def __init__(self, path: str):
path = os.path.abspath(path)
metadata, err = MLObject.create_object_from_file(path)
if err:
raise RuntimeError(err)
self.task_name: str = metadata['task_name']
# Input/output bindings map parameter name to parameter value
self.input_binding: dict = metadata['input_binding']
self.output_binding: dict = metadata['output_binding']
def __init__(self, file_path: str):
"""
Args:
file_path (str): Path to a 'mlbox_docker.yaml' that is usually located in the MLBox root directory.
"""
metadata, err = MLObject.create_object_from_file(file_path)
if err:
raise RuntimeError(err)
self.type = RuntimeType.Docker
self.image = metadata['image'] # Docker image name
self.docker = metadata['docker_runtime'] # Docker executable ('docker' or 'nvidia-docker').
def __init__(self, path: str, **kwargs: Any):
"""
Args:
path (str): Path to a Singularity platform that is usually located in the MLBox `platforms` directory.
**kwargs (Any): Reserved for future use to unify implementation of Platform Definition classes across
various runners.
"""
metadata, err = MLObject.create_object_from_file(path)
if err:
raise RuntimeError(err)
self.type: str = 'singularity'
self.image: str = metadata['image']
def __init__(self, path: str):
"""
Args:
path (str): Path to a MLBox task file.
"""
path = os.path.abspath(path)
metadata, err = MLObject.create_object_from_file(path)
if err:
raise RuntimeError(err)
self.inputs = {input_['name']: input_['type'] for input_ in metadata.get('inputs', [])}
self.outputs = {output['name']: output['type'] for output in metadata.get('outputs', [])}
def __init__(self, path: str):
"""
Args:
path (str): Path to a MLBox root directory.
"""
path = os.path.abspath(path)
metadata, err = MLObject.create_object_from_file(os.path.join(path, 'mlbox.yaml'))
if err:
raise RuntimeError(err)
self.root = path
self.name = metadata['name']
self.version = metadata['version']
self.runtime = DockerRuntime.load(path)
def __init__(self, path: str):
"""
Args:
path (str): Path to a MLBox root directory.
"""
path = os.path.abspath(path)
metadata, err = MLObject.create_object_from_file(os.path.join(path, 'mlbox.yaml'))
if err:
raise RuntimeError(err)
self.root = path
self.name = metadata['name']
self.version = metadata['version']
self.task: Optional[MLBoxTask] = None
self.invoke: Optional[MLBoxInvoke] = None
self.platform: Any = None
def parse_mlcube_task(filename):
(task, err) = MLObject.create_object_from_file(filename)
if err:
return None, err
inputs = {}
for input_dict in task.inputs:
input_obj, err = mlobject_from_dict('mlcube_task_input', '1.0.0', input_dict)
if err:
return None, err
inputs[input_obj.name] = input_obj
outputs = {}
for output_dict in task.outputs:
output_obj, err = mlobject_from_dict('mlcube_task_output', '1.0.0', output_dict)
if err:
return None, err
outputs[output_obj.name] = output_obj
task.inputs = inputs
task.outputs = outputs
return task, None
from mlspeclib import MLObject, MLSchema
from pathlib import Path
import pprint
MLSchema.populate_registry()
MLSchema.append_schema_to_registry(Path("schemas"))
(sample_instantiated_object,
err) = MLObject.create_object_from_file('sample_instantiated_schema.yaml')
pp = pprint.PrettyPrinter(indent=4)
if err != {}:
pp.pprint(err)
else:
pp.pprint(sample_instantiated_object.tasks)
(sample_task_object,
err) = MLObject.create_object_from_file('tasks/download_data.yaml')
if err != {}:
pp.pprint(err)
else:
pp.pprint(sample_task_object.inputs)
pp.pprint(sample_task_object.outputs)
load_path = Path('tasks').glob("*.yaml")
load_list = list(load_path)
for this_file in load_list:
file_text = this_file.read_text()
(loaded_object, err) = MLObject.create_object_from_string(file_text)
def parse_mlbox_docker(filename):
(docker, err) = MLObject.create_object_from_file(filename)
return docker, err
def parse_mlbox_root(filename):
(root, err) = MLObject.create_object_from_file(filename)
return root, err
def parse_mlbox_invoke(filename):
if not os.path.exists(filename):
return None, 'No such invocation file: {}'.format(filename)
(root, err) = MLObject.create_object_from_file(filename)
return root, err
from mlspeclib import MLObject, MLSchema
from pathlib import Path
import pprint
MLSchema.populate_registry()
MLSchema.append_schema_to_registry(Path("schemas"))
(sample_instantiated_object,
err) = MLObject.create_object_from_file('sample_docker_schema.yaml')
pp = pprint.PrettyPrinter(indent=4)
if err != {}:
pp.pprint(err)
else:
pp.pprint(sample_instantiated_object.tasks)