Machine Learning Platform and Recommendation Engine built on Kubernetes

Seldon

Last update: Dec 15, 2022

Overview

Update January 2018

Seldon Core open sourced.
- Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more restricted focus to solving the final step in a machine learning project which is serving models in production. Please have a look at the project page which includes extensive documentation to investigate further.

Seldon Server : * * Archived * *

This project is not actively maintained anymore please see Seldon Core.

Seldon Server is a machine learning platform that helps your data science team deploy models into production.

It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure).

Seldon supports models built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool — it even supports models built with commercial tools and services where the model is exportable.

It includes an API with two key endpoints:

Predict - Build and deploy supervised machine learning models created in any machine learning library or framework at scale using containers and microservices.
Recommend - High-performance user activity and content based recommendation engine with various algorithms ready to run out of the box.

Other features include:

Complex dynamic algorithm configuration and combination with no downtime: run A/B and Multivariate tests, cascade algorithms and create ensembles.
Command Line Interface (CLI) for configuring and managing Seldon Server.
Secure OAuth 2.0 REST and gRPC APIs to streamline integration with your data and application.
Grafana dashboard for real-time analytics built with Kafka Streams, Fluentd and InfluxDB.

Seldon is used by some of the world's most innovative organisations — it's the perfect machine learning deployment platform for start-ups and can scale to meet the demands of large enterprises.

Get Started

It takes a few minutes to install Seldon on a Kubernetes cluster. Visit our install guide and read our tech docs.

Community & Support

Join the Seldon Users Group.
Register for our newsletter to be the first to receive updates about our products and events.
Visit our website, follow @seldon_io on Twitter and like our Facebook page.
If you're in London, meet us at TensorFlow London - a community of over 1200 data scientists that we co-organise.
We also offer commercial support plans and managed services.

License

Seldon is available under Apache Licence, Version 2.0

Comments

Error in reuters-example in the Serve Recommendations part

Hello, I am trying to run reuters-example following this http://docs.seldon.io/content-recommendation-example.html but when I execute run_recommendation_microservice.sh reuters-example seldonio/reuters-example 1.0 reuters I have this error:

deployment "reuters-example" configured service "reuters-example" configured WARNING:kazoo.client:Connection dropped: socket connection error: None WARNING:kazoo.client:Connection dropped: socket connection error: None connecting to zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181Traceback (most recent call last): File "/opt/conda/bin/seldon-cli", line 4, in <module> __import__('pkg_resources').run_script('seldon==2.0.1', 'seldon-cli') File "/opt/conda/lib/python2.7/site-packages/setuptools-18.5-py2.7.egg/pkg_resources/__init__.py", line 742, in run_script File "/opt/conda/lib/python2.7/site-packages/setuptools-18.5-py2.7.egg/pkg_resources/__init__.py", line 1667, in run_script File "/opt/conda/lib/python2.7/site-packages/seldon-2.0.1-py2.7.egg/EGG-INFO/scripts/seldon-cli", line 5, in <module> seldon.cli.start_seldoncli() File "/opt/conda/lib/python2.7/site-packages/seldon-2.0.1-py2.7.egg/seldon/cli/__init__.py", line 3, in start_seldoncli cli_main.main() File "/opt/conda/lib/python2.7/site-packages/seldon-2.0.1-py2.7.egg/seldon/cli/cli_main.py", line 346, in main start_zk_client(opts) File "/opt/conda/lib/python2.7/site-packages/seldon-2.0.1-py2.7.egg/seldon/cli/cli_main.py", line 301, in start_zk_client gdata["zk_client"].start() File "/opt/conda/lib/python2.7/site-packages/kazoo/client.py", line 546, in start raise self.handler.timeout_exception("Connection time-out") kazoo.handlers.threading.KazooTimeoutError: Connection time-out error: error executing remote command: Error executing command in container: Error executing in Docker Container: 1

PS: I have installed seldon in a cloud kubernetes cluster and i have my DNS pod which is working well.

Here are my running pods: default influxdb-grafana-7mpow 2/2 Running 0 2h kubernetes-2 default kafka-controller-q5n79 1/1 Running 14 2h kubernetes-2 default memcached1-0mm1t 1/1 Running 0 2h kubernetes-2 default memcached2-xy165 1/1 Running 0 2h kubernetes-2 default reuters-example-1808766350-tq0ic 1/1 Running 0 2h kubernetes-2 default seldon-control 1/1 Running 0 2h kubernetes-3 default seldon-server-1847842434-nyksb 2/2 Running 35 2h kubernetes-2 default spark-master-controller-xuqfw 1/1 Running 0 19h kubernetes-3 default spark-worker-controller-f2aum 1/1 Running 0 1d kubernetes-3 default spark-worker-controller-l131c 1/1 Running 0 19h kubernetes-3 my-system elasticsearch-logging-v1-d13tc 1/1 Running 0 1d kubernetes-3 my-system fluentd-elasticsearch-kubernetes-2 1/1 Running 0 2h kubernetes-2 my-system fluentd-elasticsearch-kubernetes-3 1/1 Terminating 3 1d kubernetes-3 my-system heapster-f9lsf 2/2 Running 0 1d kubernetes-3 my-system infludb-grafana-m3fqi 2/2 Running 0 1d kubernetes-3 my-system kibana-logging-v1-7c8qr 1/1 Running 2 1d kubernetes-3 my-system kube-apiserver-kubernetes-1 1/1 Running 0 2h kubernetes-1 my-system kube-controller-manager-kubernetes-1 1/1 Running 55 2h kubernetes-1 my-system kube-dns-v8-fdk73 5/5 Running 0 1d kubernetes-3 my-system kube-scheduler-kubernetes-1 1/1 Running 44 2h kubernetes-1 my-system scheduler-master-kubernetes-1 2/2 Running 15 2h kubernetes-1 Could please help me or even give me an idea to explore. Thank you in advance

Nadia

opened by ghost 19
Errors creating kubernetes/conf/examples

When I try kubectl create -f import-data-job.json I get the following error:

could not read an encoded object from import-data-job.json: API version "batch/v1" in "import-data-job.json" isn't supported, only supports API versions ["v1"]

Same thing happens with examples/ml100k/ml100k-import.json. Any hint about this issue?

Anyway this new dockerized architecture seems great! Thank you!

opened by beeva-enriqueotero 18
Can't run server with "seldon-up.sh" - mysql status is forever Pending

I run Kubernetes locally via Minikube 0.12.1. My OS: Ubuntu 16.04. RAM: 16G (and 16GB swap). Problem: when I run "seldon-up.sh" (Seldon v1.3.11), I see mysql node status is forever Pending. When I run "kubectl get events", I see this: 24m 35m 40 mysql Pod Warning FailedScheduling {default-scheduler } pod (mysql) failed to fit in any node fit failure on node (minikube): Insufficient memory

But I have 8.4GB RAM free.

opened by ukrbublik 11
Error with seldon-cli, does kubectl 1.6.2 supported?

Do you have the following error running seldon-cli?

error: expected 'exec POD_NAME COMMAND [ARG1] [ARG2] ... [ARGN]'. POD_NAME and COMMAND are required arguments for the exec command See 'kubectl exec -h' for help and examples.

Is 1.6.2 supported or should I use the older version?

my kubectl version is: Client Version: version.Info{Major:"1", Minor:"6", GitVersion:"v1.6.2", GitCommit:"477efc3cbe6a7effca06bd1452fa356e2201e1ee", GitTreeState:"clean", BuildDate:"2017-04-19T20:33:11Z", GoVersion:"go1.7.5", Compiler:"gc", Platform:"linux/amd64"} Server Version: version.Info{Major:"1", Minor:"6", GitVersion:"v1.6.2", GitCommit:"477efc3cbe6a7effca06bd1452fa356e2201e1ee", GitTreeState:"clean", BuildDate:"2017-04-19T20:22:08Z", GoVersion:"go1.7.5", Compiler:"gc", Platform:"linux/amd64"}

opened by parkerzf 9

Python support

Hello! Is it support python 3.x? Because when I started with start-microservice --type prediction --client test -i iris-xgboost seldonio/iris_xgboost:2.1 rest 1.0 after running kubernetes, it shows bug like

$ start-microservice --type prediction --client test -i iris-xgboost seldonio/iris_xgboost:2.1 rest 1.0
  File "/Users/kwangin/workspace/seldon-server/kubernetes/bin/start-microservice", line 144
    print "Replicas is ",self.replicas
                       ^
SyntaxError: Missing parentheses in call to 'print'

Also, it shows an error at python 2.x like below.

$ start-microservice --type prediction --client test -i iris-xgboost seldonio/iris_xgboost:2.1 rest 1.0
[Microservice(iris-xgboost,seldonio/iris_xgboost:2.1,rest,1.000000)]
Replicas is  1
kubectl apply -f /Users/kwangin/workspace/seldon-server/kubernetes/bin/../conf/microservices/microservice-iris-xgboost.json
deployment "iris-xgboost" created
service "iris-xgboost" created
error: expected 'exec POD_NAME COMMAND [ARG1] [ARG2] ... [ARGN]'.
POD_NAME and COMMAND are required arguments for the exec command
See 'kubectl exec -h' for help and examples.
Traceback (most recent call last):
  File "/Users/kwangin/workspace/seldon-server/kubernetes/bin/start-microservice", line 313, in <module>
    runner.run(args.type,args.client,args.i + args.p)
  File "/Users/kwangin/workspace/seldon-server/kubernetes/bin/start-microservice", line 214, in run
    self.predictCreate(client,services)
  File "/Users/kwangin/workspace/seldon-server/kubernetes/bin/start-microservice", line 203, in predictCreate
    raise MicroserviceError("failed to run seldon cli to create conf"+name)
NameError: global name 'name' is not defined

Is there something missing?

Thanks.

opened by kination 7

minikube json validation error

Minikube complains about a json validation error for mysql in the latest released version.

error validating "/seldon-server/kubernetes/bin/../conf/mysql.json": error validating data: [unexpected type: object, unexpected type: object, unexpected type: object, unexpected type: object]; if you choose to ignore these errors, turn validation off with --validate=false

opened by geoHeil 7
tagaffinityrecommender tag store not found

I'm trying tagaffinity recommender. the model looks fine. but i can't get any recommendations. I get this error in api logs 2016-05-11 13:00:02,173 DEBUG http-nio-8080-exec-10 UserTagAffinityRecommender [client] [test123] [] - Failed to get tag store for client client 2016-05-11 13:00:02,173 DEBUG http-nio-8080-exec-10 RecommendationPeer [client] [test123] [] - Recommender userTagAffinityRecommender returned 0 results what's tag store? why seldon can't find it?

In case it's related, this is my recommender configuration. /all_clients/client/algs: {"algorithms": [{"name":"userTagAffinityRecommender","filters":[],"includers":["recentItemsIncluder"], "config":[{"name":"io.seldon.algorithm.tags.attrid","value":7}, {"name":"io.seldon.algorithm.tags.useitemdim","value":"false"}, {"name":"io.seldon.algorithm.inclusion.itemsperincluder","value":1000}, {"name":"io.seldon.algorithm.clusters.minnumberitemsforvalidclusterresult","value":10}, {"name":"io.seldon.algorithm.clusters.decayratesecs","value":10800}, {"name":"io.seldon.algorithm.clusters.categorydimensionname","value":"category"}]}, {"name":"recentItemsRecommender","filters":[],"includers":[],"config":[]}], "combiner":"firstSuccessfulCombiner"}

and I got tags.attrid from item_attr table in seldon db | attr_id | name | type | item_type | semantic | | 7 | tags | VARCHAR | 1 | NULL |

opened by boogardgodig 7
recommendation filtering by dimension with MF

Hi everyone. I got this error when i try to filter a recommendation from the MF alg

2017-03-09 15:38:05,055 DEBUG http-nio-8080-exec-132 UserDimensionMappingModelManager [user1] [10206938095543821] [] - dimensions in: [74] 2017-03-09 15:38:05,055 DEBUG http-nio-8080-exec-132 UserDimensionMappingModelManager [user1] [10206938095543821] [] - No mappings for client[user1] 2017-03-09 15:38:05,055 DEBUG http-nio-8080-exec-132 UserDimensionMappingModelManager [user1] [10206938095543821] [] - dimensions out: [74]

Otherwise if i use a recentItems recommendation al the dimensions works correctly Can someone explain what this log means?

opened by Rocknpools 6
zookeeper cluster cannot communicate with each other

Hi all, when I installing the seldon, I faced a problem. After I installed kubernets, I run seldon-up.sh, the progress keep checking zookeeper status. I investigate the problem, it was caused by that in the zookeeper pods, cannot get communication with each other with the hostname. I try to using the service ip of each zookeeper, still failed. Because that the service ip cannot be accessed by the pod. can you give some advise? thanks very much !!!

opened by sbookworm 6
td-agent configuration file

I recently deployed seldon-server 1.2.3. I can add actions using the api but no new action logs are created in ${TOMCAT_HOME}/logs/fluentd/ and GroupActionsJob is not working. td-agent.conf is set to parse the previous (not json) action logs. How can I fix it?

opened by boogardgodig 6
NoTableManagedException

After the server started, I visit http://localhost:8080/seldon-server/token?consumer_key=seldonAll&consumer_secret=seldon

The server throw NoTableManagedException

2015-06-19 13:09:34,517 DEBUG http-bio-8080-exec-3 Log4JLogger.debug(58) - QueryCompilation: [filter:DyadicExpression{PrimaryExpression{consumerKey} = ParameterExpression{c}}] [symbols: c type=java.lang.String, this type=io.seldon.api.jdo.Consumer] 2015-06-19 13:09:34,519 DEBUG http-bio-8080-exec-3 Log4JLogger.debug(58) - JDOQL Query : Compiling "SELECT FROM io.seldon.api.jdo.Consumer WHERE consumerKey == c PARAMETERS java.lang.String c" for datastore 2015-06-19 13:09:40,765 WARN http-bio-8080-exec-3 Log4JLogger.warn(106) - Query for candidates of io.seldon.api.jdo.Consumer and subclasses resulted in no possible candidates Persistent class "io.seldon.api.jdo.Consumer" has no table in the database, but the operation requires it. Please check the specification of the MetaData for this class. org.datanucleus.store.rdbms.exceptions.NoTableManagedException: Persistent class "io.seldon.api.jdo.Consumer" has no table in the database, but the operation requires it. Please check the specification of the MetaData for this class. at org.datanucleus.store.rdbms.RDBMSStoreManager.getDatastoreClass(RDBMSStoreManager.java:692) at org.datanucleus.store.rdbms.query.RDBMSQueryUtils.getStatementForCandidates(RDBMSQueryUtils.java:425)

opened by vincent-chang 6
start start-microservice raise MicroserviceError("failed to run seldon cli to create conf"+name)

when I command

start-microservice --type prediction --client test -i iris-xgboost seldonio/iris_xgboost:2.1 rest 1.0

raise Exception
maybe zookeeper can not normal work

Traceback (most recent call last): File "/opt/conda/bin/seldon-cli", line 4, in connecting to zookeeper-1:2181,zookeeper-2:2181,zookeeper-3:2181 [SUCCEEDED] import('pkg_resources').run_script('seldon==2.2.6', 'seldon-cli') File "/opt/conda/lib/python2.7/site-packages/setuptools-23.0.0-py2.7.egg/pkg_resources/init.py", line 719, in run_script File "/opt/conda/lib/python2.7/site-packages/setuptools-23.0.0-py2.7.egg/pkg_resources/init.py", line 1504, in run_script File "/opt/conda/lib/python2.7/site-packages/seldon-2.2.6-py2.7.egg/EGG-INFO/scripts/seldon-cli", line 5, in seldon.cli.start_seldoncli() File "/opt/conda/lib/python2.7/site-packages/seldon-2.2.6-py2.7.egg/seldon/cli/init.py", line 3, in start_seldoncli cli_main.main() File "/opt/conda/lib/python2.7/site-packages/seldon-2.2.6-py2.7.egg/seldon/cli/cli_main.py", line 373, in main cmds[cmd](opts,command_data, command_args) File "/opt/conda/lib/python2.7/site-packages/seldon-2.2.6-py2.7.egg/seldon/cli/cmd_pred.py", line 203, in cmd_pred actions[action](command_data, opts) File "/opt/conda/lib/python2.7/site-packages/seldon-2.2.6-py2.7.egg/seldon/cli/cmd_pred.py", line 158, in action_create if not is_existing_client(zkroot, client_name): File "/opt/conda/lib/python2.7/site-packages/seldon-2.2.6-py2.7.egg/seldon/cli/cmd_pred.py", line 64, in is_existing_client client_names = os.listdir(zkroot + gdata["all_clients_node_path"]) OSError: [Errno 2] No such file or directory: '/seldon-data/conf/zkroot/all_clients' command terminated with exit code 1 Traceback (most recent call last): File "/home/muller/Documents/github/seldon-server/kubernetes/bin/start-microservice", line 313, in runner.run(args.type,args.client,args.i + args.p) File "/home/muller/Documents/github/seldon-server/kubernetes/bin/start-microservice", line 214, in run self.predictCreate(client,services) File "/home/muller/Documents/github/seldon-server/kubernetes/bin/start-microservice", line 203, in predictCreate raise MicroserviceError("failed to run seldon cli to create conf"+name) NameError: global name 'name' is not defined

NAME READY STATUS RESTARTS AGE influxdb-grafana-65fd8f899-x6r2f 2/2 Running 0 21m kafka-controller-94b9896d9-h8gzv 1/1 Running 0 17m memcached1-5ccc54f47d-6s99q 1/1 Running 0 21m memcached2-8d984bb95-52xlw 1/1 Running 0 21m mysql-c74858654-mqq52 1/1 Running 0 21m redis-74cf78b489-xlsfq 1/1 Running 0 21m seldon-control-7594c78659-7pz78 1/1 Running 0 21m spark-master-controller-7cfb76b44d-hdgvj 0/1 Pending 0 4m td-agent-server-54bd8d4f58-9xvn5 1/1 Running 0 17m zookeeper1-7769dc5c48-qvnsv 1/1 Running 0 21m zookeeper2-6754ddb54b-4qhxc 1/1 Running 0 21m zookeeper3-6749cd76d4-wd5sk 1/1 Running 0 21m

[root@localhost docker-compost]# kubectl get po NAME READY STATUS RESTARTS AGE influxdb-grafana-65fd8f899-x6r2f 2/2 Running 0 26m iris-xgboost-6ff4567f58-krsnz 1/1 Running 0 5m kafka-controller-94b9896d9-h8gzv 1/1 Running 0 22m memcached1-5ccc54f47d-6s99q 1/1 Running 0 26m memcached2-8d984bb95-52xlw 1/1 Running 0 26m mysql-c74858654-mqq52 1/1 Running 0 26m redis-74cf78b489-xlsfq 1/1 Running 0 26m seldon-control-7594c78659-7pz78 1/1 Running 0 26m spark-master-controller-7cfb76b44d-hdgvj 0/1 Pending 0 9m td-agent-server-54bd8d4f58-9xvn5 1/1 Running 0 22m zookeeper1-7769dc5c48-qvnsv 1/1 Running 0 26m zookeeper2-6754ddb54b-4qhxc 1/1 Running 0 26m zookeeper3-6749cd76d4-wd5sk 1/1 Running 0 26m

[root@localhost docker-compost]# kubectl get po NAME READY STATUS RESTARTS AGE influxdb-grafana-65fd8f899-x6r2f 2/2 Running 0 26m iris-xgboost-6ff4567f58-krsnz 1/1 Running 0 5m kafka-controller-94b9896d9-h8gzv 1/1 Running 0 22m memcached1-5ccc54f47d-6s99q 1/1 Running 0 26m memcached2-8d984bb95-52xlw 1/1 Running 0 26m mysql-c74858654-mqq52 1/1 Running 0 26m redis-74cf78b489-xlsfq 1/1 Running 0 26m seldon-control-7594c78659-7pz78 1/1 Running 0 26m spark-master-controller-7cfb76b44d-hdgvj 0/1 Pending 0 9m td-agent-server-54bd8d4f58-9xvn5 1/1 Running 0 22m zookeeper1-7769dc5c48-qvnsv 1/1 Running 0 26m zookeeper2-6754ddb54b-4qhxc 1/1 Running 0 26m zookeeper3-6749cd76d4-wd5sk 1/1 Running 0 26m

opened by mullerhai 1

configmap type volume gets mounted as EmptyDir

create file: { "apiVersion": "machinelearning.seldon.io/v1alpha1", "kind": "SeldonDeployment", "metadata": { "labels": { "app": "seldon" }, "name": "graph-baiyun" }, "spec": { "annotations": { "project_name": "baiyun", "deployment_version": "v0.1" }, "name": "graph-baiyun", "oauth_key": "baiyun", "oauth_secret": "yunbai",

    "predictors": [
        {
            "componentSpec": {
                "spec": {
                    "containers": [
                        {
                            "image": "cr.d.xiaomi.net/baiyun/seldon-server-base:0.2",
                            "imagePullPolicy": "IfNotPresent",
                            "name": "graph-regression",
                            "volumeMounts": [
                                {
                                    "name": "entrypoint",
                                    "mountPath": "/microservice"
                                }
                            ],
                            "command": ["/tmp/microservice/Ingress.sh"],
                            "resources": {
                                "requests": {
                                    "memory": "100Mi"
                                }
                            }
                        }
                    ],
                    "terminationGracePeriodSeconds": 20,
                    "volumes": [
                        {
                            "name": "entrypoint",
                            "configMap":{
                                        "name": "seldon-magic",
                                        "defaultMode": 420
                                    }
                        }
                    ]
                }
            },
            "graph": {
                "children": [],
                "name": "graph-regression",
                "endpoint": {
		"type" : "REST"
	    },
                "type": "MODEL"
            },
            "name": "baiyun",
            "replicas": 1,
	"annotations": {
	    "predictor_version" : "v0.1"
	}
        }
    ]
}

} result: kubectl describe po xxx -n seldon: Volumes: entrypoint: Type: EmptyDir (a temporary directory that shares a pod's lifetime) Medium: default-token-scs3c: Type: Secret (a volume populated by a Secret) SecretName: default-token-scs3c Optional: false

opened by bainiu87 0

Seldon building on Kubernetes

Hi, I installed Kubernetes on AWS using the official guidance. Now, I have a node master (m3.medium) and two workers (t2.medium) on which I need to run Seldon. I'm using the official guidance: http://docs.seldon.io/install.html But when i run the seldon-up.sh on Kubernetes, I obtained the following error during the creation of containers seldon-control-3388397084-pvr8k and influxdb-grafana-3089120915-fzxt4 :

Multi-Attach error for volume “pvc-d97313ee-93e1-11e7-ae85-02f1ec9e7a70” Volume is already exclusively attached to one node and can’t be attached to another Unable to mount volumes for pod “influxdb-grafana-3089120915-fzxt4_default(e21ad2fc-93e1-11e7-ae85-02f1ec9e7a70)“: timeout expired waiting for volumes to attach/mount for pod “default”/“influxdb-grafana-3089120915-fzxt4". list of unattached/unmounted volumes=[storage] Error syncing pod

How could I solve this issue?? Thank you for your time in advance.

opened by Anto188bas 0
Minor code fix in import_actions_utils.py

action["client_userid"] = f["item_id"] changed to => action["client_userid"] = f["user_id"] action["client_itemid"] = f["user_id"] changed to => action["client_itemid"] = f["item_id"]

It is a minor error and doesn't seem to impact the output, since the model build doesn't seem to be using the client_userid and client_itemid fields. I did verify the first few lines of the actions.json file generated after the fix and it looked correct.

I ran the ml100k example and the recommendations generated after the fix were similar to those before the fix.

opened by mmulchandani 0
CORS: "Access-Control-Allow-Origin"

Is there currently support for adding the "Access-Control-Allow-Origin" header to the REST API requests? Nearly all modern browsers are set to block those sorts of requests without that header.

(Obviously this is ignoring JSONP, which has its share of security concerns.)

opened by thesuperzapper 1

Releases(v1.4.10)

v1.4.10(Nov 1, 2017)

Maintenance release. Issues with oauth REST api fixed.
Source code(tar.gz)
Source code(zip)
v1.4.9(Oct 24, 2017)

Maintenance release. Issue with seldon-control container fixed.
Source code(tar.gz)
Source code(zip)
v1.4.8(Oct 20, 2017)
This is a maintenance release.

Includes merge request for seldon-cli improvement

Updates to latest Tomcat and Hadoop versions

Source code(tar.gz)
Source code(zip)
v1.4.7(Jun 29, 2017)

Bug fix release. Update "seldon-cli client --action processactions" to use Spark memory settings.
Source code(tar.gz)
Source code(zip)
v1.4.6(Jun 22, 2017)
Maintenance release

Kubernetes default memory requests and limits set for core services

GlusterFS persistent volume claim updated to work with Kubernetes 1.6

Spark driver/executor memory can be set from Luigi and seldon-cli

Kafka stream processing simplified for API stats on dashboard

Source code(tar.gz)
Source code(zip)
v1.4.5(May 22, 2017)
Google Cloud ingress to allow TLS security over external IP to Seldon API on Google Container Platform

Updated Kafka-stream analytics using 0.10.2.1 of kafka/kafka-streams and running as Kubernetes Deployments

Several small bug fixes

Source code(tar.gz)
Source code(zip)
v1.4.4(May 4, 2017)
Product Naming Update Seldon’s current open-source platform has been renamed Seldon Core to allow us to distinguish our open-source machine learning deployment platform from a new product that we are currently working on. We will share more info with you soon and will reach out to this group for beta testers.

Seldon Core 1.4.4. includes various updates:

Kubernetes persistent volumes are abstracted by using Persistent Volume Claims. Fixes issue #50.

All components run as Kubernetes Deployments. Fixes issue #49.

Initial beta ability to run external Google MySQL server outside Kubernetes.

Updated luigi configuration for describing ML pipelines and built-in algorithms.

Thanks to @colemickens for submitting both of the above issues!
Source code(tar.gz)
Source code(zip)
v1.4.3(Apr 6, 2017)

Bug fix release for kubernetes configuration
Source code(tar.gz)
Source code(zip)
v1.4.2(Mar 28, 2017)

Maintenance release. Docs for anomaly detection demo updated and python libraries in docker fixed.
Source code(tar.gz)
Source code(zip)
v1.4.1(Mar 27, 2017)

Maintenance release with updates to built-in recommendation algorithms. Initial code for Anomaly Detection.
Source code(tar.gz)
Source code(zip)
v1.4(Dec 19, 2016)

This release contains a gRPC interface for prediction calls to complement the REST APIs. Details can be found here along with a benchmarking test.

This release contains a breaking change in the prediction REST and JS API calls for the format to pass in data.

There are also changes to the seldon scripts including a new script to start REST and RPC microservices and one to start locust load tests.
Source code(tar.gz)
Source code(zip)
v1.3.11(Oct 6, 2016)
bug fix release for Flask microservices

scikit example for iris datatset

Source code(tar.gz)
Source code(zip)
v1.3.10(Oct 3, 2016)
Adds initial A/B testing for prediction endpoint.

Bug fixes for Luigi item-similarity job

Source code(tar.gz)
Source code(zip)
v1.3.9(Sep 21, 2016)
Added new demo: digits recogniser based on tensorflow’s tutorial "deep MNIST for experts"

seldon-cli updates

Source code(tar.gz)
Source code(zip)
v1.3.8(Aug 1, 2016)

A minor release adding a static recommender that pulls precomputed recommendations from a DB.
Source code(tar.gz)
Source code(zip)
v1.3.7(Jul 27, 2016)

This a maintenance release
Source code(tar.gz)
Source code(zip)
v1.3.6(Jul 13, 2016)

This a maintenance release
Source code(tar.gz)
Source code(zip)
v1.3.5(Jul 6, 2016)

Updates to allow use with Kubernetes minikube for local testing
Source code(tar.gz)
Source code(zip)
v1.3.4(Jun 20, 2016)
New sentiment prediction demo

Easier modification of resources and settings for Tomcat, Mysql for benchmarking

Source code(tar.gz)
Source code(zip)
v1.3.3(Jun 14, 2016)
Updated analytics jobs using Kafka Streams

New Movielens 10 Million Demo

Guide to benchmarking Seldon with load testing for production deployment

For more information please read our 1.3.3 release blog post.
Source code(tar.gz)
Source code(zip)
v1.3.2(Jun 1, 2016)

Added Grafana Dashboards for real-time analytics.

For more information please read our 1.3.2 release blog post.
Source code(tar.gz)
Source code(zip)
v1.3.1(May 24, 2016)

Bug fix release. Fixes for kubernetes deployment
Source code(tar.gz)
Source code(zip)
v1.3(Apr 18, 2016)
Release 1.3 provides Seldon running as docker containers orchestrated within a Kubernetes cluster. By leveraging Kubernetes Seldon can easily be run on cloud (AWS, Google, Azure) or on-premise. This release also provides easier control of Seldon via the seldon-cli. Full docs can be found at http://docs.seldon.io

Highlights:

Seldon on Kubernetes.

Single command install with seldon-up.sh

Example configurations for HostPath or GlusterFS included for persistent data, but any Kubernetes persistent volume can be used.

Seldon-cli

Modelling jobs using luigi

Simple examples for basic recommendation and prediction using reuters, iris and Movielens 100k data sets. We will be adding more examples soon.

For more information please read our 1.3 release blog post.
Source code(tar.gz)
Source code(zip)
v1.3-alpha.1(Apr 12, 2016)

1.3 alpha 1 release for Kubernetes and Seldon CLI updates.
Source code(tar.gz)
Source code(zip)
v1.2.3(Mar 28, 2016)

bug fix release
Source code(tar.gz)
Source code(zip)
V1.1(Jan 15, 2016)
includes beta shell interface to control Seldon

Source code(tar.gz)
Source code(zip)
V0.99(Nov 18, 2015)
Updates python pipelines to be scikit-learn compatible and allow use with pandas data frames. online docs

Source code(tar.gz)
Source code(zip)
v0.98(Oct 21, 2015)

This release contains an update to Python feature transformation library to use pandas internally.
Source code(tar.gz)
Source code(zip)
v0.97(Sep 9, 2015)
This release provides the ability to create predictive pipelines for multi-class classification models.

Create feature extraction and manipulation pipelines in python to create appropriate features for training machine learning models. Automatically load and run the same transformations at runtime when receiving features to provide predictions on. Feature transformations include:

TFIDF feaures with chi-squared feature selection

Automatic detection of categorical, date and numeric features with normalisation of numeric features

Simple pipeline and transformation classes that can be extended to create custom feature transformations

Create classification models using Vowpal Wabbit and XGBoost

Example microservices for runtime scoring that load and run feature pipelines and predict against Vowpal Wabbit and XGBoost models

For further technical docs please see: http://docs.seldon.io/prediction-overview.html We provide a demo for creating a multi-class classification predictive endpoint for the classic Iris classification task: http://docs.seldon.io/iris-demo.html

Source code(tar.gz)
Source code(zip)
v0.96.2(Aug 25, 2015)

Bug fix release
Source code(tar.gz)
Source code(zip)