It is the UR so Events are taken from the EventStore and converted into a
Mahout DistributedRowMatrix of RandomAccessSparseVectors, which are both
serializable. This path works fine and has for several years.
This must be a config problem, like not using the MahoutKryoRegistrator, which
registers the serializers for these.
@Noelia, you have left out the sparkConf section of the engine.json. The one
used in the integration test should work:
{
"comment":" This config file uses default settings for all but the required
values see README.md for docs",
"id": "default",
"description": "Default settings",
"engineFactory": "com.actionml.RecommendationEngine",
"datasource": {
"params" : {
"name": "tiny_app_data.csv",
"appName": "TinyApp",
"eventNames": ["view"]
}
},
"sparkConf": { <================= THIS WAS LEFT OUT IN YOUR ENGINE.JSON BELOW
IN THIS THREAD
"spark.serializer": "org.apache.spark.serializer.KryoSerializer",
"spark.kryo.registrator":
"org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator",
"spark.kryo.referenceTracking": "false",
"spark.kryoserializer.buffer": "300m",
"es.index.auto.create": "true"
},
"algorithms": [
{
"comment": "simplest setup where all values are default, popularity based
backfill, must add eventsNames",
"name": "ur",
"params": {
"appName": "TinyApp",
"indexName": "urindex",
"typeName": "items",
"comment": "must have data for the first event or the model will not
build, other events are optional",
"eventNames": ["view"]
}
}
]
}
On Oct 18, 2017, at 8:49 AM, Donald Szeto <don...@apache.org> wrote:
Chiming in a bit. Looking at the serialization error, it looks like we are just
one little step away from getting this to work.
Noelia, what does your synthesized data look like? All data that is processed
by Spark needs to be serializable. At some point, a non-serializable vector
object showing in the stack is created out of your synthesized data. It would
be great to know what your input event looks like and see where in the code
path has caused this.
Regards,
Donald
On Tue, Oct 17, 2017 at 12:14 AM Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@vicomtech.org>> wrote:
Pat, you mentioned the problem could be that the data I was using was too
small. So now I'm using the attached data file as the data (4 users and 100
items). But I'm still getting the same error. I'm sorry I forgot to mention I
had increased the dataset.
The reason why I want to make it work with a very small dataset is because I
want to be able to follow the calculations. I want to understand what the UR is
doing and understand the impact of changing this or that, here or there... I
find that easier to achieve with a small example in which I know exactly what's
happening. I want to build my trust on my understanding of the UR before I move
on to applying it to a real problem. If I'm not confident that I know how to
use it, how can I tell my client that the results I'm getting are good with any
degree of confidence?
On 16 October 2017 at 20:44, Pat Ferrel <p...@occamsmachete.com
<mailto:p...@occamsmachete.com>> wrote:
So all setup is the same for the integration-test and your modified test
*except the data*?
The error looks like a setup problem because the serialization should happen
with either test. But if the only difference really is the data, then toss it
and use either real data or the integration test data, why are you trying to
synthesize fake data if it causes the error?
BTW the data you include below in this thread would never create internal IDs
as high as 94 in the vector. You must have switched to a new dataset???
I would get a dump of your data using `pio export` and make sure it’s what you
thought it was. You claim to have only 4 user ids and 4 item ids but the
serialized vector thinks you have at least 94 of user or item ids. Something
doesn’t add up.
On Oct 16, 2017, at 4:43 AM, Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@vicomtech.org>> wrote:
Pat, you are absolutely right! I increased the sleep time and now the
integration test for handmade works perfectly.
However, the integration test adapted to run with my tiny app runs into the
same problem I've been having with this app:
[ERROR] [TaskSetManager] Task 1.0 in stage 10.0 (TID 23) had a not serializable
result: org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
- object not serializable (class:
org.apache.mahout.math.RandomAccessSparseVector, value:
{66:1.0,29:1.0,70:1.0,91:1.0,58:1.0,37:1.0,13:1.0,8:1.0,94:1.0,30:1.0,57:1.0,22:1.0,20:1.0,35:1.0,97:1.0,60:1.0,27:1.0,72:1.0,3:1.0,34:1.0,77:1.0,46:1.0,81:1.0,86:1.0,43:1.0})
- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
- object (class scala.Tuple2,
(1,{66:1.0,29:1.0,70:1.0,91:1.0,58:1.0,37:1.0,13:1.0,8:1.0,94:1.0,30:1.0,57:1.0,22:1.0,20:1.0,35:1.0,97:1.0,60:1.0,27:1.0,72:1.0,3:1.0,34:1.0,77:1.0,46:1.0,81:1.0,86:1.0,43:1.0}));
not retrying
[ERROR] [TaskSetManager] Task 2.0 in stage 10.0 (TID 24) had a not serializable
result: org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
...
Any ideas?
On 15 October 2017 at 19:09, Pat Ferrel <p...@occamsmachete.com
<mailto:p...@occamsmachete.com>> wrote:
This is probably a timing issue in the integration test, which has to wait for
`pio deploy` to finish before the queries can be made. If it doesn’t finish the
queries will fail. By the time the rest of the test quits the model has been
deployed so you can run queries. In the integration-test script increase the
delay after `pio deploy…` and see if it passes then.
This is probably an integrtion-test script problem not a problem in the system
On Oct 6, 2017, at 4:21 AM, Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@vicomtech.org>> wrote:
Pat,
I have run the integration test for the handmade example out of curiosity.
Strangely enough things go more or less as expected apart from the fact that I
get a message saying:
...
[INFO] [CoreWorkflow$] Updating engine instance
[INFO] [CoreWorkflow$] Training completed successfully.
Model will remain deployed after this test
Waiting 30 seconds for the server to start
nohup: redirecting stderr to stdout
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:--
0curl: (7) Failed to connect to localhost port 8000: Connection refused
So the integration test does not manage to get the recommendations even though
the model trained and deployed successfully. However, as soon as the
integration test finishes, on the same terminal, I can get the recommendations
by doing the following:
$ curl -H "Content-Type: application/json" -d '
> {
> "user": "u1"
> }' http://localhost:8000/queries.json <http://localhost:8000/queries.json>
{"itemScores":[{"item":"Nexus","score":0.057719700038433075},{"item":"Surface","score":0.0}]}
Isn't this odd? Can you guess what's going on?
Thank you very much for all your support!
noelia
On 5 October 2017 at 19:22, Pat Ferrel <p...@occamsmachete.com
<mailto:p...@occamsmachete.com>> wrote:
Ok, that config should work. Does the integration test pass?
The data you are using is extremely small and though it does look like it has
cooccurrences, they may not meet minimum “big-data” thresholds used by default.
Try adding more data or use the handmade example data, rename purchase to view
and discard the existing view data if you wish.
The error is very odd and I’ve never seen it. If the integration test works I
can only surmise it's your data.
On Oct 5, 2017, at 12:02 AM, Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@vicomtech.org>> wrote:
SPARK: spark-1.6.3-bin-hadoop2.6
PIO: 0.11.0-incubating
Scala: whatever gets installed when installing PIO 0.11.0-incubating, I haven't
installed Scala separately
UR: ActionML's UR v0.6.0 I suppose as that's the last version mentioned in the
readme file. I have attached the UR zip file I downloaded from the actionml
github account.
Thank you for your help!!
On 4 October 2017 at 17:20, Pat Ferrel <p...@occamsmachete.com
<mailto:p...@occamsmachete.com>> wrote:
What version of Scala. Spark, PIO, and UR are you using?
On Oct 4, 2017, at 6:10 AM, Noelia Osés Fernández <no...@vicomtech.org
<mailto:no...@vicomtech.org>> wrote:
Hi all,
I'm still trying to create a very simple app to learn to use PredictionIO and
still having trouble. I have done pio build no problem. But when I do pio train
I get a very long error message related to serialisation (error message copied
below).
pio status reports system is all ready to go.
The app I'm trying to build is very simple, it only has 'view' events. Here's
the engine.json:
===========================================================
{
"comment":" This config file uses default settings for all but the required
values see README.md for docs",
"id": "default",
"description": "Default settings",
"engineFactory": "com.actionml.RecommendationEngine",
"datasource": {
"params" : {
"name": "tiny_app_data.csv",
"appName": "TinyApp",
"eventNames": ["view"]
}
},
"algorithms": [
{
"comment": "simplest setup where all values are default, popularity based
backfill, must add eventsNames",
"name": "ur",
"params": {
"appName": "TinyApp",
"indexName": "urindex",
"typeName": "items",
"comment": "must have data for the first event or the model will not
build, other events are optional",
"eventNames": ["view"]
}
}
]
}
===========================================================
The data I'm using is:
"u1","i1"
"u2","i1"
"u2","i2"
"u3","i2"
"u3","i3"
"u4","i4"
meaning user u viewed item i.
The data has been added to the database with the following python code:
===========================================================
"""
Import sample data for recommendation engine
"""
import predictionio
import argparse
import random
RATE_ACTIONS_DELIMITER = ","
SEED = 1
def import_events(client, file):
f = open(file, 'r')
random.seed(SEED)
count = 0
print "Importing data..."
items = []
users = []
f = open(file, 'r')
for line in f:
data = line.rstrip('\r\n').split(RATE_ACTIONS_DELIMITER)
users.append(data[0])
items.append(data[1])
client.create_event(
event="view",
entity_type="user",
entity_id=data[0],
target_entity_type="item",
target_entity_id=data[1]
)
print "Event: " + "view" + " entity_id: " + data[0] + " target_entity_id: "
+ data[1]
count += 1
f.close()
users = set(users)
items = set(items)
print "All users: " + str(users)
print "All items: " + str(items)
for item in items:
client.create_event(
event="$set",
entity_type="item",
entity_id=item
)
count += 1
print "%s events are imported." % count
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Import sample data for recommendation engine")
parser.add_argument('--access_key', default='invald_access_key')
parser.add_argument('--url', default="http://localhost:7070
<http://localhost:7070/>")
parser.add_argument('--file', default="./data/tiny_app_data.csv")
args = parser.parse_args()
print args
client = predictionio.EventClient(
access_key=args.access_key,
url=args.url,
threads=5,
qsize=500)
import_events(client, args.file)
===========================================================
My pio_env.sh is the following:
===========================================================
#!/usr/bin/env bash
#
# Copy this file as pio-env.sh and edit it for your site's configuration.
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
<http://www.apache.org/licenses/LICENSE-2.0>
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# PredictionIO Main Configuration
#
# This section controls core behavior of PredictionIO. It is very likely that
# you need to change these to fit your site.
# SPARK_HOME: Apache Spark is a hard dependency and must be configured.
# SPARK_HOME=$PIO_HOME/vendors/spark-2.0.2-bin-hadoop2.7
SPARK_HOME=$PIO_HOME/vendors/spark-1.6.3-bin-hadoop2.6
POSTGRES_JDBC_DRIVER=$PIO_HOME/lib/postgresql-42.1.4.jar
MYSQL_JDBC_DRIVER=$PIO_HOME/lib/mysql-connector-java-5.1.41.jar
# ES_CONF_DIR: You must configure this if you have advanced configuration for
# your Elasticsearch setup.
# ES_CONF_DIR=/opt/elasticsearch
#ES_CONF_DIR=$PIO_HOME/vendors/elasticsearch-1.7.6
# HADOOP_CONF_DIR: You must configure this if you intend to run PredictionIO
# with Hadoop 2.
# HADOOP_CONF_DIR=/opt/hadoop
# HBASE_CONF_DIR: You must configure this if you intend to run PredictionIO
# with HBase on a remote cluster.
# HBASE_CONF_DIR=$PIO_HOME/vendors/hbase-1.0.0/conf
# Filesystem paths where PredictionIO uses as block storage.
PIO_FS_BASEDIR=$HOME/.pio_store
PIO_FS_ENGINESDIR=$PIO_FS_BASEDIR/engines
PIO_FS_TMPDIR=$PIO_FS_BASEDIR/tmp
# PredictionIO Storage Configuration
#
# This section controls programs that make use of PredictionIO's built-in
# storage facilities. Default values are shown below.
#
# For more information on storage configuration please refer to
# http://predictionio.incubator.apache.org/system/anotherdatastore/
<http://predictionio.incubator.apache.org/system/anotherdatastore/>
# Storage Repositories
# Default is to use PostgreSQL
PIO_STORAGE_REPOSITORIES_METADATA_NAME=pio_meta
PIO_STORAGE_REPOSITORIES_METADATA_SOURCE=ELASTICSEARCH
PIO_STORAGE_REPOSITORIES_EVENTDATA_NAME=pio_event
PIO_STORAGE_REPOSITORIES_EVENTDATA_SOURCE=HBASE
PIO_STORAGE_REPOSITORIES_MODELDATA_NAME=pio_model
PIO_STORAGE_REPOSITORIES_MODELDATA_SOURCE=LOCALFS
# Storage Data Sources
# PostgreSQL Default Settings
# Please change "pio" to your database name in PIO_STORAGE_SOURCES_PGSQL_URL
# Please change PIO_STORAGE_SOURCES_PGSQL_USERNAME and
# PIO_STORAGE_SOURCES_PGSQL_PASSWORD accordingly
PIO_STORAGE_SOURCES_PGSQL_TYPE=jdbc
PIO_STORAGE_SOURCES_PGSQL_URL=jdbc:postgresql://localhost/pio <>
PIO_STORAGE_SOURCES_PGSQL_USERNAME=pio
PIO_STORAGE_SOURCES_PGSQL_PASSWORD=pio
# MySQL Example
# PIO_STORAGE_SOURCES_MYSQL_TYPE=jdbc
# PIO_STORAGE_SOURCES_MYSQL_URL=jdbc:mysql://localhost/pio <>
# PIO_STORAGE_SOURCES_MYSQL_USERNAME=pio
# PIO_STORAGE_SOURCES_MYSQL_PASSWORD=pio
# Elasticsearch Example
# PIO_STORAGE_SOURCES_ELASTICSEARCH_TYPE=elasticsearch
# PIO_STORAGE_SOURCES_ELASTICSEARCH_HOSTS=localhost
# PIO_STORAGE_SOURCES_ELASTICSEARCH_PORTS=9200
# PIO_STORAGE_SOURCES_ELASTICSEARCH_SCHEMES=http
# PIO_STORAGE_SOURCES_ELASTICSEARCH_HOME=$PIO_HOME/vendors/elasticsearch-5.2.1
# Elasticsearch 1.x Example
PIO_STORAGE_SOURCES_ELASTICSEARCH_TYPE=elasticsearch
PIO_STORAGE_SOURCES_ELASTICSEARCH_CLUSTERNAME=myprojectES
PIO_STORAGE_SOURCES_ELASTICSEARCH_HOSTS=localhost
PIO_STORAGE_SOURCES_ELASTICSEARCH_PORTS=9300
PIO_STORAGE_SOURCES_ELASTICSEARCH_HOME=$PIO_HOME/vendors/elasticsearch-1.7.6
# Local File System Example
PIO_STORAGE_SOURCES_LOCALFS_TYPE=localfs
PIO_STORAGE_SOURCES_LOCALFS_PATH=$PIO_FS_BASEDIR/models
# HBase Example
PIO_STORAGE_SOURCES_HBASE_TYPE=hbase
PIO_STORAGE_SOURCES_HBASE_HOME=$PIO_HOME/vendors/hbase-1.2.6
===========================================================
Error message:
===========================================================
[ERROR] [TaskSetManager] Task 2.0 in stage 10.0 (TID 24) had a not serializable
result: org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
- object not serializable (class:
org.apache.mahout.math.RandomAccessSparseVector, value: {3:1.0,2:1.0})
- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
- object (class scala.Tuple2, (2,{3:1.0,2:1.0})); not retrying
[ERROR] [TaskSetManager] Task 3.0 in stage 10.0 (TID 25) had a not serializable
result: org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
- object not serializable (class:
org.apache.mahout.math.RandomAccessSparseVector, value: {0:1.0,3:1.0})
- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
- object (class scala.Tuple2, (3,{0:1.0,3:1.0})); not retrying
[ERROR] [TaskSetManager] Task 1.0 in stage 10.0 (TID 23) had a not serializable
result: org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
- object not serializable (class:
org.apache.mahout.math.RandomAccessSparseVector, value: {1:1.0})
- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
- object (class scala.Tuple2, (1,{1:1.0})); not retrying
[ERROR] [TaskSetManager] Task 0.0 in stage 10.0 (TID 22) had a not serializable
result: org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
- object not serializable (class:
org.apache.mahout.math.RandomAccessSparseVector, value: {0:1.0})
- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
- object (class scala.Tuple2, (0,{0:1.0})); not retrying
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to
stage failure: Task 2.0 in stage 10.0 (TID 24) had a not serializable result:
org.apache.mahout.math.RandomAccessSparseVector
Serialization stack:
- object not serializable (class:
org.apache.mahout.math.RandomAccessSparseVector, value: {3:1.0,2:1.0})
- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
- object (class scala.Tuple2, (2,{3:1.0,2:1.0}))
at org.apache.spark.scheduler.DAGScheduler.org
<http://org.apache.spark.scheduler.dagscheduler.org/>$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)
at org.apache.spark.rdd.RDD$$anonfun$fold$1.apply(RDD.scala:1088)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.fold(RDD.scala:1082)
at org.apache.mahout.sparkbindings.drm.CheckpointedDrmSpark.com
<http://s.drm.checkpointeddrmspark.com/>puteNRow(CheckpointedDrmSpark.scala:188)
at
org.apache.mahout.sparkbindings.drm.CheckpointedDrmSpark.nrow$lzycompute(CheckpointedDrmSpark.scala:55)
at
org.apache.mahout.sparkbindings.drm.CheckpointedDrmSpark.nrow(CheckpointedDrmSpark.scala:55)
at
org.apache.mahout.sparkbindings.drm.CheckpointedDrmSpark.newRowCardinality(CheckpointedDrmSpark.scala:219)
at com.actionml.IndexedDatasetSpark$.apply(Preparator.scala:213)
at com.actionml.Preparator$$anonfun$3.apply(Preparator.scala:71)
at com.actionml.Preparator$$anonfun$3.apply(Preparator.scala:49)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at com.actionml.Preparator.prepare(Preparator.scala:49)
at com.actionml.Preparator.prepare(Preparator.scala:32)
at
org.apache.predictionio.controller.PPreparator.prepareBase(PPreparator.scala:37)
at org.apache.predictionio.controller.Engine$.train(Engine.scala:671)
at org.apache.predictionio.controller.Engine.train(Engine.scala:177)
at
org.apache.predictionio.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:67)
at
org.apache.predictionio.workflow.CreateWorkflow$.main(CreateWorkflow.scala:250)
at
org.apache.predictionio.workflow.CreateWorkflow.main(CreateWorkflow.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
===========================================================
Thank you all for your help.
Best regards,
noelia
--
<http://www.vicomtech.org/>
Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior
no...@vicomtech.org <mailto:no...@vicomtech.org>
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
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--
<http://www.vicomtech.org/>
Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior
no...@vicomtech.org <mailto:no...@vicomtech.org>
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos para Energía y Procesos
Industriales
<https://www.linkedin.com/company/vicomtech>
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+[34] 943 30 92 30
Data Intelligence for Energy and
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