Source code for metanames
################################################################################
#
# Licensed Materials - Property of IBM
# (C) Copyright IBM Corp. 2017
# US Government Users Restricted Rights - Use, duplication disclosure restricted
# by GSA ADP Schedule Contract with IBM Corp.
#
################################################################################
from __future__ import print_function
from watson_machine_learning_client.libs.repo.mlrepository import MetaNames
from tabulate import tabulate
import copy
from watson_machine_learning_client.wml_resource import WMLResource
from watson_machine_learning_client.utils import STR_TYPE, STR_TYPE_NAME
from watson_machine_learning_client.utils.log_util import get_logger
from watson_machine_learning_client.wml_client_error import WMLClientError
from watson_machine_learning_client.href_definitions import API_VERSION, SPACES,PIPELINES, EXPERIMENTS, RUNTIMES, LIBRARIES, DEPLOYMENTS
import json
logger = get_logger('watson_machine_learning_client.metanames')
class MetaProp:
def __init__(self, name, key, prop_type, required, example_value, ignored=False, hidden=False, default_value='', schema= '',path=None, transform=lambda x, client: x):
self.key = key
self.name = name
self.prop_type = prop_type
self.required = required
self.example_value = example_value
self.ignored = ignored
self.hidden = hidden
self.schema = schema
self.default_value = default_value
self.path = path if path is not None else '/' + key
self.transform = transform
class MetaNamesBase:
def __init__(self, meta_props_definitions):
self._meta_props_definitions = meta_props_definitions
def _validate(self, meta_props):
for meta_prop in self._meta_props_definitions:
if meta_prop.key != MetaNames.OUTPUT_DATA_SCHEMA and meta_prop.key != MetaNames.INPUT_DATA_SCHEMA:
if meta_prop.ignored is False:
WMLResource._validate_meta_prop(meta_props, meta_prop.key, meta_prop.prop_type, meta_prop.required)
else:
if(meta_prop.key in meta_props):
logger.warning('\'{}\' meta prop is deprecated. It will be ignored.'.format(meta_prop.name))
def _check_types_only(self, meta_props):
for meta_prop in self._meta_props_definitions:
if meta_prop.ignored is False:
WMLResource._validate_meta_prop(meta_props, meta_prop.key, meta_prop.prop_type, False)
else:
if (meta_prop.key in meta_props):
logger.warning('\'{}\' meta prop is deprecated. It will be ignored.'.format(meta_prop.name))
def get(self):
return sorted(list(map(lambda x: x.name, filter(lambda x: not x.ignored and not x.hidden, self._meta_props_definitions))))
def show(self):
print(self._generate_table())
def _generate_doc_table(self):
return self._generate_table('MetaName', 'Type', 'Required', 'Default value', 'Schema', 'Example value',
show_examples=True, format='grid', values_format='``{}``')
def _generate_doc(self, resource_name):
return """
Set of MetaNames for {}.
Available MetaNames:
{}
""".format(resource_name, MetaNamesBase(self._meta_props_definitions)._generate_doc_table())
def _generate_table(self, name_label='META_PROP NAME', type_label='TYPE',
required_label='REQUIRED', default_value_label='DEFAULT_VALUE',
example_value_label='EXAMPLE_VALUE', schema_label='SCHEMA',show_examples=False, format='simple', values_format='{}'):
show_defaults = any(meta_prop.default_value is not '' for meta_prop in filter(lambda x: not x.ignored and not x.hidden, self._meta_props_definitions))
show_schema = any(meta_prop.schema is not '' for meta_prop in filter(lambda x: not x.ignored and not x.hidden, self._meta_props_definitions))
header = [name_label, type_label, required_label]
if show_schema:
header.append(schema_label)
if show_defaults:
header.append(default_value_label)
if show_examples:
header.append(example_value_label)
table_content = []
for meta_prop in filter(lambda x: not x.ignored and not x.hidden, self._meta_props_definitions):
row = [meta_prop.name, meta_prop.prop_type.__name__, u'Y' if meta_prop.required else u'N']
if show_defaults:
row.append(values_format.format(meta_prop.default_value) if meta_prop.default_value is not '' else '')
if show_examples:
row.append(values_format.format(meta_prop.example_value) if meta_prop.example_value is not '' else '')
if show_schema:
row.append(values_format.format(meta_prop.schema) if meta_prop.schema is not '' else '')
table_content.append(row)
table = tabulate(
[header] + table_content,
tablefmt=format
)
return table
def get_example_values(self):
return dict((x.key, x.example_value) for x in filter(lambda x: not x.ignored and not x.hidden, self._meta_props_definitions))
def _generate_resource_metadata(self, meta_props, client=None, with_validation=False, initial_metadata={}):
if with_validation:
self._validate(meta_props)
metadata = copy.deepcopy(initial_metadata)
def update_map(m, path, el):
if type(m) is dict:
if len(path) == 1:
m[path[0]] = el
else:
if path[0] not in m:
if type(path[1]) is not int:
m[path[0]] = {}
else:
m[path[0]] = []
update_map(m[path[0]], path[1:], el)
elif type(m) is list:
if len(path) == 1:
if len(m) > len(path):
m[path[0]] = el
else:
m.append(el)
else:
if len(m) <= path[0]:
m.append({})
update_map(m[path[0]], path[1:], el)
else:
raise WMLClientError('Unexpected metadata path type: {}'.format(type(m)))
for meta_prop_def in filter(lambda x: not x.ignored, self._meta_props_definitions):
if meta_prop_def.key in meta_props:
path = [int(p) if p.isdigit() else p for p in meta_prop_def.path.split('/')[1:]]
update_map(
metadata,
path,
meta_prop_def.transform(meta_props[meta_prop_def.key], client)
)
return metadata
def _generate_patch_payload(self,
current_metadata,
meta_props,
client=None,
with_validation=False,
asset_meta_patch=False):
if with_validation:
self._check_types_only(meta_props)
updated_metadata = self._generate_resource_metadata(meta_props, client, False, current_metadata)
patch_payload = []
def contained_path(metadata, path):
if path[0] in metadata:
if len(path) == 1:
return [path[0]]
else:
rest_of_path = contained_path(metadata[path[0]], path[1:])
if rest_of_path is None:
return [path[0]]
else:
return [path[0]] + rest_of_path
else:
return []
def get_value(metadata, path):
if len(path) == 1:
return metadata[path[0]]
else:
return get_value(metadata[path[0]], path[1:])
def already_in_payload(path):
return any([el['path'] == path for el in patch_payload])
def update_payload(path):
existing_path = contained_path(current_metadata, path)
if asset_meta_patch:
prepend = '/metadata/'
else:
prepend = '/'
if len(existing_path) == len(path):
patch_payload.append({
'op': 'replace',
'path': prepend + '/'.join(existing_path),
'value': get_value(updated_metadata, existing_path)
})
else:
if not already_in_payload(existing_path):
patch_payload.append({
'op': 'add',
'path': prepend + '/'.join(existing_path + [path[len(existing_path)]]),
'value': get_value(updated_metadata, existing_path + [path[len(existing_path)]])
})
for meta_prop_def in filter(lambda x: not x.ignored, self._meta_props_definitions):
if meta_prop_def.key in meta_props:
path = [int(p) if p.isdigit() else p for p in meta_prop_def.path.split('/')[1:]]
update_payload(path)
return patch_payload
[docs]class TrainingConfigurationMetaNames(MetaNamesBase):
TAGS = "tags"
EXPERIMENT_UID = "experiment_uid"
PIPELINE_UID = "pipeline_uid"
TRAINING_LIB = "training_lib"
TRAINING_LIB_UID = "training_lib_uid"
TRAINING_LIB_MODEL_TYPE = "model_type"
TRAINING_LIB_RUNTIME_UID = "runtime"
TRAINING_LIB_PARAMETERS = "parameters"
COMMAND = "command"
COMPUTE = "compute"
PIPELINE_DATA_BINDINGS = "data_bindings"
PIPELINE_NODE_PARAMETERS = "node_parameters"
PIPELINE_MODEL_TYPE = "model_type"
SPACE_UID = "space_uid"
TRAINING_DATA_REFERENCES = "training_data_references"
TRAINING_RESULTS_REFERENCE = "results_reference"
_COMPUTE_CONFIGURATION_DEFAULT = u'k80'
_meta_props_definitions = [
MetaProp('TRAINING_DATA_REFERENCES', TRAINING_DATA_REFERENCES, list, True, [{u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net',u'access_key_id': u'***',u'secret_access_key': u'***'},u'location': {u'bucket': u'train-data',u'path':u'training_path'},u'type': u's3',u'schema':{u'id':u'1', u'fields': [{u'name': u'x', u'type': u'double', u'nullable': u'False'}]}}] ,schema=[{u'name(optional)': u'string',u'type(required)': u'string',u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'},u'schema(optional)':{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}}]),
MetaProp('TRAINING_RESULTS_REFERENCE', TRAINING_RESULTS_REFERENCE, dict, True, {u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net',u'access_key_id': u'***',u'secret_access_key': u'***'},u'location': {u'bucket': u'test-results',u'path':u'training_path'},u'type': u's3'},schema={u'name(optional)': u'string',u'type(required)': u'string',u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'}}),
MetaProp('TAGS', TAGS , list, False, [{u'value': u"string",u"description": u"string"}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('PIPELINE_UID', PIPELINE_UID, STR_TYPE, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6", path="/pipeline/href",transform=lambda x, client: API_VERSION+PIPELINES+"/"+x),
MetaProp('EXPERIMENT_UID', EXPERIMENT_UID, STR_TYPE, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6", path="/experiment/href",transform=lambda x, client: API_VERSION + EXPERIMENTS + "/" + x),
MetaProp('PIPELINE_DATA_BINDINGS', PIPELINE_DATA_BINDINGS, list, False, path="/pipeline/data_bindings",example_value=[{ "data_reference_name": "string","node_id": "string"}],schema=[{"data_reference_name(required)":"string","node_id(required)":"string"}]),
MetaProp('PIPELINE_NODE_PARAMETERS', PIPELINE_NODE_PARAMETERS, list, False, path="/pipeline/node_parameters",example_value=[{"node_id": "string","parameters": {}}],schema=[{"node_id(required)":"string","parameters(required)":"dict"}] ),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6', path='/space/href',
transform=lambda x, client: API_VERSION + SPACES + "/" + x),
MetaProp('TRAINING_LIB', TRAINING_LIB, dict, False, example_value={"href": "/v4/libraries/3c1ce536-20dc-426e-aac7-7284cf3befc6","compute": {"name": "k80","nodes": 0},"runtime": { "href": "/v4/runtimes/3c1ce536-20dc-426e-aac7-7284cf3befc6"},"command": "python3 convolutional_network.py","parameters": {}}, path="/training_lib",schema={"href(required)":"string","type(required)":"string","runtime(optional)":{"href":"string"},"command(optional)":"string","parameters(optional)":"dict"}),
MetaProp('TRAINING_LIB_UID', TRAINING_LIB_UID, STR_TYPE, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
path="/training_lib/href"),
MetaProp('TRAINING_LIB_MODEL_TYPE', TRAINING_LIB_MODEL_TYPE, STR_TYPE, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
),
MetaProp('TRAINING_LIB_RUNTIME_UID', TRAINING_LIB_RUNTIME_UID, STR_TYPE, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
),
MetaProp('TRAINING_LIB_PARAMETERS', TRAINING_LIB_PARAMETERS, dict, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
),
MetaProp('COMMAND', COMMAND, STR_TYPE, False,
example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
),
MetaProp('COMPUTE', COMPUTE, dict, False,
example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
),
MetaProp('PIPELINE_MODEL_TYPE', PIPELINE_MODEL_TYPE, str, False, example_value="tensorflow_1.1.3-py3",path="/pipeline/model_type")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('trainings')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class TrainingConfigurationMetaNamesCp4d30(MetaNamesBase):
TAGS = "tags"
EXPERIMENT = "experiment"
PIPELINE = "pipeline"
MODEL_DEFINITION = "model_definition"
FEDERATED_LEARNING = "federated_learning"
NAME = "name"
DESCRIPTION = "description"
SPACE_UID = "space_uid"
TRAINING_DATA_REFERENCES = "training_data_references"
TRAINING_RESULTS_REFERENCE = "results_reference"
_COMPUTE_CONFIGURATION_DEFAULT = u'k80'
_meta_props_definitions = [
MetaProp('TRAINING_DATA_REFERENCES', TRAINING_DATA_REFERENCES, list, True, [{u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net',u'access_key_id': u'***',u'secret_access_key': u'***'},u'location': {u'bucket': u'train-data',u'path':u'training_path'},u'type': u's3',u'schema':{u'id':u'1', u'fields': [{u'name': u'x', u'type': u'double', u'nullable': u'False'}]}}] ,schema=[{u'name(optional)': u'string',u'type(required)': u'string',u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'},u'schema(optional)':{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}}]),
MetaProp('TRAINING_RESULTS_REFERENCE', TRAINING_RESULTS_REFERENCE, dict, True, {u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net',u'access_key_id': u'***',u'secret_access_key': u'***'},u'location': {u'bucket': u'test-results',u'path':u'training_path'},u'type': u's3'},schema={u'name(optional)': u'string',u'type(required)': u'string',u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'}}),
MetaProp('TAGS', TAGS , list, False, [u"string"],schema=[u'string']),
MetaProp('PIPELINE', PIPELINE, dict, False, {"id":"3c1ce536-20dc-426e-aac7-7284cf3befc6","rev":"1","modeltype":"tensorflow_1.1.3-py3", "data_bindings":[{ "data_reference_name":"string","node_id": "string"}],"node_parameters":[{"node_id": "string","parameters": {}}],"hardware_spec":{"id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab","rev": "12","name": "string","num_nodes": "2"}, "hybrid_pipeline_hardware_specs": [{"node_runtime_id": "string","hardware_spec": {"id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab","rev": "12","name": "string", "num_nodes": "2"}}]}),
MetaProp('EXPERIMENT', EXPERIMENT, dict, False, {"id":"3c1ce536-20dc-426e-aac7-7284cf3befc6", "rev": 1, "description": "test experiment"}),
MetaProp('FEDERATED_LEARNING', FEDERATED_LEARNING, dict, False, example_value="3c1ce536-20dc-426e-aac7-7284cf3befc6",
path="/federated_learning"),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6', path='/space/href',
transform=lambda x, client: x),
MetaProp('MODEL_DEFINITION', MODEL_DEFINITION, dict, False, {"id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab","rev": "12", "model_type": "string","hardware_spec": {"id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab","rev": "12","name": "string","num_nodes": "2"},"software_spec": {"id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab","rev": "12", "name": "..."},"command": "string","parameters": {}}),
MetaProp('DESCRIPTION', DESCRIPTION, str, True, example_value="tensorflow model training"),
MetaProp('NAME', NAME, str, True, example_value="sample training")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('trainings')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class ExperimentMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
TAGS = "tags"
EVALUATION_METHOD = "evaluation_method"
EVALUATION_METRICS = "evaluation_metrics"
TRAINING_REFERENCES = "training_references"
SPACE_UID = "space_uid"
LABEL_COLUMN = "label_column"
CUSTOM = "custom"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, u'Hand-written Digit Recognition'),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, u'Hand-written Digit Recognition training'),
MetaProp('TAGS', TAGS, list, False, [{u'value': 'dsx-project.<project-guid>',u'description': 'DSX project guid'}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('EVALUATION_METHOD', EVALUATION_METHOD, STR_TYPE, False, u'multiclass', path="/evaluation_definition/method"),
MetaProp('EVALUATION_METRICS', EVALUATION_METRICS, list, False, [{u'name':u'accuracy', u'maximize': False}], path="/evaluation_definition/metrics", schema=[{u'name(required)': u'string', u'maximize(optional)': u'boolean'}]),
MetaProp('TRAINING_REFERENCES', TRAINING_REFERENCES, list, True, [{u'pipeline': {u'href': u'/v4/pipelines/6d758251-bb01-4aa5-a7a3-72339e2ff4d8'}}],schema=[{u'pipeline(optional)': {u'href(required)': u'string', u'data_bindings(optional)':[{u'data_reference(required)':u'string', u'node_id(required)':u'string'}], u'nodes_parameters(optional)':[{u'node_id(required)':u'string', u'parameters(required)':u'dict'}]}, u'training_lib(optional)': {u'href(required)': u'string', u'compute(optional)': {u'name(required)': u'string', u'nodes(optional)': u'number'}, u'runtime(optional)': {u'href(required)': u'string'}, u'command(optional)': u'string', u'parameters(optional)':u'dict'}}]),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6', path='/space/href', transform=lambda x, client: API_VERSION + SPACES + "/" + x),
MetaProp('LABEL_COLUMN', LABEL_COLUMN, STR_TYPE , False, u'label'),
MetaProp('CUSTOM', CUSTOM, dict, False, {"field1": "value1"})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('experiments')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class PipelineMetanames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
TAGS = "tags"
SPACE_UID = "space_url"
IMPORT = "import"
DOCUMENT = "document"
CUSTOM = "custom"
RUNTIMES = "runtimes"
COMMAND = "command"
LIBRARY_UID = "training_lib_href"
COMPUTE = "compute"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, u'Hand-written Digit Recognitionu'),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, u'Hand-written Digit Recognition training'),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6',path="/space/href",transform=lambda x, client: API_VERSION+SPACES+"/"+x),
MetaProp('TAGS', TAGS, list, False, [{u'value': 'dsx-project.<project-guid>',u'description': 'DSX project guid'}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('DOCUMENT', DOCUMENT, dict, False, example_value={"doc_type":"pipeline","version": "2.0","primary_pipeline": "dlaas_only","pipelines": [{"id": "dlaas_only","runtime_ref": "hybrid","nodes": [{"id": "training","type": "model_node","op": "dl_train","runtime_ref": "DL","inputs": [],"outputs": [],"parameters": {"name": "tf-mnist","description": "Simple MNIST model implemented in TF","command": "python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 6000","compute": {"name": "k80","nodes": 1},"training_lib_href":"/v4/libraries/64758251-bt01-4aa5-a7ay-72639e2ff4d2/content"},"target_bucket": "wml-dev-results"}]}]},schema={"doc_type(required)":"string","version(required)": "string","primary_pipeline(required)": "string","pipelines(required)": [{"id(required)": "string","runtime_ref(required)": "string","nodes(required)": [{"id": "string","type": "string","inputs": "list","outputs": "list","parameters": {"training_lib_href":"string"}}]}]}),
MetaProp('CUSTOM', CUSTOM, dict, False, example_value={"field1":"value1"}),
MetaProp('IMPORT', IMPORT, dict, False, example_value={u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net',u'access_key_id': u'***',u'secret_access_key': u'***'},u'location': {u'bucket': u'train-data',u'path':u'training_path'},u'type': u's3'},schema={u'name(optional)': u'string',u'type(required)': u'string',u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'}}),
MetaProp('RUNTIMES', RUNTIMES, list, False, example_value=[{"id":"id","name":"tensorflow","version":"1.13-py3"}]),
MetaProp('COMMAND', COMMAND, STR_TYPE, False, example_value="convolutional_network.py --trainImagesFile train-images-idx3-ubyte.gz --trainLabelsFile train-labels-idx1-ubyte.gz --testImagesFile t10k-images-idx3-ubyte.gz --testLabelsFile t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 6000"),
MetaProp("LIBRARY_UID", LIBRARY_UID, STR_TYPE, False, example_value="fb9752c9-301a-415d-814f-cf658d7b856a"),
MetaProp("COMPUTE", COMPUTE, dict, False, example_value={"name":"k80","nodes":1})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('pipelines')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class LearningSystemMetaNames(MetaNamesBase):
_COMPUTE_CONFIGURATION_DEFAULT = 'k80'
FEEDBACK_DATA_REFERENCE = "feedback_data_reference"
SPARK_REFERENCE = "spark_instance"
MIN_FEEDBACK_DATA_SIZE = "min_feedback_data_size"
AUTO_RETRAIN = "auto_retrain"
AUTO_REDEPLOY = "auto_redeploy"
COMPUTE_CONFIGURATION = "compute_configuration"
TRAINING_RESULTS_REFERENCE = "training_results_reference"
_meta_props_definitions = [
MetaProp('FEEDBACK_DATA_REFERENCE', FEEDBACK_DATA_REFERENCE, dict, True, example_value={}),
MetaProp('SPARK_REFERENCE', SPARK_REFERENCE, dict, False, example_value={}),
MetaProp('MIN_FEEDBACK_DATA_SIZE', MIN_FEEDBACK_DATA_SIZE, int, True, example_value=100),
MetaProp('AUTO_RETRAIN', AUTO_RETRAIN, STR_TYPE, True, example_value="conditionally"),
MetaProp('AUTO_REDEPLOY', AUTO_REDEPLOY, STR_TYPE, True, example_value="always"),
MetaProp('COMPUTE_CONFIGURATION', COMPUTE_CONFIGURATION, dict, False, example_value={u'name': _COMPUTE_CONFIGURATION_DEFAULT}),
MetaProp('TRAINING_RESULTS_REFERENCE', TRAINING_RESULTS_REFERENCE, dict, False, example_value={u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net', u'access_key_id': u'***', u'secret_access_key': u'***'},u'target': {u'bucket': u'train-data'}, u'type': u's3'}),
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('learning system')
[docs]class MemberMetaNames(MetaNamesBase):
IDENTITY = "identity"
ROLE = "role"
IDENTITY_TYPE = "identity_type"
_meta_props_definitions = [
MetaProp('IDENTITY', IDENTITY, STR_TYPE, True, "IBMid-060000123A (service-ID or IAM-userID)"),
MetaProp('ROLE', ROLE, STR_TYPE, True, "Supported values - Viewer/Editor/Admin"),
MetaProp('IDENTITY_USER', IDENTITY_TYPE, STR_TYPE, True, "Supported values - service/user")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Member Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class ModelMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = MetaNames.DESCRIPTION
TRAINING_DATA_REFERENCES = "training_data_references"
OUTPUT_DATA_SCHEMA = MetaNames.OUTPUT_DATA_SCHEMA
LABEL_FIELD = MetaNames.LABEL_FIELD
TRANSFORMED_LABEL_FIELD = MetaNames.TRANSFORMED_LABEL_FIELD
RUNTIME_UID = "runtime"
INPUT_DATA_SCHEMA = MetaNames.INPUT_DATA_SCHEMA
CUSTOM="custom"
DOMAIN="domain"
HYPER_PARAMETERS="hyper_parameters"
TAGS = "tags"
SPACE_UID = "space"
PIPELINE_UID = "pipeline"
TYPE="type"
SIZE="size"
IMPORT = "import"
TRAINING_LIB_UID = "training_lib"
MODEL_DEFINITION_UID = "model_definition"
METRICS="metrics"
SOFTWARE_SPEC_UID = "software_spec"
TF_MODEL_PARAMS = "tf_model_params"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_model"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description", path="/description"),
MetaProp('INPUT_DATA_SCHEMA', INPUT_DATA_SCHEMA, list, False, example_value={"id":"1","type": "struct", "fields": [{"name": "x", "type": "double", "nullable": False, "metadata": {}}, {"name": "y", "type": "double", "nullable": False, "metadata": {}}]}, path="/input_data_schema",schema={u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}),
MetaProp('TRAINING_DATA_REFERENCES',TRAINING_DATA_REFERENCES, list, False, [], path="/training_data_references",schema=[{"name(optional)":"string","type(required)":"string",u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'},u'schema(optional)':{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}}]),
MetaProp('OUTPUT_DATA_SCHEMA', OUTPUT_DATA_SCHEMA, dict, False, example_value={"id":"1","type": "struct", "fields": [{"name": "x", "type": "double", "nullable": False, "metadata": {}}, {"name": "y", "type": "double", "nullable": False, "metadata": {}}]}, path="/output_data_schema",schema={u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}),
MetaProp('LABEL_FIELD', LABEL_FIELD, STR_TYPE, False, example_value='PRODUCT_LINE', path="/label_column"),
MetaProp('TRANSFORMED_LABEL_FIELD', TRANSFORMED_LABEL_FIELD, STR_TYPE, False, example_value='PRODUCT_LINE_IX', path="/transformed_label"),
MetaProp('TAGS', TAGS, list, False, example_value=[{"value": "string","description": "string"}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('SIZE', SIZE, dict, False, example_value={"in_memory": 0,"content": 0},schema={u'in_memory(optional)': u'string', u'content(optional)': u'string'}),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, example_value="53628d69-ced9-4f43-a8cd-9954344039a8", path="/space/href"),
MetaProp('PIPELINE_UID', PIPELINE_UID, STR_TYPE, False, example_value="53628d69-ced9-4f43-a8cd-9954344039a8", path="/pipeline/href"),
MetaProp('RUNTIME_UID', RUNTIME_UID, STR_TYPE, False, example_value="53628d69-ced9-4f43-a8cd-9954344039a8",path="/runtime/href"),
MetaProp('TYPE', TYPE, STR_TYPE, True, example_value="mllib_2.1", path="/type"),
MetaProp('CUSTOM', CUSTOM , dict, False, example_value={}),
MetaProp('DOMAIN', DOMAIN, STR_TYPE, False, example_value="Watson Machine Learning"),
MetaProp('HYPER_PARAMETERS', HYPER_PARAMETERS, dict, False, example_value=""),
MetaProp('METRICS', METRICS, list, False, example_value=""),
MetaProp('IMPORT', IMPORT, dict, False, example_value={u'connection': {u'endpoint_url': u'https://s3-api.us-geo.objectstorage.softlayer.net',u'access_key_id': u'***',u'secret_access_key': u'***'},u'location': {u'bucket': u'train-data',u'path':u'training_path'},u'type': u's3'},schema={u'name(optional)': u'string',u'type(required)': u'string',u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'}}),
MetaProp('TRAINING_LIB_UID', TRAINING_LIB_UID, STR_TYPE, False, example_value="53628d69-ced9-4f43-a8cd-9954344039a8", path="/training_lib"),
MetaProp('MODEL_DEFINITION_UID',MODEL_DEFINITION_UID, STR_TYPE, False, example_value="53628d6_cdee13-35d3-s8989343", path="/model_definition"),
MetaProp('SOFTWARE_SPEC_UID', SOFTWARE_SPEC_UID, STR_TYPE, False, example_value="53628d69-ced9-4f43-a8cd-9954344039a8", path="/software_spec/id", transform=lambda x, client: x),
MetaProp('TF_MODEL_PARAMS', TF_MODEL_PARAMS, dict, False,
example_value={"save_format": "None", "signatures": "struct", "options": "None",
"custom_objects": "string"}, path="/tf_model_params")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('models')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class PayloadLoggingMetaNames(MetaNamesBase):
PAYLOAD_DATA_REFERENCE = "payload_store"
LABELS = "labels"
OUTPUT_DATA_SCHEMA = "output_data_schema"
_meta_props_definitions = [
MetaProp('PAYLOAD_DATA_REFERENCE', PAYLOAD_DATA_REFERENCE, dict, True, {}),
MetaProp('LABELS', LABELS, list, False, ['a', 'b', 'c']),
MetaProp('OUTPUT_DATA_SCHEMA', OUTPUT_DATA_SCHEMA, dict, False, {})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('payload logging system')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class FunctionMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
RUNTIME_UID = "runtime_uid"
INPUT_DATA_SCHEMAS = "input_data_schemas"
OUTPUT_DATA_SCHEMAS = "output_data_schemas"
TAGS = "tags"
SPACE_UID = "space_uid"
SOFTWARE_SPEC_UID = "software_spec_uid"
TYPE = "type"
CUSTOM="custom"
SAMPLE_SCORING_INPUT="sample_scoring_input"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "ai_function"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "This is ai function"),
MetaProp('RUNTIME_UID', RUNTIME_UID, STR_TYPE, False, '53628d69-ced9-4f43-a8cd-9954344039a8', path="/runtime/href", transform=lambda x, client: API_VERSION+RUNTIMES+"/"+x),
MetaProp('SOFTWARE_SPEC_UID', SOFTWARE_SPEC_UID, STR_TYPE, False, '53628d69-ced9-4f43-a8cd-9954344039a8',
path="/software_spec/id", transform=lambda x, client: x),
MetaProp('INPUT_DATA_SCHEMAS', INPUT_DATA_SCHEMAS, list, False, [{"id":"1","type": "struct", "fields": [{"name": "x", "type": "double", "nullable": False, "metadata": {}}, {"name": "y", "type": "double", "nullable": False, "metadata": {}}]}],schema=[{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}]),
MetaProp('OUTPUT_DATA_SCHEMAS', OUTPUT_DATA_SCHEMAS, list, False, [{"id":"1","type": "struct", "fields": [{"name": "multiplication", "type": "double", "nullable": False, "metadata": {}}]}],schema=[{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}]),
MetaProp('TAGS', TAGS, list, False, [{"value": "ProjectA", "description": "Functions created for ProjectA"}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('TYPE', TYPE, STR_TYPE, False, u'python'),
MetaProp('CUSTOM', CUSTOM, dict, False, example_value=u'{}'),
MetaProp('SAMPLE_SCORING_INPUT', SAMPLE_SCORING_INPUT, list, False, example_value={ "input_data": [ { "fields": [ "name", "age", "occupation" ], "values": [ [ "john",23, "student"], [ "paul", 33, "engineer" ]] }]},schema={"id(optional)":"string","fields(optional)":"array","values(optional)":"array"}),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3628d69-ced9-4f43-a8cd-9954344039a8',path="/space/href",transform=lambda x, client: API_VERSION+SPACES+"/"+x)
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('AI functions')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class ScoringMetaNames(MetaNamesBase):
INPUT_DATA = "input_data"
INPUT_DATA_REFERENCES = "input_data_references"
OUTPUT_DATA_REFERENCE = "output_data_reference"
EVALUATIONS_SPEC = "evaluations_spec"
ENVIRONMENT_VARIABLES = "environment_variables"
_meta_props_definitions = [
MetaProp('INPUT_DATA',INPUT_DATA,list,False, path="/scoring/input_data",example_value=[{"fields": ["name","age","occupation"],"values": [["john",23,"student"]]}], schema=[{"name(optional)":"string","id(optional)":"string","fields(optional)":"array[string]","values":"array[array[string]]"}]),
MetaProp('INPUT_DATA_REFERENCES', INPUT_DATA_REFERENCES, list, False,example_value = "",path="/scoring/input_data_references",schema=[{"id(optional)":"string","name(optional)":"string","type(required)":"string",u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'},u'schema(optional)':{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}}]),
MetaProp('OUTPUT_DATA_REFERENCE', OUTPUT_DATA_REFERENCE, dict, False, example_value = "",path="/scoring/output_data_reference", schema={"name(optional)":"string","type(required)":"string",u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'},u'schema(optional)':{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}}),
MetaProp('EVALUATIONS_SPEC', EVALUATIONS_SPEC, list, False, path="/scoring/evaluations",example_value=[{"id": "string","input_target": "string","metrics_names": ["auroc","accuracy"]}], schema=[{"id(optional)":"string", "input_target(optional)":"string","metrics_names(optional)":"array[string]"}]),
MetaProp('ENVIRONMENT_VARIABLES', ENVIRONMENT_VARIABLES, dict, False, path="/scoring/environment_variables", example_value = {"my_env_var1": "env_var_value1","my_env_var2": "env_var_value2"})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Scoring')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class DecisionOptimizationMetaNames(MetaNamesBase):
INPUT_DATA = "input_data"
INPUT_DATA_REFERENCES = "input_data_references"
OUTPUT_DATA = "output_data"
OUTPUT_DATA_REFERENCES = "output_data_references"
SOLVE_PARAMETERS = "solve_parameters"
_meta_props_definitions = [
MetaProp('INPUT_DATA', INPUT_DATA, list, False,path="/decision_optimization/input_data", example_value=[{"fields": ["name","age","occupation"],"values": [["john",23,"student"]]}], schema=[{"name(optional)":"string","id(optional)":"string","fields(optional)":"array[string]","values":"array[array[string]]"}]),
MetaProp('INPUT_DATA_REFERENCES', INPUT_DATA_REFERENCES, list, False,path="/decision_optimization/input_data_references", example_value=[{"fields": ["name","age","occupation"],"values": [["john",23,"student"]]}], schema=[{"name(optional)":"string","id(optional)":"string","fields(optional)":"array[string]","values":"array[array[string]]"}]),
MetaProp('OUTPUT_DATA', OUTPUT_DATA, list, False,example_value="",path="/decision_optimization/output_data", schema=[{"name(optional)": "string"}]),
MetaProp('OUTPUT_DATA_REFERENCES', OUTPUT_DATA_REFERENCES, list, False,example_value="",path="/decision_optimization/output_data_references", schema={"name(optional)":"string","type(required)":"string",u'connection(required)':{u'endpoint_url(required)': u'string',u'access_key_id(required)':u'string',u'secret_access_key(required)':u'string'},u'location(required)':{u'bucket':u'string',u'path':u'string'},u'schema(optional)':{u'id(required)':u'string',u'fields(required)':[{u'name(required)':u'string',u'type(required)':u'string',u'nullable(optional)':u'string'}]}}),
MetaProp('SOLVE_PARAMETERS', SOLVE_PARAMETERS, dict, False, example_value="",path="/decision_optimization/solve_parameters")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Decision Optimization')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class RuntimeMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
CUSTOM = "custom"
PLATFORM = "platform"
LIBRARIES_UIDS = "libraries_uids"
CONFIGURATION_FILEPATH = "configuration_filepath"
TAGS = "tags"
SPACE_UID = "space"
COMPUTE = "compute"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "runtime_spec_python_3.7"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "sample runtime"),
MetaProp('PLATFORM', PLATFORM, dict, True, u'{"name":python","version":"3.7")',schema={"name(required)":"string","version(required)":"version"}),
MetaProp('LIBRARIES_UIDS', LIBRARIES_UIDS, list, False, ["46dc9cf1-252f-424b-b52d-5cdd9814987f"]),
MetaProp('CONFIGURATION_FILEPATH', CONFIGURATION_FILEPATH, STR_TYPE, False, "/home/env_config.yaml"),
MetaProp('TAGS', TAGS, list, False, [{u'value': 'dsx-project.<project-guid>',u'description': 'DSX project guid'}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('CUSTOM', CUSTOM, dict, False, u'{"field1": "value1"}'),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, path="/space/href",example_value="46dc9cf1-252f-424b-b52d-5cdd9814987f",transform=lambda x, client:API_VERSION+SPACES+"/"+x),
MetaProp('COMPUTE', COMPUTE, dict, False, example_value={"name":"name1", "nodes": 1},schema={"name(required)":"string","nodes(optional)":"string"})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Runtime Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class LibraryMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
FILEPATH = "filepath"
VERSION = "version"
PLATFORM = "platform"
TAGS = "tags"
SPACE_UID = "space_uid"
MODEL_DEFINITION = "model_definition"
CUSTOM = "custom"
COMMAND = "command"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_lib"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my lib"),
MetaProp('PLATFORM', PLATFORM, dict, True, {"name": "python", "versions": ["3.7"]},schema={"name(required)":"string","version(required)":"version"}),
MetaProp('VERSION', VERSION, STR_TYPE, True, "1.0"),
MetaProp('FILEPATH', FILEPATH, STR_TYPE, True, "/home/user/my_lib_1_0.zip"),
MetaProp('TAGS', TAGS, dict, False, [{u'value': 'dsx-project.<project-guid>',u'description': 'DSX project guid'}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6', path='/space/href', transform=lambda x, client: API_VERSION+SPACES+"/"+x),
MetaProp('MODEL_DEFINITION', MODEL_DEFINITION, bool, False, False),
MetaProp('COMMAND', COMMAND, STR_TYPE, False, u'command'),
MetaProp('CUSTOM', CUSTOM, dict, False, {"field1": "value1"})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Custom Libraries')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class SpacesMetaNames(MetaNamesBase):
TAGS = "tags"
CUSTOM = "custom"
NAME = "name"
DESCRIPTION = "description"
ONLINE_DEPLOYMENTS = "online_deployments"
SCHEDULES = "schedules"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_space"),
MetaProp('TAGS', TAGS, list, False, [{u'value': 'dsx-project.<project-guid>',u'description': 'DSX project guid'}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('CUSTOM', CUSTOM, dict, False, '{"field1":"value1"}'),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('ONLINE_DEPLOYMENTS', ONLINE_DEPLOYMENTS, list, False, [{}], path="/deployment_descriptor/online_deployments", schema=[{u'name(optional)': u'string',u'description(optional)': u'string', u'guid(optional)': u'string', u'compute(optional)': {u'name(required)': u'string', u'nodes(optional)': u'number'}}]),
MetaProp('SCHEDULES', SCHEDULES, list, False, [{}],path="/deployment_descriptor/schedules", schema=[{u'cron(optional)': u'string', u'assets(optional)': [{u'name(optional)': u'string', u'description(optional)': u'string', u'guid(optional)': u'string', u'compute(optional)': {u'name(required)': u'string', u'nodes(optional)': u'number'}}]}]),
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Spaces Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class ExportMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
ASSETS = "assets"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_space"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('ASSETS', ASSETS, dict, example_value={u'data_assets(optional)': [], u'wml_model': []}, required=True)
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Space Exports Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class AssetsMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
CONNECTION_ID = "connection_id"
DATA_CONTENT_NAME = "data_content_name"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_data_asset"),
MetaProp('DATA_CONTENT_NAME', DATA_CONTENT_NAME, STR_TYPE, True, "/test/sample.csv"),
MetaProp('CONNECTION_ID', CONNECTION_ID, STR_TYPE, False, "39eaa1ee-9aa4-4651-b8fe-95d3ddae"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Data Asset Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
## update this later #Todo
class SwSpecMetaNames(MetaNamesBase):
TAGS = "tags"
NAME = "name"
DESCRIPTION = "description"
PACKAGE_EXTENSIONS = "package_extenstion"
SOFTWARE_CONFIGURATION = "software_configuration"
BASE_SOFTWARE_SPECIFICATION = "base_software_specification"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "Python 3.6 with pre-installed ML package"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('PACKAGE_EXTENSIONS', PACKAGE_EXTENSIONS, list, False, [{"guid":"value"}]),
MetaProp('SOFTWARE_CONFIGURATION', SOFTWARE_CONFIGURATION, dict, False,
{"platform": {"name": "python","version": "3.6"}},
schema={"platform(required)": "string"}),
MetaProp('BASE_SOFTWARE_SPECIFICATION', BASE_SOFTWARE_SPECIFICATION, dict, True, {"guid": u'BASE_SOFTWARE_SPECIFICATION_ID'} )
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Software Specifications Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class ScriptMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
SOFTWARE_SPEC_UID = "software_spec_uid"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "Python script"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('SOFTWARE_SPEC_UID', SOFTWARE_SPEC_UID, STR_TYPE, True, '53628d69-ced9-4f43-a8cd-9954344039a8')
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Script Specifications')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class ShinyMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "Shiny App"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Shiny Specifications')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class PkgExtnMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
TYPE = "type"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "Python 3.6 with pre-installed ML package"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('TYPE', TYPE, STR_TYPE, True, "conda_yml/custom_library")
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Package Extensions Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
## update this later #Todo
class HwSpecMetaNames(MetaNamesBase):
TAGS = "tags"
NAME = "name"
DESCRIPTION = "description"
HARDWARE_CONFIGURATION = "hardware_configuration"
BASE_SOFTWARE_SPECIFICATION = "base_software_specification"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "Python 3.6 with pre-installed ML package"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('HARDWARE_CONFIGURATION', HARDWARE_CONFIGURATION, dict, False, {})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Software Specifications Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class ModelDefinitionMetaNames(MetaNamesBase):
NAME = "name"
DESCRIPTION = "description"
PLATFORM = "platform"
VERSION = "version"
SPACE_UID = "space_id"
COMMAND = "command"
CUSTOM = "custom"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_model_definition"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my model_definition"),
MetaProp('PLATFORM', PLATFORM, dict, True, {"name": "python", "versions": ["3.7"]},
schema={"name(required)": "string", "version(required)": "version"}),
MetaProp('VERSION', VERSION, STR_TYPE, True, "1.0"),
MetaProp('COMMAND', COMMAND, STR_TYPE, False, "python3 convolutional_network.py"),
MetaProp('CUSTOM', CUSTOM, dict, False, {"field1": "value1"}),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6', path='/space/href',
transform=lambda x, client: API_VERSION + SPACES + "/" + x)
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Model Definition')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
class ConnectionMetaNames(MetaNamesBase):
DATASOURCE_TYPE = "datasource_type"
NAME = "name"
DESCRIPTION = "description"
PROPERTIES = "properties"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, True, "my_space"),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, "my_description"),
MetaProp('DATASOURCE_TYPE', DATASOURCE_TYPE, STR_TYPE, True, "1e3363a5-7ccf-4fff-8022-4850a8024b68"),
MetaProp('PROPERTIES', PROPERTIES, dict, True, example_value={"database": "BLUDB","host": "dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net","password": "a1b2c3d4#","username": "usr21370"})
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Spaces Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
[docs]class DeploymentMetaNames(MetaNamesBase):
NAME = "name"
TAGS = "tags"
DESCRIPTION = "description"
#ENVIRONMENTS = "environments"
CUSTOM = "custom"
AUTO_REDEPLOY = "auto_redeploy"
COMPUTE = "compute"
ONLINE = "online"
BATCH = "batch"
VIRTUAL = "virtual"
SPACE_UID = "space"
HARDWARE_SPEC = "hardware_spec"
ASSET = "asset"
R_SHINY = "r_shiny"
HYBRID_PIPELINE_HARDWARE_SPECS = "hybrid_pipeline_hardware_specs"
# VIRTUAL = "virtual"
_meta_props_definitions = [
MetaProp('NAME', NAME, STR_TYPE, False, 'my_deployment'),
MetaProp('TAGS', TAGS, list, False, [{u'value': 'dsx-project.<project-guid>',u'description': 'DSX project guid'}],schema=[{u'value(required)': u'string',u'description(optional)': u'string'}]),
MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, 'my_deployment'),
#MetaProp('ENVIRONMENTS', ENVIRONMENTS, list, False, ['dev', 'staging']),
MetaProp('CUSTOM', CUSTOM, dict, False, {}),
MetaProp('AUTO_REDEPLOY', AUTO_REDEPLOY, bool, False, False),
MetaProp('SPACE_UID', SPACE_UID, STR_TYPE, False, u'3c1ce536-20dc-426e-aac7-7284cf3befc6', path='/space/href',transform=lambda x, client: API_VERSION + SPACES + "/" + x),
MetaProp('COMPUTE', COMPUTE, dict, example_value=None,required=False),
MetaProp('ONLINE', ONLINE, dict, example_value={}, required=False),
MetaProp('BATCH', BATCH, dict, example_value={}, required=False),
MetaProp('VIRTUAL', VIRTUAL, dict, example_value={}, required=False),
MetaProp('ASSET', ASSET, dict, example_value={}, required=False),
MetaProp('R_SHINY', R_SHINY, dict, example_value={}, required=False),
MetaProp('HYBRID_PIPELINE_HARDWARE_SPECS', HYBRID_PIPELINE_HARDWARE_SPECS, list, example_value=[{'id': '3342-1ce536-20dc-4444-aac7-7284cf3befc'}], required=False),
MetaProp('HARDWARE_SPEC',HARDWARE_SPEC, dict, example_value={'id': '3342-1ce536-20dc-4444-aac7-7284cf3befc'}, required=False)
]
__doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Deployments Specs')
def __init__(self):
MetaNamesBase.__init__(self, self._meta_props_definitions)
# class EnvironmentMetaNames(MetaNamesBase):
# NAME = "name"
# DESCRIPTION = "description"
# BASE_URL = "base_url"
# # VIRTUAL = "virtual"
#
# _meta_props_definitions = [
# MetaProp('NAME', NAME, STR_TYPE, False, 'my_deployment'),
# MetaProp('DESCRIPTION', DESCRIPTION, STR_TYPE, False, 'my_deployment'),
# MetaProp('BASE_URL', BASE_URL, STR_TYPE, False)
# ]
#
# __doc__ = MetaNamesBase(_meta_props_definitions)._generate_doc('Environment Specs')
#
# def __init__(self):
# MetaNamesBase.__init__(self, self._meta_props_definitions)
