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
values |
|---|---|---|---|---|---|---|
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/bert-sample/bert.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: torchserve-bert
spec:
predictor:
containers:
- image: {username}/torchserve-bert:latest
name: kserve-container
env:
- name: STORAGE_URI
value: "gs://kfserving-examples/models/torchserve/bert/" # Th... | 347 | 25.769231 | 97 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/bert-sample/hugging-face-bert-sample.md | # Torchserve custom server with Hugging Face bert sample
## Model archive file creation
Clone [pytorch/serve](https://github.com/pytorch/serve) repository. Install torchserve and torch model archiver with the steps provided. Navigate to examples/Huggingface_Transformers and follow the steps for creating mar file.
[T... | 4,370 | 31.619403 | 306 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/docs/autoscaling.md | # Autoscaling
## Deployment yaml
For example, specify a “concurrency target” of “5”, the autoscaler will try to make sure that every replica receives on average 5 requests at a time.
By default the pod scale with concurrency metrics
```yaml
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
... | 2,284 | 32.115942 | 231 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/docs/canary.md | # Canary Rollouts
## Deployment yaml
```yaml
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve-custom"
spec:
predictor:
containers:
- image: {username}/torchserve:latest
name: torchserve-container
```
### Canary model
Change the path and deploy
```yaml
a... | 3,083 | 26.292035 | 201 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/docs/metrics.md | # Metrics
This adds prometheus and grafana to the cluster with some default metrics.
## 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/task... | 4,328 | 25.888199 | 201 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/logger/README.md | # Inference Logger Demo
First let us create a message dumper KNative service which will print out the Cloud Events it receives.
We will use the following resource yaml:
```yaml
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: message-dumper
spec:
template:
spec:
containers:
- image... | 3,704 | 27.5 | 170 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/logger/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/v1beta1/custom/torchserve/logger/torchserve-logger.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve-custom"
spec:
predictor:
minReplicas: 1
logger:
url: http://message-dumper.default.svc.cluster.local
mode: all
containers:
- image: {username}/torchserve:latest
name: torchserve-c... | 329 | 22.571429 | 58 | yaml |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/torchserve-image/README.md |
# Docker image building
**Steps:**
1. Download model archive file from the [model-zoo](https://github.com/pytorch/serve/blob/master/docs/model_zoo.md) or create you own using the step provided [here](https://github.com/pytorch/serve/blob/master/model-archiver/README.md)
2. Copy model archive files to model-store f... | 723 | 33.47619 | 236 | md |
kserve | kserve-master/docs/samples/v1beta1/custom/torchserve/torchserve-image/dockerd-entrypoint.sh | #!/bin/bash
set -e
if [[ "$1" = "serve" ]]; then
shift 1
torchserve --start --ts-config /home/model-server/config.properties
else
eval "$@"
fi
# prevent docker exit
tail -f /dev/null
| 197 | 14.230769 | 71 | sh |
kserve | kserve-master/docs/samples/v1beta1/lightgbm/README.md | ## Creating your own model and testing the LightGBM server.
To test the LightGBM Server, first we need to generate a simple LightGBM model using Python.
```python
import lightgbm as lgb
from sklearn.datasets import load_iris
import os
model_dir = "."
BST_FILE = "model.bst"
iris = load_iris()
y = iris['target']
X =... | 3,844 | 30.008065 | 241 | md |
kserve | kserve-master/docs/samples/v1beta1/lightgbm/lightgbm.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "lightgbm-iris"
spec:
predictor:
lightgbm:
storageUri: "gs://kfserving-examples/models/lightgbm/iris"
| 197 | 21 | 64 | yaml |
kserve | kserve-master/docs/samples/v1beta1/onnx/README.md |
# Deploy InferenceService with ONNX model
## 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-control/... | 1,479 | 33.418605 | 254 | md |
kserve | kserve-master/docs/samples/v1beta1/onnx/onnx.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "style-sample"
spec:
predictor:
onnx:
storageUri: "gs://kfserving-examples/models/onnx"
| 183 | 19.444444 | 55 | yaml |
kserve | kserve-master/docs/samples/v1beta1/onnx/assets/onnx_ml_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: onnx-ml.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _m... | 66,322 | 40.923515 | 6,346 | py |
kserve | kserve-master/docs/samples/v1beta1/onnx/assets/predict_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: predict.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _refle... | 7,875 | 35.462963 | 660 | py |
kserve | kserve-master/docs/samples/v1beta1/paddle/README.md | # Predict on an InferenceService using PaddleServer
In this example, we use a trained paddle resnet50 model to classify images by running an inference service with Paddle predictor.
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https://github.com/kserve/kserve#installation).
2. You... | 25,255 | 427.067797 | 22,573 | md |
kserve | kserve-master/docs/samples/v1beta1/paddle/img2json.py | #!/usr/bin/python3
import os
import argparse
import json
import cv2
from img_preprocess import preprocess
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="converts image to json request",
type=str)
args = parser.parse_args()
input_file = args.filename
img = preprocess(cv2.... | 500 | 21.772727 | 70 | py |
kserve | kserve-master/docs/samples/v1beta1/paddle/img_preprocess.py | import cv2
import numpy as np
def resize_short(img, target_size):
""" resize_short """
percent = float(target_size) / min(img.shape[0], img.shape[1])
resized_width = int(round(img.shape[1] * percent))
resized_height = int(round(img.shape[0] * percent))
resized = cv2.resize(img, (resized_width, res... | 1,250 | 28.785714 | 70 | py |
kserve | kserve-master/docs/samples/v1beta1/paddle/paddle.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "paddle-resnet50"
spec:
predictor:
paddle:
storageUri: "gs://kfserving-examples/models/paddle/resnet"
| 197 | 21 | 64 | yaml |
kserve | kserve-master/docs/samples/v1beta1/pmml/README.md | ## Creating your own model and testing the PMML Server locally.
To test the [PMMLServer](http://dmg.org/pmml/pmml_examples/#Iris) server, first we need to download model from [single_iris_dectree](http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml)
# Predict on a InferenceService using ... | 2,334 | 36.063492 | 215 | md |
kserve | kserve-master/docs/samples/v1beta1/pmml/pmml.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "pmml-demo"
spec:
predictor:
pmml:
storageUri: gs://kfserving-examples/models/pmml
| 178 | 18.888889 | 53 | yaml |
kserve | kserve-master/docs/samples/v1beta1/rollout/README.md | # Rollout InferenceService with Canary Strategy
## 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-co... | 9,155 | 37.309623 | 203 | md |
kserve | kserve-master/docs/samples/v1beta1/rollout/canary.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "my-model"
spec:
predictor:
# 10% of traffic is sent to this model
canaryTrafficPercent: 10
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers-2"
| 273 | 23.909091 | 71 | yaml |
kserve | kserve-master/docs/samples/v1beta1/rollout/default.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "my-model"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
| 199 | 21.222222 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/rollout/pinned.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "my-model"
spec:
predictor:
# 0% of traffic is sent to this model and all traffic goes back to previous good model
canaryTrafficPercent: 0
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers-2"
| 320 | 28.181818 | 90 | yaml |
kserve | kserve-master/docs/samples/v1beta1/rollout/promotion.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "my-model"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers-2"
| 201 | 21.444444 | 71 | yaml |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v1/README.md | ## Creating your own model and testing the SKLearn Server locally.
To test the [Scikit-Learn](https://scikit-learn.org/stable/) server, first we need to generate a simple scikit-learn model using Python.
```python
from sklearn import svm
from sklearn import datasets
from joblib import dump
clf = svm.SVC(gamma='scale... | 3,865 | 32.912281 | 233 | md |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v1/perf.yaml | apiVersion: batch/v1
kind: Job
metadata:
generateName: load-test
spec:
backoffLimit: 6
parallelism: 1
template:
metadata:
annotations:
sidecar.istio.io/inject: "false"
spec:
restartPolicy: OnFailure
containers:
- args:
- vegeta -cpus=5 attack -duration=1m -rate=50... | 1,047 | 20.387755 | 89 | yaml |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v1/sklearn.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
| 199 | 21.222222 | 68 | yaml |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v1/sklearn-mixedtype-model/__init__.py | 0 | 0 | 0 | py | |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v1/sklearn-mixedtype-model/custom_transformer.py | import pandas as pd
from sklearn.base import TransformerMixin
class DictToDFTransformer(TransformerMixin):
def transform(self, X, y=None):
return pd.DataFrame(X)
def fit(self, X, y=None):
return self
| 228 | 18.083333 | 44 | py |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v1/sklearn-mixedtype-model/sklearn-mixedtype.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-mixedtype"
spec:
predictor:
sklearn:
storageUri: "gs://kfserving-examples/models/sklearn/1.0/mixedtype"
| 208 | 22.222222 | 72 | yaml |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v2/README.md | # Deploying SKLearn models
This example walks you through how to deploy a `scikit-learn` model leveraging
the `v1beta1` version of the `InferenceService` CRD.
Note that, by default the `v1beta1` version will expose your model through an
API compatible with the existing V1 Dataplane.
However, this example will show you... | 4,950 | 26.971751 | 110 | md |
kserve | kserve-master/docs/samples/v1beta1/sklearn/v2/sklearn.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-irisv2"
spec:
predictor:
sklearn:
protocolVersion: v2
storageUri: "gs://seldon-models/sklearn/iris"
| 210 | 20.1 | 51 | yaml |
kserve | kserve-master/docs/samples/v1beta1/spark/README.md | # Predict on a Spark MLlib model PMML InferenceService
spark.mllib supports model export to Predictive Model Markup Language [PMML](https://en.wikipedia.org/wiki/Predictive_Model_Markup_Language).
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https://github.com/kserve/kserve#install... | 3,564 | 32.009259 | 157 | md |
kserve | kserve-master/docs/samples/v1beta1/spark/deployment.yaml | apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: spark-pmml-iris
component: predictor
serving.kubeflow.org/inferenceservice: spark-pmml-iris
name: spark-pmml-iris
spec:
replicas: 1
selector:
matchLabels:
app: spark-pmml-iris
strategy:
rollingUpdate:
maxSurge: 25%
... | 2,315 | 24.450549 | 62 | yaml |
kserve | kserve-master/docs/samples/v1beta1/spark/spark_pmml.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "spark-pmml"
spec:
predictor:
pmml:
storageUri: gs://kfserving-examples/models/sparkpmml
| 184 | 19.555556 | 58 | yaml |
kserve | kserve-master/docs/samples/v1beta1/spark/sparkml.py | from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import RFormula
from pyspark2pmml import PMMLBuilder
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
df = spark.read.csv("Iris.csv", header... | 610 | 32.944444 | 73 | py |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/README.md | # Predict on an InferenceService with Tensorflow model
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#deploy-kfserving).
2. Your cluster's Istio Ingress gateway must be network accessible.
## Create the Infere... | 5,958 | 27.649038 | 191 | md |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/canary.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "flower-sample"
spec:
predictor:
canaryTrafficPercent: 20
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers-2"
| 235 | 22.6 | 71 | yaml |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/gpu.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "tensorflow-gpu"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
resources:
limits:
nvidia.com/gpu: 1
| 266 | 21.25 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/grpc.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "flower-grpc"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
ports:
- containerPort: 9000
name: h2c
protocol: TCP
| 289 | 21.307692 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/grpc_client.py | import argparse
import json
import base64
import grpc
from tensorflow.contrib.util import make_tensor_proto
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
def predict(host, port, hostname, model, signature_name, input_path):
# If hostname not set, ... | 2,068 | 41.22449 | 109 | py |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/tensorflow.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "flower-sample"
annotations:
serving.kserve.io/enable-metric-aggregation: "true"
serving.kserve.io/enable-prometheus-scraping: "true"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorf... | 333 | 26.833333 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/tensorflow/versioning.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "tensorflow-versioning"
spec:
predictor:
tensorflow:
storageUri: "gs://kfserving-examples/models/tensorflow/flowers"
image: "tensorflow/serving:v1.14"
| 252 | 24.3 | 69 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/README.md | # Predict on a InferenceService using TorchServe
In this example, we use a trained PyTorch MNIST model to predict handwritten digits by running an inference service with [TorchServe](https://github.com/pytorch/serve) predictor.
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https:/... | 4,427 | 56.506494 | 374 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/autoscaling/README.md | # Autoscaling
KServe supports the implementation of Knative Pod Autoscaler (KPA) and Kubernetes’ Horizontal Pod Autoscaler (HPA).
The features and limitations of each of these Autoscalers are listed below.
IMPORTANT: If you want to use Kubernetes Horizontal Pod Autoscaler (HPA), you must install [HPA extension](https:... | 3,783 | 37.612245 | 224 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/autoscaling/autoscaling.yaml | # For example, specify a “concurrency target” of “10”, the autoscaler will try to make sure that every replica receives on average 10 requests at a time.
# A target is always evaluated against a specified metric.
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve"
annotatio... | 477 | 35.769231 | 153 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/canary/README.md | # Canary Rollout
## Create InferenceService with default model
```yaml
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve"
spec:
predictor:
pytorch:
storageUri: "gs://kfserving-examples/models/torchserve/image_classifier/v1"
```
Apply the InferenceService
```b... | 3,807 | 28.984252 | 201 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/canary/canary.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve"
spec:
predictor:
canaryTrafficPercent: 20
pytorch:
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v2
| 237 | 22.8 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/metrics/README.md | # Metrics
This adds prometheus and grafana to the cluster with some default metrics.
## 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/task... | 4,228 | 23.587209 | 201 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/metrics/metrics.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torch-metrics"
annotations:
prometheus.io/scrape: 'true'
prometheus.io/port: '8082'
spec:
predictor:
pytorch:
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v1
| 286 | 22.916667 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/README.md | # Generate model archiver files for torchserve
## 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-co... | 3,969 | 24.126582 | 240 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/model-archiver.yaml | apiVersion: v1
kind: Pod
metadata:
name: margen-pod
spec:
volumes:
- name: model-pv-claim
persistentVolumeClaim:
claimName: model-pv-claim
initContainers:
- name: data-permission-fix
image: busybox
command: ["/bin/chmod","-R","777", "/data"]
volumeMounts:
- name: mode... | 614 | 22.653846 | 49 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/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/torchserve/model-archiver/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/torchserve/model-archiver/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/torchserve/model-archiver/model-archiver-image/README.md | # Model archiver for torchserve
**Steps:**
1. Modify config in entrypoint for default config (optional)
2. Build docker image
3. Push docker image to repo
```bash
docker build --file Dockerfile -t margen:latest .
docker tag margen:latest {username}/margen:latest
docker push {username}/margen:latest
```
| 312 | 18.5625 | 61 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/model-archiver-image/dockerd-entrypoint.sh | #!/bin/bash
########)#############################################################
#### Script for model archiving and config.properties generation #####
######################################################################
set -e
BASE_PATH='/home/model-server'
MODEL_STORE=$BASE_PATH/model-store
CONFIG_PATH=$BASE_PAT... | 4,636 | 56.9625 | 718 | sh |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/model-store/README.md | # Model archiver for torchserve
## Place all the file required to grenerate marfile in the model folder | 104 | 34 | 71 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/model-store/densenet_161/model.py | from torchvision.models.densenet import DenseNet
class ImageClassifier(DenseNet):
def __init__(self):
super(ImageClassifier, self).__init__(48, (6, 12, 36, 24), 96)
def load_state_dict(self, state_dict, strict=True):
# '.'s are no longer allowed in module names, but previous _DenseLayer
... | 1,126 | 42.346154 | 118 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/model-store/mnist/mnist.py | import torch
from torch import nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0... | 757 | 25.137931 | 44 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/model-archiver/model-store/mnist/mnist_handler.py | from torchvision import transforms
from ts.torch_handler.image_classifier import ImageClassifier
class MNISTDigitClassifier(ImageClassifier):
"""
MNISTDigitClassifier handler class. This handler extends class ImageClassifier from image_classifier.py, a
default handler. This handler takes an image and ret... | 660 | 30.47619 | 110 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/README.md | # Predict on a InferenceService using TorchServe
In this example, we use a trained pytorch mnist model to predict handwritten digits by running an inference service with [TorchServe](https://github.com/pytorch/serve) predictor.
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https:/... | 21,935 | 136.962264 | 16,490 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/gpu.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "torchserve-gpu"
spec:
predictor:
pytorch:
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v1
resources:
limits:
memory: 4Gi
nvidia.com/gpu: "1"
| 299 | 20.428571 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/grpc.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-grpc"
spec:
predictor:
pytorch:
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v1
ports:
- containerPort: 7070
name: h2c
protocol: TCP
| 293 | 21.615385 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/torchserve.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve"
spec:
predictor:
pytorch:
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v1
# storageUri: pvc://model-pv-claim
| 245 | 23.6 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/torchserve_grpc_client.py | import grpc
import inference_pb2
import inference_pb2_grpc
import management_pb2
import management_pb2_grpc
import argparse
def get_inference_stub(host, port, hostname):
channel = grpc.insecure_channel(
host + ":" + str(port),
options=(
(
"grpc.ssl_target_name_override"... | 3,999 | 21.47191 | 80 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/bert/README.md | # TorchServe example with Huggingface bert model
In this example we will show how to serve [Huggingface Transformers with TorchServe](https://github.com/pytorch/serve/tree/master/examples/Huggingface_Transformers)
on KServe.
## Model archive file creation
Clone [pytorch/serve](https://github.com/pytorch/serve) reposi... | 3,831 | 35.150943 | 173 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/bert/bert.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-bert"
spec:
predictor:
pytorch:
storageUri: gs://kfserving-examples/models/torchserve/huggingface
# storageUri: pvc://model-pv-claim
| 242 | 23.3 | 71 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/bloom/README.md | # TorchServe example with Huggingface BLOOM model
In this example we will show how to serve [Large Huggingface models with TorchServe](https://github.com/pytorch/serve/tree/master/examples/Huggingface_Largemodels)
on KServe.
## Model archive file creation
Clone [pytorch/serve](https://github.com/pytorch/serve) reposi... | 5,584 | 28.86631 | 211 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/bloom/bloom-560m.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-bloom-560m"
spec:
predictor:
pytorch:
storageUri: pvc://model-cache
resources:
limits:
cpu: 4
memory: 16Gi
nvidia.com/gpu: 1
nodeName: ip-xxx-xxx-xxx-xxx.us-west-2... | 338 | 21.6 | 57 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/bloom/pvc-pod.yml |
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
name: model-local-claim
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 700Gi
storageClassName: fast-disks
---
apiVersion: v1
kind: Pod
metadata:
name: pv-pod
spec:
volumes:
- name: pv-storage
persistentVolumeClaim... | 492 | 16 | 36 | yml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/bloom/storageclass.yaml | kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
name: fast-disks
provisioner: kubernetes.io/no-provisioner
volumeBindingMode: WaitForFirstConsumer
| 162 | 22.285714 | 41 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/imgconv/README.md | # Convert image to byteArray
The python script converts image to bytesarray
**Steps:**
1. Check python packages are installed
2. Run below command
3. This will write a file `input.json`
```bash
python img2bytearray.py 0.png
```
| 235 | 15.857143 | 46 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v1/imgconv/img2bytearray.py | import base64
import json
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="converts image to bytes array",
type=str)
args = parser.parse_args()
image = open(args.filename, 'rb') # open binary file in read mode
image_read = image.read()
image_64_encode = bas... | 565 | 22.583333 | 69 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/README.md | # Predict on a InferenceService using TorchServe
In this example, we use a trained pytorch mnist model to predict handwritten digits by running an inference service with [TorchServe](https://github.com/pytorch/serve) predictor.
## Setup
1. Your ~/.kube/config should point to a cluster with [KServe installed](https:/... | 10,453 | 111.408602 | 7,455 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/mnist.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-mnist-v2"
spec:
predictor:
pytorch:
protocolVersion: v2
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v2
# storageUri: pvc://model-pv-claim
| 280 | 24.545455 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/mnist_gpu.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-mnist-v2"
spec:
predictor:
pytorch:
protocolVersion: v2
storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v2
resources:
limits:
memory: 4Gi
nvidia.com/gpu: ... | 324 | 22.214286 | 79 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/README.md | # TorchServe example with Huggingface bert model
In this example we will show how to serve [Huggingface Transformers with TorchServe](https://github.com/pytorch/serve/tree/master/examples/Huggingface_Transformers)
on KServe.
## Model archive file creation
Clone [pytorch/serve](https://github.com/pytorch/serve) repos... | 3,533 | 40.093023 | 666 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/bert.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-bert-v2"
spec:
predictor:
pytorch:
protocolVersion: v2
storageUri: gs://kfserving-examples/models/torchserve/bert/v2
# storageUri: pvc://model-pv-claim
| 267 | 23.363636 | 67 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/bert_gpu.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: "torchserve-bert-v2"
spec:
predictor:
pytorch:
protocolVersion: v2
storageUri: gs://kfserving-examples/models/torchserve/bert/v2
resources:
limits:
memory: 4Gi
nvidia.com/gpu: "1" | 310 | 22.923077 | 67 | yaml |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/sequence_classification/Transformer_kserve_handler.py | import torch
import logging
from Transformer_handler_generalized import (
TransformersSeqClassifierHandler,
captum_sequence_forward,
construct_input_ref,
summarize_attributions,
get_word_token,
)
import json
from captum.attr import LayerIntegratedGradients
logger = logging.getLogger(__name__)
# TOD... | 5,355 | 41.507937 | 100 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/sequence_classification/bytes/README.md | To generate input file
```
python tobytes.py --input_text "this year business is good"
``` | 92 | 17.6 | 59 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/sequence_classification/bytes/tobytes.py | import uuid
import json
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--input_text", help="Input text", type=str, required=True)
parser.add_argument("--result_file", help="Path to result file", default="bert_v2.json", type=str)
args = vars(parser.parse_args())
request = {
"id": ... | 628 | 25.208333 | 98 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/sequence_classification/tensor/README.md | For generating tokenized input run the following command
```
python bert_tokenizer.py --input_text "this year business is good"
``` | 132 | 25.6 | 66 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bert/sequence_classification/tensor/bert_tokenizer.py | import uuid
import json
from argparse import ArgumentParser
from transformers import AutoTokenizer
parser = ArgumentParser()
parser.add_argument("--input_text", help="Input text", type=str, required=True)
parser.add_argument("--model_name", help="bert model name", default="bert-base-uncased", type=str)
parser.add_argu... | 1,581 | 30.64 | 98 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bytes_conv/README.md | # Generate bytes input for KServe v2 protocol
The python script converts input image into bytes
**Steps:**
1. Check python packages are installed
2. Run below command
3. This will write a json file with the name of the image.
```bash
python tobytes.py 0.png
```
`0.json` file be created
In case of bytes input,... | 470 | 26.705882 | 173 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/bytes_conv/tobytes.py | import base64
import json
import argparse
import uuid
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="converts image to bytes array", type=str)
args = parser.parse_args()
image = open(args.filename, "rb") # open binary file in read mode
image_read = image.read()
image_64_encode = base64.b64e... | 691 | 31.952381 | 98 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/tensor_conv/README.md | # Generate tensor input for KServe v2 protocol
The python script converts input file to tensor data
**Steps:**
1. Check python packages are installed
2. Run below command
3. This will write a file `input.json`
```bash
python totensor.py 0.png
```
```bash
python totensor.py sample.txt
```
| 297 | 15.555556 | 52 | md |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/tensor_conv/mnist_handler.py | from torchvision import transforms
from ts.torch_handler.image_classifier import ImageClassifier
class MNISTDigitClassifier(ImageClassifier):
"""
MNISTDigitClassifier handler class. This handler extends class ImageClassifier from image_classifier.py, a
default handler. This handler takes an image and retu... | 1,022 | 36.888889 | 110 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/tensor_conv/totensor.py | #!/usr/bin/python3
"""
The script takes text or image file as input and generates json input with
tensor inputs for KServe v2 protocol.
"""
import json
import uuid
import numpy as np
import argparse
from utils import check_image_with_pil, _to_datatype
from PIL import Image
from torchvision import transforms
parser = a... | 1,199 | 26.906977 | 114 | py |
kserve | kserve-master/docs/samples/v1beta1/torchserve/v2/tensor_conv/utils.py | import numpy as np
from PIL import Image
_DatatypeToNumpy = {
"BOOL": "bool",
"UINT8": "uint8",
"UINT16": "uint16",
"UINT32": "uint32",
"UINT64": "uint64",
"INT8": "int8",
"INT16": "int16",
"INT32": "int32",
"INT64": "int64",
"FP16": "float16",
"FP32": "float32",
"FP64":... | 884 | 18.666667 | 74 | py |
kserve | kserve-master/docs/samples/v1beta1/transformer/collocation/README.md | # Collocate transformer and predictor in same pod
Please refer to [collocate transformer with predictor](https://github.com/kserve/website/tree/main/docs/modelserving/v1beta1/transformer/feast/) on KServe website repository.
| 227 | 44.6 | 174 | md |
kserve | kserve-master/docs/samples/v1beta1/transformer/collocation/collocation.yaml | apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: custom-transformer-collocation
spec:
predictor:
containers:
- name: kserve-container
image: kserve/custom-model-grpc:latest
args:
- --model_name=custom-model
- --grpc_port=8082
- --ht... | 713 | 26.461538 | 73 | yaml |
kserve | kserve-master/docs/samples/v1beta1/transformer/feast/README.md | # Predict on an InferenceService with transformer using Feast online feature store
Please refer to [feast transformer example](https://github.com/kserve/website/tree/main/docs/modelserving/v1beta1/transformer/feast/) on KServe website repository for deploying feast transformer server. | 288 | 71.25 | 202 | md |
kserve | kserve-master/docs/samples/v1beta1/transformer/feast/driver_transformer.Dockerfile | FROM python:3.9-slim
COPY driver_transformer driver_transformer
WORKDIR driver_transformer
RUN pip install --upgrade pip
RUN pip install -e .
ENTRYPOINT ["python", "-m", "driver_transformer"]
| 193 | 23.25 | 49 | Dockerfile |
kserve | kserve-master/docs/samples/v1beta1/transformer/feast/driver_transformer.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-driver-transformer"
spec:
transformer:
containers:
- image: "kserve/driver-transformer:latest"
name: driver-container
command:
- "python"
- "-m"
- "driver_transformer"
... | 739 | 27.461538 | 67 | yaml |
kserve | kserve-master/docs/samples/v1beta1/transformer/feast/driver_transformer_modelmesh.yaml | apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "sklearn-driver-transformer"
annotations:
serving.kserve.io/deploymentMode: ModelMesh
spec:
transformer:
containers:
- image: "{username}/driver-transformer:latest"
name: driver-container
command:
... | 817 | 27.206897 | 67 | yaml |