Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
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kserve | kserve-master/docs/samples/gcp-iap/README.md | ## KFServing GCP/IAP Example
When using Kubeflow with GCP it is common to use a [GCP Identity Aware Proxy](https://cloud.google.com/iap) (IAP) to manage client authentication to the KFServing endpoints. The proxy intercepts and authenticates users and passes identity assertion (JWT) to kubernetes service/pods. Whils... | 8,988 | 55.892405 | 514 | md |
kserve | kserve-master/docs/samples/gcp-iap/iap_request_auth.py | # -*- coding: utf-8 -*-
# Reference:
# https://cloud.google.com/iap/docs/authentication-howto#iap-make-request-python
# https://github.com/kubeflow/pipelines/blob/85cb99173dead8bd2ca09c8e040b137f59d00ad7/sdk/python/kfp/_auth.py#L183
# This is a helper function to make KFserving inferenceService
# call to a GCP cluster ... | 5,807 | 36.714286 | 114 | py |
kserve | kserve-master/docs/samples/gcp-iap/make-prediction-auth.sh | # This script explains how to make a http request to an inference service hosted behind a GCP IAP.
# Prerequisites:
# set following parameters:
# - ${PROJECT} - the gcp project that owns the Identity Aware Proxy https://console.cloud.google.com/security/iap
# - ${NAMESPACE} - the k8s namespace the inference servic... | 1,835 | 50 | 137 | sh |
kserve | kserve-master/docs/samples/gcp-iap/make-prediction.sh | # This script explains how to make a http request to an inference service hosted behind a GCP IAP. It handles obtaining and using a JWT.
# Pre-requisites:
# 1. download service account key
# - gcloud iam service-accounts keys create key.json --iam-account <service-account>@<project>.iam.gserviceaccount.com
# Se... | 1,940 | 57.818182 | 137 | sh |
kserve | kserve-master/docs/samples/gcp-iap/sklearn-iap-no-authz.yaml | apiVersion: "serving.kubeflow.org/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iap"
namespace: <namespace> # Set the user namespace your profile is in.
labels:
app: kfserving-app
annotations:
sidecar.istio.io/inject: "false"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving... | 351 | 24.142857 | 69 | yaml |
kserve | kserve-master/docs/samples/gcp-iap/sklearn-iap-with-authz.yaml | apiVersion: "serving.kubeflow.org/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iap"
namespace: <namespace> # Set the user namespace your profile is in.
labels:
app: kfserving-app
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/iris"
| 299 | 24 | 69 | yaml |
kserve | kserve-master/docs/samples/gcp-iap/virtual-service.yaml | apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: kfserving-iap
namespace: <namespace> # Set the user namespace your profile is in.
labels:
app: kfserving-app
spec:
gateways:
- kubeflow/kubeflow-gateway
hosts:
- '*'
http:
- match:
- uri:
prefix: /kfserving/<... | 650 | 22.25 | 77 | yaml |
kserve | kserve-master/docs/samples/graph/README.md | - [**Inference Graph**](#inference-graph)
- [**1. Problem Statement**](#1-problem-statement)
- [**2. KServe Inference Graph**](#2-kserve-inference-graph)
- [**2.1 Inference Graph**](#21-inference-graph)
- [**2.2 Sequence Node**](#22-sequence-node)
- [**2.3 Switch Node**](#23-switch-node)
- [**2.4 En... | 11,537 | 45.337349 | 432 | md |
kserve | kserve-master/docs/samples/graph/ensemble.yaml | ---
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
---
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "xgboost-iris"
spec:
predict... | 693 | 20.030303 | 68 | yaml |
kserve | kserve-master/docs/samples/graph/sequence.yaml | ---
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
---
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "xgboost-iris"
spec:
predict... | 659 | 19.625 | 68 | yaml |
kserve | kserve-master/docs/samples/graph/splitter.yaml | ---
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
---
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "xgboost-iris"
spec:
predict... | 677 | 19.545455 | 68 | yaml |
kserve | kserve-master/docs/samples/graph/switch.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "single-1"
spec:
predictor:
minReplicas: 1
containers:
- image: iamlovingit/bgtest:latest
name: custom
env:
- name: "target"
value: "single-1"
---
apiVersion: "serving.kserve.io/v1beta1"
kind: "Inf... | 1,362 | 22.5 | 71 | yaml |
kserve | kserve-master/docs/samples/graph/bgtest/README.md | ### Build bgtest image
```
1. cd bgtest;go mod vendor; cd -
2. docker build -t {$your-dockerhub-name}/bgtest:latest .
3. docker push {$your-dockerhub-name}/bgtest:latest
``` | 173 | 28 | 57 | md |
kserve | kserve-master/docs/samples/graph/bgtest/bgtest/main.go | package main
import (
"fmt"
"net/http"
"os"
"github.com/gin-gonic/gin"
"github.com/gin-gonic/gin/binding"
)
type Instance struct {
Name string `json:"name"`
IntVal int `json:"intval"`
StrVal string `json:"strval"`
}
type Response struct {
Source string `json:"from"`
Instances []Instance `json:... | 812 | 18.357143 | 72 | go |
kserve | kserve-master/docs/samples/istio-dex/README.md | # KFServing on Kubeflow with Istio-Dex
This example shows how to create a KFServing InferenceService as well as sending a prediction request to the InferenceService in an Istio-Dex environment.
We will be using the [SKLearn example](/docs/samples/v1beta1/sklearn/v1) to create our InferenceService.
## Setup
Deploy a ... | 3,817 | 34.682243 | 190 | md |
kserve | kserve-master/docs/samples/istio-dex/sklearn.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
annotations:
sidecar.istio.io/inject: "false"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
| 251 | 21.909091 | 68 | yaml |
kserve | kserve-master/docs/samples/kafka/README.md | Please refer to [kafka example](https://github.com/kserve/website/blob/main/docs/modelserving/kafka/kafka.md)
| 110 | 54.5 | 109 | md |
kserve | kserve-master/docs/samples/kafka/addressable_resolver.yaml | apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: inferenceservice-addressable-resolver
labels:
contrib.eventing.knative.dev/release: devel
duck.knative.dev/addressable: "true"
# Do not use this role directly. These rules will be added to the "addressable-resolver" role.
rules:
- ... | 481 | 24.368421 | 94 | yaml |
kserve | kserve-master/docs/samples/kafka/kafka_source.yaml | apiVersion: sources.knative.dev/v1beta1
kind: KafkaSource
metadata:
name: kafka-source
spec:
consumerGroup: knative-group
# Broker URL. Replace this with the URLs for your kafka cluster,
# which is in the format of my-cluster-kafka-bootstrap.my-kafka-namespace:9092.
bootstrapServers:
- kafka-headless.defa... | 511 | 25.947368 | 81 | yaml |
kserve | kserve-master/docs/samples/kafka/minio.yaml | apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: minio
name: minio
spec:
progressDeadlineSeconds: 600
replicas: 1
revisionHistoryLimit: 10
selector:
matchLabels:
app: minio
strategy:
type: Recreate
template:
metadata:
labels:
app: minio
spec:
cont... | 940 | 17.45098 | 57 | yaml |
kserve | kserve-master/docs/samples/kafka/mnist_kafka.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: mnist
spec:
predictor:
minReplicas: 1
tensorflow:
resources:
limits:
cpu: 100m
memory: 1Gi
requests:
cpu: 100m
memory: 1Gi
runtimeVersion: 1.14.0
storageUri:... | 609 | 19.333333 | 46 | yaml |
kserve | kserve-master/docs/samples/kafka/mnist_kafka_new.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: mnist
spec:
predictor:
minReplicas: 1
model:
modelFormat:
name: tensorflow
resources:
limits:
cpu: 100m
memory: 1Gi
requests:
cpu: 100m
memory: 1Gi
r... | 648 | 19.28125 | 46 | yaml |
kserve | kserve-master/docs/samples/kafka/s3_secret.yaml | apiVersion: v1
kind: Secret
metadata:
name: mysecret
annotations:
serving.kserve.io/s3-endpoint: minio-service:9000 # replace with your s3 endpoint
serving.kserve.io/s3-usehttps: "0" # by default 1, for testing with minio you need to set to 0
type: Opaque
data:
AWS_ACCESS_KEY_ID: bWluaW8=
AWS_SECRET_ACC... | 440 | 21.05 | 98 | yaml |
kserve | kserve-master/docs/samples/kafka/setup.py | #
# 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 t... | 1,191 | 29.564103 | 74 | py |
kserve | kserve-master/docs/samples/kafka/transformer.Dockerfile | FROM python:3.9-slim
RUN apt-get update && apt-get install -y libglib2.0-0
RUN pip install --no-cache-dir --upgrade pip && pip install --no-cache-dir kserve
COPY . .
RUN pip install --no-cache-dir -e .
ENTRYPOINT ["python", "-m", "image_transformer"]
| 252 | 30.625 | 81 | Dockerfile |
kserve | kserve-master/docs/samples/kafka/image_transformer/__init__.py | #
# 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 t... | 545 | 41 | 74 | py |
kserve | kserve-master/docs/samples/kafka/image_transformer/__main__.py | #
# 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 t... | 1,187 | 37.322581 | 101 | py |
kserve | kserve-master/docs/samples/kafka/image_transformer/image_transformer.py | #
# 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 t... | 2,551 | 37.666667 | 117 | py |
kserve | kserve-master/docs/samples/logger/basic/README.md | # Inference Logger Demo
First let us create a message dumper Knative service which will print out the CloudEvents it receives.
We will use the following resource YAML:
```
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: message-dumper
spec:
template:
spec:
containers:
- image: gcr... | 3,105 | 23.456693 | 173 | md |
kserve | kserve-master/docs/samples/logger/basic/message-dumper.yaml | apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: message-dumper
spec:
template:
spec:
containers:
- image: gcr.io/knative-releases/knative.dev/eventing-contrib/cmd/event_display
| 214 | 20.5 | 85 | yaml |
kserve | kserve-master/docs/samples/logger/basic/sklearn-logging.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: sklearn-iris
spec:
predictor:
logger:
mode: all
url: http://message-dumper.default/
sklearn:
storageUri: gs://kfserving-examples/models/sklearn/1.0/model
| 261 | 20.833333 | 66 | yaml |
kserve | kserve-master/docs/samples/logger/knative-eventing/README.md | # Knative Eventing Inference Logger Demo
This demo assumes you have a cluster running with [Knative Eventing installed](https://knative.dev/docs/eventing/getting-started/),
along with KServe.
Following components of Knative Eventing must be installed:
- Knative Eventing CRDs
- Knative Eventing Core components
- A bro... | 4,952 | 24.015152 | 173 | md |
kserve | kserve-master/docs/samples/logger/knative-eventing/broker.yaml | apiVersion: eventing.knative.dev/v1
kind: broker
metadata:
name: default
| 74 | 14 | 35 | yaml |
kserve | kserve-master/docs/samples/logger/knative-eventing/message-dumper.yaml | apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: message-dumper
spec:
template:
spec:
containers:
- image: gcr.io/knative-releases/knative.dev/eventing-contrib/cmd/event_display
| 214 | 20.5 | 85 | yaml |
kserve | kserve-master/docs/samples/logger/knative-eventing/sklearn-logging.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: sklearn-iris
spec:
predictor:
logger:
mode: all
url: http://broker-ingress.knative-eventing.svc.cluster.local/default/default
sklearn:
storageUri: gs://kfserving-examples/models/sklearn/1.0/model
| 303 | 24.333333 | 83 | yaml |
kserve | kserve-master/docs/samples/logger/knative-eventing/trigger.yaml | apiVersion: eventing.knative.dev/v1
kind: Trigger
metadata:
name: message-dumper-trigger
spec:
broker: default
subscriber:
ref:
apiVersion: serving.knative.dev/v1
kind: Service
name: message-dumper
| 226 | 17.916667 | 40 | yaml |
kserve | kserve-master/docs/samples/logger/local/README.md | # Local Test of Kfserving Logger
In one terminal start an echo http server on port 8000
```
docker run -it -p 8000:80 --rm -t mendhak/http-https-echo
```
Start an SKLearn Iris model on port 8080. You will need to have pip installed the sklearnserver. See `/python/sklearnserver`.
```
gsutil cp -r gs://kfserving-exam... | 3,317 | 28.105263 | 141 | md |
kserve | kserve-master/docs/samples/metrics-and-monitoring/README.md | # Metrics and Monitoring
> Getting started with Prometheus-based monitoring of KFServing models.
# Table of Contents
1. [Install Prometheus](#install-prometheus)
2. [Access Prometheus Metrics](#access-prometheus-metrics)
3. [Metrics-driven experiments and progressive delivery](#metrics-driven-experiments-and-progress... | 7,150 | 49.359155 | 388 | md |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus-operator/kustomization.yaml | namePrefix: kfserving-
namespace: kfserving-monitoring
resources:
- github.com/prometheus-operator/prometheus-operator?ref=v0.44.1
- namespace.yaml | 147 | 28.6 | 64 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus-operator/namespace.yaml | apiVersion: v1
kind: Namespace
metadata:
name: monitoring
| 60 | 11.2 | 18 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus/clusterrole.yaml | apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: prometheus
rules:
- apiGroups: [""]
resources:
- nodes
- nodes/metrics
- services
- endpoints
- pods
verbs: ["get", "list", "watch"]
- apiGroups: [""]
resources:
- configmaps
verbs: ["get"]
- apiGroups:
- networking.k8s.i... | 431 | 17 | 40 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus/clusterrolebinding.yaml | apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: prometheus
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: prometheus
subjects:
- kind: ServiceAccount
name: prometheus
| 235 | 18.666667 | 40 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus/kfserving-service-monitor.yaml | apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: monitoring
spec:
namespaceSelector:
any: true
selector:
matchLabels:
networking.internal.knative.dev/serviceType: Private
endpoints:
- port: http-usermetric
interval: 15s | 272 | 20 | 58 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus/kustomization.yaml | namePrefix: kfserving-
namespace: kfserving-monitoring
resources:
- clusterrole.yaml
- clusterrolebinding.yaml
- prometheus.yaml
- serviceaccount.yaml
- kfserving-service-monitor.yaml | 183 | 22 | 32 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus/prometheus.yaml | apiVersion: monitoring.coreos.com/v1
kind: Prometheus
metadata:
name: prometheus
spec:
serviceAccountName: kfserving-prometheus
serviceMonitorNamespaceSelector: {}
serviceMonitorSelector: {}
resources:
requests:
memory: 400Mi
enableAdminAPI: false | 269 | 21.5 | 42 | yaml |
kserve | kserve-master/docs/samples/metrics-and-monitoring/prometheus/serviceaccount.yaml | apiVersion: v1
kind: ServiceAccount
metadata:
name: prometheus | 64 | 15.25 | 20 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/benchmark/BENCHMARK.md | # Benchmark of Multi-Model Serving
The following stress and performance tests have been executed to support that Multi-Model Serving is a solution to the model deployment scalability problem as discussed in the [MMS Guide](../../../MULTIMODELSERVING_GUIDE.md).
## Stress Test
To determine the maximum amount of models ... | 8,334 | 53.477124 | 242 | md |
kserve | kserve-master/docs/samples/multimodelserving/sklearn/README.md | # Multi-Model Serving with Sklearn
## Overview
The general overview of multi-model serving:
1. Deploy InferenceService with the framework specified
2. Deploy TrainedModel(s) with the storageUri, framework, and memory
3. A config map will be created and will contain details about each trained model
4. Model Agent load... | 7,974 | 48.534161 | 315 | md |
kserve | kserve-master/docs/samples/multimodelserving/sklearn/inferenceservice.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris-example"
spec:
predictor:
minReplicas: 1
sklearn:
protocolVersion: v1
name: "sklearn-iris-predictor"
resources:
limits:
cpu: 100m
memory: 512Mi
requests:
... | 359 | 19 | 39 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/sklearn/trainedmodels.yaml | apiVersion: "serving.kserve.io/v1alpha1"
kind: "TrainedModel"
metadata:
name: "model1-sklearn"
spec:
inferenceService: "sklearn-iris-example"
model:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
framework: "sklearn"
memory: "256Mi"
---
apiVersion: "serving.kserve.io/v1alpha1"
kind: "T... | 538 | 23.5 | 66 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/triton/README.md | # Multi Model Serving with Triton(Alpha)
There are growing use cases of developing per-user or per-category ML models instead of cohort model. For example a news classification service trains custom model based on each news category,
a recommendation model trains on each user's usage history to personalize the recommen... | 11,823 | 49.101695 | 311 | md |
kserve | kserve-master/docs/samples/multimodelserving/triton/minio.yaml | ---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
labels:
app: minio
name: minio
spec:
progressDeadlineSeconds: 600
replicas: 1
revisionHistoryLimit: 10
selector:
matchLabels:
app: minio
strategy:
type: Recreate
template:
metadata:
labels:
app: minio
s... | 1,188 | 18.816667 | 55 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/triton/multi_model_triton.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "triton-mms"
spec:
predictor:
triton:
args:
- --log-verbose=1
env:
- name: OMP_NUM_THREADS
value: "1"
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
... | 358 | 16.95 | 39 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/triton/perf.yaml | apiVersion: batch/v1
kind: Job
metadata:
generateName: torchscript-load-test
spec:
backoffLimit: 6
parallelism: 1
template:
metadata:
annotations:
sidecar.istio.io/inject: "false"
spec:
restartPolicy: OnFailure
containers:
- args:
- vegeta -cpus=1 attack -duration... | 125,066 | 36.625451 | 81 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/triton/s3_secret.yaml | apiVersion: v1
kind: Secret
metadata:
name: mysecret
annotations:
serving.kserve.io/s3-region: us-west-2
serving.kserve.io/s3-usevirtualbucket: "false"
serving.kserve.io/s3-endpoint: minio-service:9000 # replace with your s3 endpoint
serving.kserve.io/s3-usehttps: "0" # by default 1, for testing... | 529 | 24.238095 | 99 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/triton/trained_model.yaml | apiVersion: "serving.kserve.io/v1alpha1"
kind: "TrainedModel"
metadata:
name: "cifar10"
spec:
inferenceService: triton-mms
model:
framework: pytorch
storageUri: gs://kfserving-examples/models/torchscript/cifar10
memory: "1Gi"
| 246 | 21.454545 | 66 | yaml |
kserve | kserve-master/docs/samples/multimodelserving/triton/trained_model2.yaml | apiVersion: "serving.kserve.io/v1alpha1"
kind: "TrainedModel"
metadata:
name: "simple-string"
spec:
inferenceService: triton-mms
model:
framework: tensorflow
storageUri: gs://kfserving-examples/models/triton/simple_string
memory: "1Gi"
| 255 | 22.272727 | 67 | yaml |
kserve | kserve-master/docs/samples/outlier-detection/alibi-detect/cifar10/README.md | # CIFAR10 Outlier Detector
A [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) Outlier Detector. Run the [notebook demo](cifar10_outlier.ipynb) to test.
The notebook requires KNative Eventing >= 0.18.
The architecture used is shown below and links the payload logging available within KFServing with asynchronous... | 418 | 33.916667 | 171 | md |
kserve | kserve-master/docs/samples/outlier-detection/alibi-detect/cifar10/cifar10.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "tfserving-cifar10"
namespace: cifar10
spec:
predictor:
tensorflow:
storageUri: "gs://seldon-models/tfserving/cifar10/resnet32"
logger:
mode: all
url: http://broker-ingress.knative-eventing.svc.c... | 349 | 25.923077 | 85 | yaml |
kserve | kserve-master/docs/samples/outlier-detection/alibi-detect/cifar10/cifar10od.yaml | apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: vae-outlier
namespace: cifar10
spec:
template:
metadata:
annotations:
autoscaling.knative.dev/minScale: "1"
spec:
containers:
- image: seldonio/alibi-detect-server:1.12.0
imagePullPolicy: IfNotPresent
... | 770 | 23.870968 | 63 | yaml |
kserve | kserve-master/docs/samples/outlier-detection/alibi-detect/cifar10/trigger.yaml | apiVersion: eventing.knative.dev/v1
kind: Trigger
metadata:
name: vaeoutlier-trigger
namespace: cifar10
spec:
broker: default
filter:
attributes:
type: org.kubeflow.serving.inference.request
subscriber:
ref:
apiVersion: serving.knative.dev/v1
kind: Service
name: vae-outlier
... | 342 | 19.176471 | 50 | yaml |
kserve | kserve-master/docs/samples/performance-testing/README.md | # HTTP Performance test
This tutorial demonstrates performance testing for KServe inference services using HTTP. We will use [Iter8](https://iter8.tools) for generating load and validing service-level objectives (SLOs) for the inference service. [Iter8](https://iter8.tools) is an open-source Kubernetes release optimiz... | 5,292 | 50.892157 | 560 | md |
kserve | kserve-master/docs/samples/performance-testing/grpc.md | # gRPC Performance test
This tutorial demonstrates performance testing for KServe inference services using gRPC. We will use [Iter8](https://iter8.tools) for generating load and validing service-level objectives (SLOs) for the inference service. [Iter8](https://iter8.tools) is an open-source Kubernetes release optimiz... | 5,511 | 50.037037 | 550 | md |
kserve | kserve-master/docs/samples/pipelines/README.md | # Deploy to KServe from [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/introduction/)
## Kubeflow Pipelines KServe component
The following examples illustrate how to use the Kubeflow Pipelines component for KServe using the v1beta1 API.
These assume your cluster has a KServe version >= v0.7.0... | 2,290 | 94.458333 | 805 | md |
kserve | kserve-master/docs/samples/pipelines/sample-custom-model.py | #
# 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 t... | 1,613 | 37.428571 | 117 | py |
kserve | kserve-master/docs/samples/pipelines/sample-tf-pipeline.py | #
# 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 t... | 1,612 | 36.511628 | 107 | py |
kserve | kserve-master/docs/samples/storage/azure/README.md |
# Predict on a InferenceService with saved model on Azure
## Using Public Azure Blobs
By default, KServe uses anonymous client to download artifacts. To point to an Azure Blob, specify StorageUri to point to an Azure Blob Storage with the format:
```https://{$STORAGE_ACCOUNT_NAME}.blob.core.windows.net/{$CONTAINER}/{$... | 3,002 | 35.621951 | 234 | md |
kserve | kserve-master/docs/samples/storage/hdfs/README.md |
# HDFS Storage
KServe HDFS support requires a HDFS cluster that supports [WebHDFS](https://hadoop.apache.org/docs/r1.0.4/webhdfs.html).
Kerberos authentication is also supported.
HDFS support uses the [hdfscli](https://hdfscli.readthedocs.io/en/latest/) python bindings.
### Create a Kubernetes Secret
You need to c... | 4,983 | 32.449664 | 259 | md |
kserve | kserve-master/docs/samples/storage/pvc/README.md |
# Create a InferenceService for on-prem cluster
The guide shows how to train model and create InferenceService for the trained model for on-prem cluster.
## Prerequisites
Refer to the [document](https://kubernetes.io/docs/concepts/storage/persistent-volumes/) to create Persistent Volume (PV) and Persistent Volume Cl... | 1,458 | 44.59375 | 487 | md |
kserve | kserve-master/docs/samples/storage/pvc/mnist-pvc.yaml | apiVersion: "serving.kserve.io/v1alpha2"
kind: "InferenceService"
metadata:
name: "mnist-sample"
spec:
default:
predictor:
tensorflow:
storageUri: "pvc://${PVC_NAME}/export"
| 196 | 18.7 | 46 | yaml |
kserve | kserve-master/docs/samples/storage/s3/README.md | Please refer to the [S3 example](https://github.com/kserve/website/blob/main/docs/modelserving/storage/s3/s3.md) on KServe website repository for deploying InferenceService with S3 storage.
| 190 | 94.5 | 189 | md |
kserve | kserve-master/docs/samples/storage/s3/s3_secret.yaml | apiVersion: v1
kind: Secret
metadata:
name: mysecret
annotations:
serving.kserve.io/s3-endpoint: s3.amazonaws.com # replace with your s3 endpoint
serving.kserve.io/s3-usehttps: "1" # by default 1, for testing with minio you need to set to 0
serving.kserve.io/s3-region: "us-east-2" # replace with the ... | 787 | 36.52381 | 160 | yaml |
kserve | kserve-master/docs/samples/storage/s3/tensorflow_s3.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "mnist-s3"
spec:
predictor:
serviceAccountName: sa
tensorflow:
storageUri: "s3://kfserving-examples/mnist"
| 206 | 19.7 | 49 | yaml |
kserve | kserve-master/docs/samples/storage/storageSpec/README.md | # Predict on a InferenceService with StorageSpec
## Setup
1. Your `~/.kube/config` should point to a cluster with [KServe installed](https://github.com/kserve/kserve#installation).
2. Your cluster's Istio Ingress gateway must be [network accessible](https://istio.io/latest/docs/tasks/traffic-management/ingress/ingress-... | 3,997 | 38.584158 | 239 | md |
kserve | kserve-master/docs/samples/storage/storageSpec/common_secret.yaml | apiVersion: v1
stringData:
localMinIO: |
{
"type": "s3",
"access_key_id": "minio",
"secret_access_key": "minio123",
"endpoint_url": "http://minio-service.kubeflow:9000",
"bucket": "mlpipeline",
"region": "us-south"
}
kind: Secret
metadata:
name: storage-config
type: Opaqu... | 322 | 19.1875 | 59 | yaml |
kserve | kserve-master/docs/samples/storage/storageSpec/sklearn_storagespec.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: example-sklearn-isvc
spec:
predictor:
sklearn:
storage:
key: localMinIO
path: sklearn
# schemaPath: null
parameters:
bucket: example-models
| 273 | 18.571429 | 37 | yaml |
kserve | kserve-master/docs/samples/storage/uri/README.md | # Predict on a `InferenceService` with a saved model from a URI
This allows you to specify a model object via the URI (Uniform Resource Identifier) of the model object exposed via an `http` or `https` endpoint.
This `storageUri` option supports single file models, like `sklearn` which is specified by a [joblib](https... | 8,917 | 32.152416 | 335 | md |
kserve | kserve-master/docs/samples/storage/uri/sklearn.yaml | apiVersion: serving.kserve.io/v1alpha2
kind: InferenceService
metadata:
name: sklearn-from-uri
spec:
default:
predictor:
sklearn:
storageUri: https://github.com/tduffy000/kfserving-uri-examples/blob/master/sklearn/frozen/model.joblib?raw=true
| 265 | 25.6 | 120 | yaml |
kserve | kserve-master/docs/samples/storage/uri/tensorflow.yaml | apiVersion: serving.kserve.io/v1alpha2
kind: InferenceService
metadata:
name: tensorflow-from-uri
spec:
default:
predictor:
tensorflow:
storageUri: https://raw.githubusercontent.com/tduffy000/kfserving-uri-examples/master/tensorflow/frozen/model_artifacts.tar.gz
| 285 | 27.6 | 134 | yaml |
kserve | kserve-master/docs/samples/v1beta1/advanced/cluster_local.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris-local"
labels:
networking.knative.dev/visibility: cluster-local
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
| 268 | 23.454545 | 68 | yaml |
kserve | kserve-master/docs/samples/v1beta1/advanced/pod-overrides.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "tensorflow-pod-overrides"
spec:
predictor:
serviceAccount: "serving-admin"
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
| 251 | 24.2 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/advanced/probes.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "flowers"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
readinessProbe:
httpGet:
path: /v1/models/flowers
livenessProbe:
httpGet:
... | 345 | 22.066667 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/advanced/timeout.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "flowers"
spec:
predictor:
timeout: 600
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
livenessProbe:
httpGet:
path: /v1/models/flowers
| 288 | 21.230769 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/simple.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "custom-simple"
spec:
predictor:
minReplicas: 1
containers:
- image: codait/max-object-detector
name: kserve-container
ports:
- containerPort: 5000
protocol: TCP
| 289 | 19.714286 | 39 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/custom_model/README.md | # Deploy Custom Python Model Server with InferenceService
Please refer to [custom python model example](https://github.com/kserve/website/tree/main/docs/modelserving/v1beta1/custom/custom_model) on KServe website repository for deploying custom python model server with REST/gRPC inference protocol.
| 301 | 74.5 | 241 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/custom_model/custom.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: custom-model
spec:
predictor:
containers:
- name: kserve-container
image: kserve/custom-model:latest
---
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: custom-model-grpc
spec:
predictor:
... | 503 | 20 | 46 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/paddleserving/paddleserving-custom.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: paddleserving-custom
spec:
predictor:
containers:
- image: ruminateer/paddleserving:lac-http
name: paddleserving-container
| 222 | 21.3 | 46 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/prebuilt-image/README.md | # Predict on a InferenceService using a prebuilt image
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https://github.com/kserve/kserve/#installation).
2. Your cluster's Istio Ingress gateway must be [network accessible](https://istio.io/latest/docs/tasks/traffic-management/ingress/i... | 2,374 | 38.583333 | 376 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/prebuilt-image/custom.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: InferenceService
metadata:
labels:
controller-tools.k8s.io: "1.0"
name: custom-prebuilt-image
spec:
predictor:
containers:
- name: custom
image: codait/max-object-detector
ports:
- containerPort: 5000
protocol: TCP
| 319 | 20.333333 | 41 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/README.md | # Predict on a InferenceService using a Custom Torchserve Image
In this example we use torchserve as custom server to serve an mnist model. The idea of using torchserve as custom server is to make the transition for new users from torchserve to kfserving easier.
## Setup
1. Your ~/.kube/config should point to a clus... | 3,214 | 34.32967 | 202 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/autoscale.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: torchserve-custom
annotations:
autoscaling.knative.dev/target: "5"
spec:
predictor:
containers:
- image: {username}/torchserve:latest
name: torchserve-container
| 266 | 21.25 | 41 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/canary.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: torchserve-custom
spec:
predictor:
canaryTrafficPercent: 10
containers:
- image: {username}/torchserve:latest
name: torchserve-container
| 240 | 20.909091 | 41 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/custom-server-with-external-storage.md | # Torchserve with external storage
For running torchserve with external storage, the model archive files and config.properties should be copied to the storage.
The storage mount to ```/mnt/models``` directory.
## Folder structure
```json
├──config.properties
├── model-store
│ ├── mnist.mar
| ├── densenet161.mar... | 1,760 | 17.154639 | 124 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/gpu.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: torchserve-custom-gpu
spec:
predictor:
containers:
- image: {username}/torchserve-gpu:latest
name: torchserve-container
resources:
limits:
nvidia.com/gpu: 1
| 280 | 20.615385 | 45 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/metrics.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: torchserve-custom
annotations:
prometheus.io/scrape: 'true'
prometheus.io/port: '8082'
spec:
predictor:
containers:
- image: {username}/torchserve:latest
name: torchserve-container
| 290 | 21.384615 | 41 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/pv.yaml | apiVersion: v1
kind: PersistentVolume
metadata:
name: model-pv-volume
labels:
type: "amazonEBS"
spec:
capacity:
storage: 5Gi
accessModes:
- ReadWriteOnce
awsElasticBlockStore:
volumeID: {volume-id} #vol-074ea8934f7b80df5
fsType: ext4
| 266 | 16.8 | 48 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/pvc.yaml | apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: model-pv-claim
labels:
type: "amazonEBS"
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
| 195 | 14.076923 | 27 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/pvpod.yaml | apiVersion: v1
kind: Pod
metadata:
name: model-store-pod
spec:
volumes:
- name: model-store
persistentVolumeClaim:
claimName: model-pv-claim
containers:
- name: model-store
image: ubuntu
command: [ "sleep" ]
args: [ "infinity" ]
volumeMounts:
- mountPath: "/pv... | 426 | 18.409091 | 33 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/torchserve-custom-pv.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve-custom-pv"
spec:
predictor:
containers:
- image: {username}/torchserve:latest
name: kserve-container
env:
- name: STORAGE_URI
value: "pvc://model-pv-claim" # The storage mount... | 337 | 25 | 77 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/torchserve-custom.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: torchserve-custom
spec:
predictor:
containers:
- image: {username}/torchserve:latest
name: torchserve-container
| 211 | 20.2 | 41 | yaml |