
# Full path containing saved BentoArchive, it the dst path with service name
# and service version as prefix. e.g.:
# - s3://my-bucket/base_path => s3://my-bucket/base_path/service_name/version/
# - /tmp/my_bento_archive/ => /tmp/my_bento_archive/service_name/version/
full_saved_path = os.path.join(dst, bento_service.name, version)
if is_s3_url(dst):
with TempDirectory() as tempdir:
_save(bento_service, tempdir, version)
upload_to_s3(full_saved_path, tempdir)
else:
_save(bento_service, dst, version)
LOG.info("BentoService %s:%s saved to %s", bento_service.name, version,
full_saved_path)
return full_saved_path
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument('--input', required=True)
parser.add_argument('-o',
'--output',
default="str",
choices=['str', 'json', 'yaml'])
parser.add_argument('--orient', default=self.orient)
parser.add_argument('--output_orient', default=self.output_orient)
parsed_args = parser.parse_args(args)
orient = parsed_args.orient
output_orient = parsed_args.output_orient
cli_input = parsed_args.input
if os.path.isfile(cli_input) or is_s3_url(cli_input) or is_url(
cli_input):
if cli_input.endswith('.csv'):
df = pd.read_csv(cli_input)
elif cli_input.endswith('.json'):
df = pd.read_json(cli_input,
orient=orient,
typ=self.typ,
dtype=False)
else:
raise ValueError(
"Input file format not supported, BentoML cli only accepts .json and .csv file"
)
else:
# Assuming input string is JSON format
try:
df = pd.read_json(cli_input,
orient=orient,
typ=self.typ,
dtype=False)
except ValueError as e:
raise ValueError(
"Unexpected input format, BentoML DataframeHandler expects json string as"
"input: {}".format(e))
if self.typ == 'frame' and self.input_columns is not None:
check_dataframe_column_contains(self.input_columns, df)
result = func(df)
result = get_output_str(result, parsed_args.output, output_orient)
print(result)
def load(path, lazy_load=False):
"""
Load a BentoService or BentoModel from saved archive in given path
:param path: A BentoArchive path generated from BentoService.save call
:return: BentoService
"""
if is_s3_url(path):
tempdir = tempfile.mkdtemp()
download_from_s3(path, tempdir)
path = tempdir
config = load_bentoml_config(path)
# Load target module containing BentoService class from given path
module_file_path = os.path.join(path, config['service_name'],
config['module_file'])
module_name = config['module_name']
if module_name in sys.modules:
# module already loaded, TODO: add warning
module = sys.modules[module_name]
elif sys.version_info >= (3, 5):
import importlib.util
spec = importlib.util.spec_from_file_location(module_name,
module_file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
elif sys.version_info >= (3, 3):
from importlib.machinery import SourceFileLoader
# pylint:disable=deprecated-method
module = SourceFileLoader(module_name,
module_file_path).load_module(module_name)
# pylint:enable=deprecated-method
else:
import imp
module = imp.load_source(module_name, module_file_path)
model_service_class = module.__getattribute__(config['service_name'])
loaded_model = _LoadedBentoServiceWrapper(model_service_class, path,
config)
if not lazy_load:
loaded_model.load()
return loaded_model
def _resolve_remote_bundle_path(bundle_path):
if is_s3_url(bundle_path):
import boto3
parsed_url = urlparse(bundle_path)
bucket_name = parsed_url.netloc
object_name = parsed_url.path.lstrip('/')
s3 = boto3.client('s3')
fileobj = io.BytesIO()
s3.download_fileobj(bucket_name, object_name, fileobj)
fileobj.seek(0, 0)
elif is_gcs_url(bundle_path):
try:
from google.cloud import storage
except ImportError:
raise BentoMLException(
'"google-cloud-storage" package is required. You can install it with '
'pip: "pip install google-cloud-storage"'
)
gcs = storage.Client()
fileobj = io.BytesIO()
gcs.download_blob_to_file(bundle_path, fileobj)
fileobj.seek(0, 0)
elif _is_http_url(bundle_path):
import requests
response = requests.get(bundle_path)
if response.status_code != 200:
raise BentoMLException(
f"Error retrieving BentoService bundle. "
f"{response.status_code}: {response.text}"
)
fileobj = io.BytesIO()
fileobj.write(response.content)
fileobj.seek(0, 0)
else:
raise BentoMLException(f"Saved bundle path: '{bundle_path}' is not supported")
with tarfile.open(mode="r:gz", fileobj=fileobj) as tar:
with tempfile.TemporaryDirectory() as tmpdir:
filename = tar.getmembers()[0].name
tar.extractall(path=tmpdir)
yield os.path.join(tmpdir, filename)
def resolve_bundle_path(bento, pip_installed_bundle_path):
from bentoml.exceptions import BentoMLException
if pip_installed_bundle_path:
assert (
bento is None
), "pip installed BentoService commands should not have Bento argument"
return pip_installed_bundle_path
if os.path.isdir(bento) or is_s3_url(bento) or is_gcs_url(bento):
# saved_bundle already support loading local, s3 path and gcs path
return bento
elif ":" in bento:
# assuming passing in BentoService in the form of Name:Version tag
yatai_client = get_default_yatai_client()
name, version = bento.split(":")
get_bento_result = yatai_client.repository.get(name, version)
if get_bento_result.status.status_code != yatai_proto.status_pb2.Status.OK:
error_code, error_message = status_pb_to_error_code_and_message(
get_bento_result.status
)
raise BentoMLException(
f"BentoService {name}:{version} not found - "
f"{error_code}:{error_message}"
)
if get_bento_result.bento.uri.s3_presigned_url:
# Use s3 presigned URL for downloading the repository if it is presented
return get_bento_result.bento.uri.s3_presigned_url
if get_bento_result.bento.uri.gcs_presigned_url:
return get_bento_result.bento.uri.gcs_presigned_url
else:
return get_bento_result.bento.uri.uri
else:
raise BentoMLException(
f'BentoService "{bento}" not found - either specify the file path of '
f"the BentoService saved bundle, or the BentoService id in the form of "
f'"name:version"'
)
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument(
"--orient",
default=self.orient,
choices=PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS,
)
parsed_args, unknown_args = parser.parse_known_args(args)
orient = parsed_args.orient
cli_input = parsed_args.input
if os.path.isfile(cli_input) or is_s3_url(cli_input) or is_url(cli_input):
if cli_input.endswith(".csv"):
df = pd.read_csv(cli_input)
elif cli_input.endswith(".json"):
df = pd.read_json(cli_input, orient=orient, typ=self.typ)
else:
raise BadInput(
"Input file format not supported, BentoML cli only accepts .json "
"and .csv file"
)
else:
# Assuming input string is JSON format
try:
df = pd.read_json(cli_input, orient=orient, typ=self.typ)
except ValueError as e:
raise BadInput(
"Unexpected input format, BentoML DataframeInput expects json "
"string as input: {}".format(e)
)
if self.typ == "frame" and self.input_dtypes is not None:
check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
self.output_adapter.to_cli(result, unknown_args)
def save(bento_service, dst, version=None, pypi_package_version="1.0.0"):
"""
Save given BentoService along with all artifacts to target path
"""
if version is None:
version = _generate_new_version_str()
_validate_version_str(version)
# Full path containing saved BentoArchive, it the dst path with service name
# and service version as prefix. e.g.:
# - s3://my-bucket/base_path => s3://my-bucket/base_path/service_name/version/
# - /tmp/my_bento_archive/ => /tmp/my_bento_archive/service_name/version/
full_saved_path = os.path.join(dst, bento_service.name, version)
if is_s3_url(dst):
with TempDirectory() as tempdir:
_save(bento_service, tempdir, version, pypi_package_version)
upload_to_s3(full_saved_path, tempdir)
else:
_save(bento_service, dst, version, pypi_package_version)
return full_saved_path
def from_archive(cls, path):
from bentoml.archive import load_bentoml_config
if cls._bento_archive_path is not None and cls._bento_archive_path != path:
raise BentoMLException(
"Loaded BentoArchive(from {}) can't be loaded again from a different"
"archive path {}".format(cls._bento_archive_path, path))
if is_s3_url(path):
temporary_path = tempfile.mkdtemp()
download_from_s3(path, temporary_path)
# Use loacl temp path for the following loading operations
path = temporary_path
artifacts_path = path
# For pip installed BentoService, artifacts directory is located at
# 'package_path/artifacts/', but for loading from BentoArchive, it is
# in 'path/{service_name}/artifacts/'
if not os.path.isdir(os.path.join(path, "artifacts")):
artifacts_path = os.path.join(path, cls.name())
bentoml_config = load_bentoml_config(path)
if bentoml_config["metadata"]["service_name"] != cls.name():
raise BentoMLException(
"BentoService name does not match with BentoArchive in path: {}"
.format(path))
if bentoml_config["kind"] != "BentoService":
raise BentoMLException(
"BentoArchive type '{}' can not be loaded as a BentoService".
format(bentoml_config["kind"]))
artifacts = ArtifactCollection.load(artifacts_path,
cls._artifacts_spec)
svc = cls(artifacts)
return svc
def _is_remote_path(bundle_path):
return is_s3_url(bundle_path)
def _is_remote_path(bundle_path):
return isinstance(bundle_path, str) and (is_s3_url(bundle_path)
or is_gcs_url(bundle_path)
or _is_http_url(bundle_path))
def _is_remote_path(bundle_path):
return (
is_s3_url(bundle_path) or is_gcs_url(bundle_path) or _is_http_url(bundle_path)
)
def save(bento_service, dst, version=None, pypi_package_version="1.0.0"):
"""
Save given BentoService along with all artifacts to target path
"""
if version is None:
version = _generate_new_version_str()
_validate_version_str(version)
s3_url = None
if is_s3_url(dst):
s3_url = os.path.join(dst, bento_service.name, version)
# TODO: check s3_url not exist, otherwise raise exception
temp_dir = tempfile.mkdtemp()
Path(temp_dir, bento_service.name).mkdir(parents=True, exist_ok=True)
# Update path to subfolder in the form of 'base/service_name/version/'
path = os.path.join(temp_dir, bento_service.name, version)
else:
Path(os.path.join(dst), bento_service.name).mkdir(parents=True, exist_ok=True)
# Update path to subfolder in the form of 'base/service_name/version/'
path = os.path.join(dst, bento_service.name, version)
if os.path.exists(path):
raise ValueError("Version {version} in Path: {dst} already "
"exist.".format(version=version, dst=dst))
os.mkdir(path)
module_base_path = os.path.join(path, bento_service.name)
os.mkdir(module_base_path)
# write README.md with user model's docstring
if bento_service.__class__.__doc__:
model_description = bento_service.__class__.__doc__.strip()
else:
model_description = DEFAULT_BENTO_ARCHIVE_DESCRIPTION
with open(os.path.join(path, 'README.md'), 'w') as f:
f.write(model_description)
# save all model artifacts to 'base_path/name/artifacts/' directory
bento_service.artifacts.save(module_base_path)
# write conda environment, requirement.txt
bento_service.env.save(path)
# TODO: add bentoml.find_packages helper for more fine grained control over
# this process, e.g. packages=find_packages(base, [], exclude=[], used_module_only=True)
# copy over all custom model code
module_name, module_file = copy_used_py_modules(bento_service.__class__.__module__,
os.path.join(path, bento_service.name))
if os.path.isabs(module_file):
module_file = module_name.replace('.', os.sep) + '.py'
# create __init__.py
with open(os.path.join(path, bento_service.name, '__init__.py'), "w") as f:
f.write(
INIT_PY_TEMPLATE.format(service_name=bento_service.name, module_name=module_name,
pypi_package_version=pypi_package_version))
# write setup.py, make exported model pip installable
setup_py_content = BENTO_MODEL_SETUP_PY_TEMPLATE.format(
name=bento_service.name, pypi_package_version=pypi_package_version,
long_description=model_description)
with open(os.path.join(path, 'setup.py'), 'w') as f:
f.write(setup_py_content)
with open(os.path.join(path, 'MANIFEST.in'), 'w') as f:
f.write(MANIFEST_IN_TEMPLATE.format(service_name=bento_service.name))
# write Dockerfile
with open(os.path.join(path, 'Dockerfile'), 'w') as f:
f.write(
BENTO_SERVER_SINGLE_MODEL_DOCKERFILE_TEMPLATE.format(
conda_env_name=bento_service.env.get_conda_env_name()))
# write bentoml.yml
bentoml_yml_content = BENTOML_CONFIG_YAML_TEMPLATE.format(
service_name=bento_service.name, bentoml_version=BENTOML_VERSION, service_version=version,
module_name=module_name, module_file=module_file, created_at=str(datetime.now()))
with open(os.path.join(path, 'bentoml.yml'), 'w') as f:
f.write(bentoml_yml_content)
# Also write bentoml.yml to module base path to make it accessible
# as package data after pip installed as a python package
with open(os.path.join(module_base_path, 'bentoml.yml'), 'w') as f:
f.write(bentoml_yml_content)
if s3_url:
upload_to_s3(s3_url, path)
return s3_url
else:
return path