--- /dev/null
+# ===============LICENSE_START=======================================================
+# Acumos Apache-2.0
+# ===================================================================================
+# Copyright (C) 2017-2018 AT&T Intellectual Property & Tech Mahindra. All rights reserved.
+# ===================================================================================
+# This Acumos software file is distributed by AT&T and Tech Mahindra
+# 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
+#
+# This file 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.
+# ===============LICENSE_END=========================================================
+
+from acumos.session import AcumosSession
+from acumos.modeling import Model, List, create_dataframe
+
+import numpy as np
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+clf = RandomForestClassifier(random_state=0)
+clf.fit(X, y)
+
+# here, an appropriate NamedTuple type is inferred from a pandas DataFrame
+X_df = pd.DataFrame(X, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
+IrisDataFrame = create_dataframe('IrisDataFrame', X_df)
+
+# ==================================================================================
+# # or equivalently:
+#
+# IrisDataFrame = create_namedtuple('IrisDataFrame', [('sepal_length', List[float]),
+# ('sepal_width', List[float]),
+# ('petal_length', List[float]),
+# ('petal_width', List[float])])
+# ==================================================================================
+
+def classify_iris(df: IrisDataFrame) -> List[int]:
+ '''Returns an array of iris classifications'''
+ X = np.column_stack(df)
+ return clf.predict(X)
+
+model = Model(classify=classify_iris)
+
+session = AcumosSession()
+
+session.dump(model,'iris_sklearn','/Users/guy/Desktop')
--- /dev/null
+# ========================LICENSE_START=================================
+# O-RAN-SC
+# %%
+# Copyright (c) 2019 AT&T Intellectual Property.
+# %%
+# 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.
+# ========================LICENSE_END===================================
+
+# Adapter from RMR to standard Acumos model microservices. Must be deployed in the same pod as the Acumos model.
+# Translates RMR protocol messages into calls into Acumos RPC calls.
+
+
+from rmr import rmr
+import time
+import sys
+import signal
+import json
+import requests
+
+verbose = True
+requireartifacts = True
+
+confdir = '/conf/'
+conffilename = 'config.json'
+protobuffilename = 'model.proto'
+metadatafilename = 'metadata.json'
+
+configfilename = confdir + conffilename
+
+if verbose:
+ print("Reading config file")
+
+# Fetch and parse config file which must be mounted as a volume during deployment
+try:
+ with open(configfilename) as f:
+ conf = json.load(f)
+except:
+ print('Cannot read/parse config file at', configfilename, '; aborting')
+ exit(1)
+
+methodurl = conf['microserviceRootURL'] + conf['methodRoot']
+artifacturl = conf["microserviceRootURL"] + conf['artifactRoot']
+
+if verbose:
+ print ('\nRetrieving artifacts from Acumos model microservice\n')
+
+# See if we can retrieve protobuf and metadata artifacts from running model. Not all models may provide these, but we
+# should have a retry mechanism added for robustness
+try:
+ r = requests.get(artifacturl + 'protobuf')
+ protobuf = r.content
+ with open(confdir + protobuffilename, 'wb') as f:
+ f.write(protobuf)
+ if verbose:
+ print('Protbuf:')
+ print(protobuf.decode('ascii'))
+ r = requests.get(artifacturl + 'metadata')
+ metadata = r.content
+ with open(confdir + metadatafilename, 'wb') as f:
+ f.write(metadata)
+ if verbose:
+ print('\nMetadata:')
+ print(metadata.decode('ascii'))
+except:
+ if requireartifacts:
+ print('Problem with retrieving/saving model protobuf and/or metadata; aborting.')
+
+method1 = conf['methods']['1']
+method1url = methodurl + method1['service']
+method1headers = {'content-type': method1['content-type'], 'accept': method1['return-type']}
+
+if verbose:
+ print('\nInitializing RMR\n')
+
+if verbose:
+ print('\Awaiting connections')
+
+
+# NNG cleanup on signal
+def signal_handler(sig, frame):
+ if verbose:
+ print('SIGINT received! Cleaning up rmr')
+ rmr.rmr_close(mrc)
+ print("Exiting")
+ sys.exit(0)
+
+
+# Initialize RMR
+mrc = rmr.rmr_init("4560".encode('utf-8'), rmr.RMR_MAX_RCV_BYTES, 0x00)
+while rmr.rmr_ready(mrc) == 0:
+ time.sleep(1)
+ if verbose:
+ print("Not yet ready")
+rmr.rmr_set_stimeout(mrc, 2)
+
+
+# Capture ctrl-c
+signal.signal(signal.SIGINT, signal_handler)
+
+
+sbuf = None
+while True:
+ if verbose:
+ print("Waiting for a message; will time out after 2000ms")
+ sbuf = rmr.rmr_torcv_msg(mrc, sbuf, 2000)
+ summary = rmr.message_summary(sbuf)
+ if verbose and summary['message state'] == 12:
+ print("Nothing received.")
+ else:
+ if verbose:
+ print("Message received: {}".format(summary))
+ payload = sbuf['payload']
+ # Call Acumos microservice
+ r = requests.post(method1url, headers=method1headers, body=payload)
+ val = r.content
+ rmr.set_payload_and_length(val, sbuf)
+ sbuf = rmr.rmr_rts_msg(mrc, sbuf)
Code Review / ric-app / ml.git / commitdiff