Repository: kubeflow/example-seldon
Branch: master
Commit: d0e056aaa7ec
Files: 69
Total size: 98.7 KB
Directory structure:
gitextract_39vdm35i/
├── .gitignore
├── LICENSE
├── OWNERS
├── README.md
├── VERSION
├── k8s_serving/
│ ├── ab_test_sklearn_tensorflow.json
│ ├── ambassador-auth-service-config.yaml
│ ├── ambassador-auth-service-setup.yaml
│ ├── epsilon_greedy.json
│ ├── epsilon_greedy_3way.json
│ ├── serving_model.json
│ ├── serving_r_model.json
│ └── serving_sk_model.json
├── k8s_train/
│ ├── sklearn_training_job.yaml
│ └── tfJob.json
├── models/
│ ├── r_mnist/
│ │ ├── runtime/
│ │ │ ├── Dockerfile
│ │ │ ├── Makefile
│ │ │ ├── install.R
│ │ │ └── mnist.R
│ │ └── train/
│ │ ├── Dockerfile
│ │ ├── Makefile
│ │ ├── get_data.sh
│ │ ├── install.R
│ │ ├── train.R
│ │ └── train.sh
│ ├── sk_mnist/
│ │ ├── runtime/
│ │ │ ├── Dockerfile
│ │ │ ├── Makefile
│ │ │ ├── SkMnist.py
│ │ │ ├── contract.json
│ │ │ └── requirements.txt
│ │ └── train/
│ │ ├── Dockerfile
│ │ ├── Makefile
│ │ ├── create_model.py
│ │ ├── requirements.txt
│ │ └── train.sh
│ └── tf_mnist/
│ ├── runtime/
│ │ ├── DeepMnist.py
│ │ ├── Dockerfile
│ │ ├── Makefile
│ │ ├── contract.json
│ │ └── requirements.txt
│ └── train/
│ ├── Dockerfile
│ ├── Makefile
│ └── create_model.py
├── nfs.md
├── notebooks/
│ ├── Makefile
│ ├── __init__.py
│ ├── create-protos.sh
│ ├── proto/
│ │ ├── __init__.py
│ │ └── prediction.proto
│ ├── requirements.txt
│ ├── serving.ipynb
│ ├── training.ipynb
│ ├── utils.py
│ └── visualizer.py
├── scripts/
│ ├── README.md
│ ├── create_demo.sh
│ ├── delete-demo.sh
│ ├── env-example.sh
│ ├── nfs-pvc.yaml
│ ├── port-forwards.sh
│ └── watch-mnist.sh
└── workflows/
├── serving-r-mnist-workflow.yaml
├── serving-sk-mnist-workflow.yaml
├── serving-tf-mnist-workflow.md
├── serving-tf-mnist-workflow.yaml
├── training-r-mnist-workflow.yaml
├── training-sk-mnist-workflow.yaml
├── training-tf-mnist-workflow.md
└── training-tf-mnist-workflow.yaml
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# build
/target/
/public
cluster-manager/.m2/
.ipynb_checkpoints
# eclipse
.classpath
.settings/
.project
# Netbeans and IntelliJ files
!.gitignore
/nbproject
/*.ipr
/*.iws
*.iml
.idea
/bin/
*~
*.pyc
.m2
\#*
_*.yaml
_*.json
models/tf_mnist/runtime/build/
models/sk_mnist/runtime/build/
models/sk_mnist/train/mnist-original.mat
notebooks/proto/prediction_pb2.py
notebooks/proto/prediction_pb2_grpc.py
notebooks/tensorflow
scripts/kubeflow_src
scripts/env.sh
================================================
FILE: LICENSE
================================================
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================================================
FILE: OWNERS
================================================
approvers:
- cliveseldon
- jinchihe
- ryandawsonuk
reviewers:
- cliveseldon
- jinchihe
================================================
FILE: README.md
================================================
## :warning: **kubeflow/example-seldon is not maintained**
This repository has been deprecated and [archived](https://github.com/kubeflow/community/issues/479) on Nov 30th, 2021.
# Train and Deploy Machine Learning Models on Kubernetes with Kubeflow and Seldon-Core
Using:
* [kubeflow](https://github.com/kubeflow/kubeflow)
* [seldon-core](https://github.com/SeldonIO/seldon-core)
The example will be the MNIST handwritten digit classification task. We will train 3 different models to solve this task:
* A TensorFlow neural network model.
* A scikit-learn random forest model.
* An R least squares model.
We will then show various rolling deployments
1. Deploy the single Tensorflow model.
2. Do a rolling update to an AB test of the Tensorflow model and the sklearn model.
3. Do a rolling update to a Multi-armed Bandit over all 3 models to direct traffic in real time to the best model.
In the follow we will:
1. [Install kubeflow and seldon-core on a kubernetes cluster](#setup)
1. [Train the models](#train-the-models)
1. [Serve the models](#serve-the-models)
# Requirements
* gcloud
* kubectl
* ksonnet
* argo
# Setup
There is a consolidated script to create the demo which can be found [here](./scripts/README.md). For a step by step guide do the following:
1. [Install kubeflow on GKE](https://www.kubeflow.org/docs/started/getting-started-gke/). This should create kubeflow in a namespace ```kubeflow```. We suggest you use the command line install so you can easily modify your Ksonnet installation. Ensure you have the environment variables `KUBEFLOW_SRC` and `KFAPP` set. OAUTH is preferred as with basic auth [port-forwarding to ambassador is insufficient](https://github.com/kubeflow/kubeflow/issues/3213)
1. Install seldon. Go to your Ksonnet application folder setup in the previous step and run
```
cd ${KUBEFLOW_SRC}/${KFAPP}/ks_app
ks pkg install kubeflow/seldon
ks generate seldon seldon
ks apply default -c seldon
```
1. Install Helm
```
kubectl -n kube-system create sa tiller
kubectl create clusterrolebinding tiller --clusterrole cluster-admin --serviceaccount=kube-system:tiller
helm init --service-account tiller
kubectl rollout status deploy/tiller-deploy -n kube-system
```
1. Create an NFS disk and persistent volume claim called `nfs-1`. You can follow one guide on create an NFS volume using Google Filestore [here](https://cloud.google.com/community/tutorials/gke-filestore-dynamic-provisioning). A consolidated set of steps is shown [here](nfs.md)
1. Add Cluster Roles so Argo can start jobs successfully
```
kubectl create clusterrolebinding my-cluster-admin-binding --clusterrole=cluster-admin --user=$(gcloud info --format="value(config.account)")
kubectl create clusterrolebinding default-admin2 --clusterrole=cluster-admin --serviceaccount=kubeflow:default
```
1. Install Seldon Analytics Dashboard
```
helm install seldon-core-analytics --name seldon-core-analytics --set grafana_prom_admin_password=password --set persistence.enabled=false --repo https://storage.googleapis.com/seldon-charts --namespace kubeflow
```
1. Port forward the dashboard when running
```
kubectl port-forward $(kubectl get pods -n kubeflow -l app=grafana-prom-server -o jsonpath='{.items[0].metadata.name}') -n kubeflow 3000:3000
```
1. Visit http://localhost:3000/dashboard/db/prediction-analytics?refresh=5s&orgId=1 and login using "admin" and the password you set above when launching with helm.
# MNIST models
## Tensorflow Model
* [Python training code](models/tf_mnist/train/create_model.py)
* [Python runtime prediction code](models/tf_mnist/runtime/DeepMnist.py)
* [Dockerfile to wrap runtime prediction code to run under seldon-Core](models/tf_mnist/runtime/Dockerfile).
## SKLearn Model
* [Python training code](models/sk_mnist/train/create_model.py)
* [Python runtime prediction code](models/sk_mnist/runtime/SkMnist.py)
* [Dockerfile to wrap runtime prediction code to run under seldon-Core](models/sk_mnist/runtime/Dockerfile).
## R Model
* [R training code](models/r_mnist/train/train.R)
* [R runtime prediction code](models/r_mnist/runtime/mnist.R)
* [Dockerfile to wrap runtime prediction code to run under seldon-Core](models/r_mnist/runtime/Dockerfile).
# Train the Models
Follow the steps in [./notebooks/training.ipynb](./notebooks/training.ipynb) to:
* Run Argo Jobs for each model to:
* Creating training images and push to repo
* Run training
* Create runtime prediction images and push to repo
* Deploy individual runtime model
**To push to your own repo the Docker images you will need to setup your docker credentials as a Kubernetes secret containing a [config.json](https://www.projectatomic.io/blog/2016/03/docker-credentials-store/). To do this you can find your docker home (typically ~/.docker) and run `kubectl create secret generic docker-config --from-file=config.json=${DOCKERHOME}/config.json --type=kubernetes.io/config` to [create a secret](https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/#registry-secret-existing-credentials).**
# Serve the Models
Follow the steps in [./notebooks/serving.ipynb](./notebooks/serving.ipynb) to:
1. Deploy the single Tensorflow model.
2. Do a rolling update to an AB test of the Tensorflow model and the sklearn model.
3. Do a rolling update to a Multi-armed Bandit over all 3 models to direct traffic in real time to the best model.
To ensure the notebook can run successfully install the python dependencies:
```
pip install -r notebooks/requirements.txt
```
If you have [installed the Seldon-Core analytics](#setup) you can view them on the grafana dashboard:
================================================
FILE: VERSION
================================================
0.1
================================================
FILE: k8s_serving/ab_test_sklearn_tensorflow.json
================================================
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "mnist-classifier"
},
"spec": {
"annotations": {
"project_name": "kubeflow-seldon",
"deployment_version": "v1",
"seldon.io/rest-connection-timeout": "100"
},
"name": "mnist-classifier",
"predictors": [
{
"componentSpecs": [{
"spec": {
"containers": [
{
"image": "seldonio/deepmnistclassifier_runtime:0.2",
"name": "tf-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
},
{
"image": "seldonio/skmnistclassifier_runtime:0.2",
"name": "sk-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
}
],
"volumes": [
{
"name": "persistent-storage",
"volumeSource" : {
"persistentVolumeClaim": {
"claimName": "nfs-1"
}
}
}
]
}
}],
"name": "mnist-classifier",
"replicas": 1,
"annotations": {
"predictor_version": "v1"
},
"graph": {
"name": "random-ab-test",
"implementation":"RANDOM_ABTEST",
"parameters": [
{
"name":"ratioA",
"value":"0.5",
"type":"FLOAT"
}
],
"children": [
{
"name": "tf-model",
"endpoint":{
"type":"REST"
},
"type":"MODEL"
},
{
"name": "sk-model",
"endpoint":{
"type":"REST"
},
"type":"MODEL"
}
]
}
}
]
}
}
================================================
FILE: k8s_serving/ambassador-auth-service-config.yaml
================================================
---
apiVersion: v1
kind: Service
metadata:
name: example-auth
annotations:
getambassador.io/config: |
---
apiVersion: ambassador/v0
kind: Module
name: authentication
config:
auth_service: "example-auth:3000"
path_prefix: "/extauth"
spec:
type: ClusterIP
selector:
app: example-auth
ports:
- port: 3000
name: http-example-auth
targetPort: http-api
================================================
FILE: k8s_serving/ambassador-auth-service-setup.yaml
================================================
---
apiVersion: v1
kind: Service
metadata:
name: example-auth
spec:
type: ClusterIP
selector:
app: example-auth
ports:
- port: 3000
name: http-example-auth
targetPort: http-api
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: example-auth
spec:
replicas: 1
strategy:
type: RollingUpdate
template:
metadata:
labels:
app: example-auth
spec:
containers:
- name: example-auth
image: seldonio/ambassador-auth-service:1.1.1
imagePullPolicy: IfNotPresent
ports:
- name: http-api
containerPort: 3000
resources:
limits:
cpu: "0.1"
memory: 100Mi
================================================
FILE: k8s_serving/epsilon_greedy.json
================================================
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "mnist-classifier"
},
"spec": {
"annotations": {
"project_name": "kubeflow-seldon",
"deployment_version": "v1"
},
"name": "mnist-classifier",
"predictors": [
{
"componentSpecs": [{
"spec": {
"containers": [
{
"image": "seldonio/deepmnistclassifier_runtime:0.2",
"name": "tf-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
},
{
"image": "seldonio/skmnistclassifier_runtime:0.2",
"name": "sk-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
},
{
"image": "seldonio/mab_epsilon_greedy:1.1",
"name": "eg-router"
}
],
"volumes": [
{
"name": "persistent-storage",
"volumeSource" : {
"persistentVolumeClaim": {
"claimName": "nfs-1"
}
}
}
]
}
}],
"name": "mnist-classifier",
"replicas": 1,
"annotations": {
"predictor_version": "v1"
},
"graph": {
"name": "eg-router",
"type":"ROUTER",
"parameters": [
{
"name": "n_branches",
"value": "2",
"type": "INT"
},
{
"name": "epsilon",
"value": "0.1",
"type": "FLOAT"
},
{
"name": "verbose",
"value": "1",
"type": "BOOL"
}
],
"children": [
{
"name": "sk-model",
"type": "MODEL",
"endpoint":{
"type":"REST"
}
},
{
"name": "tf-model",
"type": "MODEL",
"endpoint":{
"type":"REST"
}
}
]
}
}
]
}
}
================================================
FILE: k8s_serving/epsilon_greedy_3way.json
================================================
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "mnist-classifier"
},
"spec": {
"annotations": {
"project_name": "kubeflow-seldon",
"deployment_version": "v1"
},
"name": "mnist-classifier",
"predictors": [
{
"componentSpecs": [{
"spec": {
"containers": [
{
"image": "seldonio/deepmnistclassifier_runtime:0.2",
"name": "tf-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
},
{
"image": "seldonio/skmnistclassifier_runtime:0.2",
"name": "sk-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
},
{
"image": "seldonio/rmnistclassifier_runtime:0.2",
"name": "r-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
},
{
"image": "seldonio/mab_epsilon_greedy:1.1",
"name": "eg-router"
}
],
"volumes": [
{
"name": "persistent-storage",
"volumeSource" : {
"persistentVolumeClaim": {
"claimName": "nfs-1"
}
}
}
]
}
}],
"name": "mnist-classifier",
"replicas": 1,
"annotations": {
"predictor_version": "v1"
},
"graph": {
"name": "eg-router",
"type":"ROUTER",
"parameters": [
{
"name": "n_branches",
"value": "3",
"type": "INT"
},
{
"name": "epsilon",
"value": "0.2",
"type": "FLOAT"
},
{
"name": "verbose",
"value": "1",
"type": "BOOL"
}
],
"children": [
{
"name": "sk-model",
"type": "MODEL",
"endpoint":{
"type":"REST"
}
},
{
"name": "tf-model",
"type": "MODEL",
"endpoint":{
"type":"REST"
}
},
{
"name": "r-model",
"type": "MODEL",
"endpoint":{
"type":"REST"
}
}
]
}
}
]
}
}
================================================
FILE: k8s_serving/serving_model.json
================================================
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "mnist-classifier"
},
"spec": {
"annotations": {
"deployment_version": "v1",
"project_name": "MNIST Example",
"seldon.io/engine-separate-pod": "false",
"seldon.io/rest-connection-timeout": "100"
},
"name": "mnist-classifier",
"predictors": [
{
"annotations": {
"predictor_version": "v1"
},
"componentSpecs": [{
"spec": {
"containers": [
{
"image": "seldonio/deepmnistclassifier_runtime:0.2",
"imagePullPolicy": "Always",
"name": "tf-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
}
],
"terminationGracePeriodSeconds": 1,
"volumes": [
{
"name": "persistent-storage",
"volumeSource" : {
"persistentVolumeClaim": {
"claimName": "nfs-1"
}
}
}
]
}
}],
"graph": {
"children": [],
"endpoint": {
"type": "REST"
},
"name": "tf-model",
"type": "MODEL"
},
"name": "mnist-classifier",
"replicas": 1
}
]
}
}
================================================
FILE: k8s_serving/serving_r_model.json
================================================
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "mnist-classifier"
},
"spec": {
"annotations": {
"deployment_version": "v1",
"project_name": "MNIST Example"
},
"name": "mnist-classifier",
"predictors": [
{
"annotations": {
"predictor_version": "v1"
},
"componentSpecs": [{
"spec": {
"containers": [
{
"image": "seldonio/rmnistclassifier_runtime:0.2",
"imagePullPolicy": "Always",
"name": "r-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
}
],
"terminationGracePeriodSeconds": 1,
"volumes": [
{
"name": "persistent-storage",
"volumeSource" : {
"persistentVolumeClaim": {
"claimName": "nfs-1"
}
}
}
]
}
}],
"graph": {
"children": [],
"endpoint": {
"type": "REST"
},
"name": "r-model",
"type": "MODEL"
},
"name": "mnist-classifier",
"replicas": 1
}
]
}
}
================================================
FILE: k8s_serving/serving_sk_model.json
================================================
{
"apiVersion": "machinelearning.seldon.io/v1alpha2",
"kind": "SeldonDeployment",
"metadata": {
"labels": {
"app": "seldon"
},
"name": "mnist-classifier"
},
"spec": {
"annotations": {
"deployment_version": "v1",
"project_name": "MNIST Example"
},
"name": "mnist-classifier",
"predictors": [
{
"annotations": {
"predictor_version": "v1"
},
"componentSpecs": [{
"spec": {
"containers": [
{
"image": "seldonio/skmnistclassifier_runtime:0.2",
"imagePullPolicy": "Always",
"name": "sk-model",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
}
],
"terminationGracePeriodSeconds": 1,
"volumes": [
{
"name": "persistent-storage",
"volumeSource" : {
"persistentVolumeClaim": {
"claimName": "nfs-1"
}
}
}
]
}
}],
"graph": {
"children": [],
"endpoint": {
"type": "REST"
},
"name": "sk-model",
"type": "MODEL"
},
"name": "mnist-classifier",
"replicas": 1
}
]
}
}
================================================
FILE: k8s_train/sklearn_training_job.yaml
================================================
apiVersion: "batch/v1"
kind: "Job"
metadata:
name: "sk-train"
namespace: "default"
spec:
template:
metadata:
name: "sk-train"
spec:
containers:
-
image: "seldonio/skmnistclassifier_trainer:0.1"
name: "sk-train"
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
restartPolicy: "Never"
volumes:
-
name: "persistent-storage"
persistentVolumeClaim:
claimName: "ml-data"
================================================
FILE: k8s_train/tfJob.json
================================================
{
"apiVersion": "kubeflow.org/v1alpha1",
"kind": "TFJob",
"metadata": {
"name": "mnist-train",
"namespace": "kubeflow-seldon"
},
"spec": {
"replicaSpecs": [
{
"replicas": 1,
"template": {
"spec": {
"containers": [
{
"image": "seldonio/deepmnistclassifier_trainer:0.1",
"name": "tensorflow",
"volumeMounts": [
{
"mountPath": "/data",
"name": "persistent-storage"
}
]
}
],
"restartPolicy": "OnFailure",
"volumes": [
{
"name": "persistent-storage",
"persistentVolumeClaim": {
"claimName": "ml-data"
}
}
]
}
},
"tfReplicaType": "MASTER"
}
]
}
}
================================================
FILE: models/r_mnist/runtime/Dockerfile
================================================
FROM rocker/r-apt:bionic
RUN apt-get update && \
apt-get install -y -qq \
r-cran-plumber \
r-cran-jsonlite \
r-cran-optparse \
r-cran-stringr \
r-cran-urltools \
r-cran-caret \
r-cran-pls \
curl
ENV MODEL_NAME mnist.R
ENV API_TYPE REST
ENV SERVICE_TYPE MODEL
ENV PERSISTENCE 0
RUN mkdir microservice
COPY . /microservice
WORKDIR /microservice
RUN curl -OL https://raw.githubusercontent.com/SeldonIO/seldon-core/v0.2.7/wrappers/s2i/R/microservice.R > /microservice/microservice.R
EXPOSE 5000
CMD Rscript microservice.R --model $MODEL_NAME --api $API_TYPE --service $SERVICE_TYPE --persistence $PERSISTENCE
================================================
FILE: models/r_mnist/runtime/Makefile
================================================
seldon_build_image_local:
docker build . -t seldonio/rmnistclassifier_runtime:0.2
seldon_push_docker_hub:
docker push seldonio/rmnistclassifier_runtime:0.2
================================================
FILE: models/r_mnist/runtime/install.R
================================================
install.packages('pls')
================================================
FILE: models/r_mnist/runtime/mnist.R
================================================
library(methods)
predict.mnist <- function(mnist,newdata=list()) {
cn <- 1:784
for (i in seq_along(cn)){cn[i] <- paste("X",cn[i],sep = "")}
colnames(newdata) <- cn
predict(mnist$model, newdata = newdata, type='prob')
}
send_feedback.mnist <- function(mnist,request=list(),reward=1,truth=list()) {
}
new_mnist <- function(filename) {
model <- readRDS(filename)
structure(list(model=model), class = "mnist")
}
initialise_seldon <- function(params) {
new_mnist("/data/model.Rds")
}
================================================
FILE: models/r_mnist/train/Dockerfile
================================================
FROM rocker/r-apt:bionic
RUN apt-get update && \
apt-get install -y -qq \
r-cran-caret \
r-cran-pls \
r-cran-e1071
RUN R -e 'install.packages("doParallel")'
RUN mkdir training
COPY /train.R /training/train.R
COPY /get_data.sh /training/get_data.sh
COPY ./train.sh /training/train.sh
RUN cd /training && \
./get_data.sh
WORKDIR /training
CMD ["/training/train.sh"]
================================================
FILE: models/r_mnist/train/Makefile
================================================
build_model:
docker build --force-rm=true -t seldonio/rmnistclassifier_trainer:0.1 .
push_image:
docker push seldonio/rmnistclassifier_trainer:0.1
================================================
FILE: models/r_mnist/train/get_data.sh
================================================
wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
gunzip train-images-idx3-ubyte.gz
gunzip train-labels-idx1-ubyte.gz
gunzip t10k-images-idx3-ubyte.gz
gunzip t10k-labels-idx1-ubyte.gz
================================================
FILE: models/r_mnist/train/install.R
================================================
install.packages('caret')
install.packages('doParallel')
install.packages('pls')
install.packages('e1071')
================================================
FILE: models/r_mnist/train/train.R
================================================
library(caret)
library(doParallel)
# Enable parallel processing.
cl <- makeCluster(detectCores())
registerDoParallel(cl)
# Load the MNIST digit recognition dataset into R
# http://yann.lecun.com/exdb/mnist/
# assume you have all 4 files and gunzip'd them
# creates train$n, train$x, train$y and test$n, test$x, test$y
# e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28)
# call: show_digit(train$x[5,]) to see a digit.
# brendan o'connor - gist.github.com/39760 - anyall.org
load_mnist <- function() {
load_image_file <- function(filename) {
ret = list()
f = file(filename,'rb')
readBin(f,'integer',n=1,size=4,endian='big')
ret$n = readBin(f,'integer',n=1,size=4,endian='big')
nrow = readBin(f,'integer',n=1,size=4,endian='big')
ncol = readBin(f,'integer',n=1,size=4,endian='big')
x = readBin(f,'integer',n=ret$n*nrow*ncol,size=1,signed=F)
ret$x = matrix(x, ncol=nrow*ncol, byrow=T)
close(f)
ret
}
load_label_file <- function(filename) {
f = file(filename,'rb')
readBin(f,'integer',n=1,size=4,endian='big')
n = readBin(f,'integer',n=1,size=4,endian='big')
y = readBin(f,'integer',n=n,size=1,signed=F)
close(f)
y
}
train <<- load_image_file('train-images-idx3-ubyte')
test <<- load_image_file('t10k-images-idx3-ubyte')
train$y <<- load_label_file('train-labels-idx1-ubyte')
test$y <<- load_label_file('t10k-labels-idx1-ubyte')
}
show_digit <- function(arr784, col=gray(12:1/12), ...) {
image(matrix(arr784, nrow=28)[,28:1], col=col, ...)
}
train <- data.frame()
test <- data.frame()
# Load data.
load_mnist()
# Normalize: X = (X - min) / (max - min) => X = (X - 0) / (255 - 0) => X = X / 255.
train$x <- train$x / 255
# Setup training data with digit and pixel values with 60/40 split for train/cv.
inTrain = data.frame(y=train$y, train$x)
inTrain$y <- as.factor(inTrain$y)
trainIndex = createDataPartition(inTrain$y, p = 0.60,list=FALSE)
training = inTrain[trainIndex,]
cv = inTrain[-trainIndex,]
# SVM. 95/94.
#fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
fit <- train(y ~ ., data = head(training, 1000), method = 'pls')
results <- predict(fit, newdata = head(cv, 1000), type='prob')
#confusionMatrix(results, head(cv$y, 1000))
saveRDS(fit, file = "/data/model.Rds", compress = TRUE)
================================================
FILE: models/r_mnist/train/train.sh
================================================
#!/usr/bin/env bash
# exit when any command fails
set -e
until mountpoint -q /data; do
echo "$(date) - waiting for /data to be mounted..."
sleep 1
done
ls -l /data
Rscript train.R
ls -l /data
================================================
FILE: models/sk_mnist/runtime/Dockerfile
================================================
FROM python:3.7-slim
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
EXPOSE 5000
# Define environment variable
ENV MODEL_NAME SkMnist
ENV API_TYPE REST
ENV SERVICE_TYPE MODEL
ENV PERSISTENCE 0
CMD exec seldon-core-microservice $MODEL_NAME $API_TYPE --service-type $SERVICE_TYPE --persistence $PERSISTENCE
================================================
FILE: models/sk_mnist/runtime/Makefile
================================================
seldon_build_image_local:
docker build . -t seldonio/skmnistclassifier_runtime:0.2
seldon_push_docker_hub:
docker push seldonio/skmnistclassifier_runtime:0.2
================================================
FILE: models/sk_mnist/runtime/SkMnist.py
================================================
from sklearn.externals import joblib
class SkMnist(object):
def __init__(self):
self.class_names = ["class:{}".format(str(i)) for i in range(10)]
self.clf = joblib.load('/data/sk.pkl')
def predict(self,X,feature_names):
predictions = self.clf.predict_proba(X)
return predictions
================================================
FILE: models/sk_mnist/runtime/contract.json
================================================
{
"features":[
{
"name":"x",
"dtype":"FLOAT",
"ftype":"continuous",
"range":[0,1],
"repeat":784
}
],
"targets":[
{
"name":"class",
"dtype":"FLOAT",
"ftype":"continuous",
"range":[0,1],
"repeat":10
}
]
}
================================================
FILE: models/sk_mnist/runtime/requirements.txt
================================================
scipy>= 0.13.3
scikit-learn>=0.18
seldon-core>=0.2.5
================================================
FILE: models/sk_mnist/train/Dockerfile
================================================
FROM python:3.7-slim
RUN apt-get update -y
RUN apt-get install -y python-pip python-dev build-essential
COPY /requirements.txt /tmp/
RUN cd /tmp && \
pip install --no-cache-dir -r requirements.txt
RUN mkdir training
COPY ./create_model.py /training/create_model.py
COPY ./train.sh /training/train.sh
WORKDIR /training
CMD ["/training/train.sh"]
================================================
FILE: models/sk_mnist/train/Makefile
================================================
build_model:
docker build --force-rm=true -t seldonio/skmnistclassifier_trainer:0.2 .
push_image:
docker push seldonio/skmnistclassifier_trainer:0.2
================================================
FILE: models/sk_mnist/train/create_model.py
================================================
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets, metrics
from sklearn.utils import shuffle
from sklearn.datasets import fetch_mldata
from sklearn.externals import joblib
from six.moves import urllib
if __name__ == '__main__':
try:
mnist = fetch_mldata('MNIST original')
except:
print("Could not download MNIST data from mldata.org, trying alternative...")
# Alternative method to load MNIST, if mldata.org is down
from scipy.io import loadmat
mnist_alternative_url = "https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat"
mnist_path = "./mnist-original.mat"
response = urllib.request.urlopen(mnist_alternative_url)
with open(mnist_path, "wb") as f:
content = response.read()
f.write(content)
mnist_raw = loadmat(mnist_path)
mnist = {
"data": mnist_raw["data"].T,
"target": mnist_raw["label"][0],
"COL_NAMES": ["label", "data"],
"DESCR": "mldata.org dataset: mnist-original",
}
print("Success!")
#mnist = fetch_mldata('MNIST original', data_home="./mnist_sklearn")
# To apply a classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(mnist['data'])
data = mnist['data'].reshape((n_samples, -1))
targets = mnist['target']
data,targets = shuffle(data,targets)
classifier = RandomForestClassifier(n_estimators=30)
# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples // 2], targets[:n_samples // 2])
# Now predict the value of the digit on the second half:
expected = targets[n_samples // 2:]
test_data = data[n_samples // 2:]
print(classifier.score(test_data, expected))
predicted = classifier.predict(data[n_samples // 2:])
print("Classification report for classifier %s:\n%s\n"
% (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
joblib.dump(classifier, '/data/sk.pkl')
================================================
FILE: models/sk_mnist/train/requirements.txt
================================================
scipy
scikit-learn>=0.18
six
================================================
FILE: models/sk_mnist/train/train.sh
================================================
#!/usr/bin/env bash
# exit when any command fails
set -e
until mountpoint -q /data; do
echo "$(date) - wainting for /data to be mounted..."
sleep 1
done
ls -l /data
python -u create_model.py
ls -l /data
================================================
FILE: models/tf_mnist/runtime/DeepMnist.py
================================================
import tensorflow as tf
import logging
logging.basicConfig(format='%(asctime)s.%(msecs)03d %(levelname)s {%(module)s} [%(funcName)s] %(message)s', datefmt='%Y-%m-%d,%H:%M:%S', level=logging.INFO)
logger = logging.getLogger(__name__)
class DeepMnist(object):
def __init__(self):
self.class_names = ["class:{}".format(str(i)) for i in range(10)]
self.sess = tf.Session()
saver = tf.train.import_meta_graph("/data/deep_mnist_model.meta")
saver.restore(self.sess,tf.train.latest_checkpoint("/data/"))
graph = tf.get_default_graph()
self.x = graph.get_tensor_by_name("x:0")
self.y = graph.get_tensor_by_name("y:0")
def predict(self,X,feature_names):
predictions = self.sess.run(self.y,feed_dict={self.x:X})
return predictions
================================================
FILE: models/tf_mnist/runtime/Dockerfile
================================================
FROM python:3.7-slim
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
EXPOSE 5000
# Define environment variable
ENV MODEL_NAME DeepMnist
ENV API_TYPE REST
ENV SERVICE_TYPE MODEL
ENV PERSISTENCE 0
CMD exec seldon-core-microservice $MODEL_NAME $API_TYPE --service-type $SERVICE_TYPE --persistence $PERSISTENCE
================================================
FILE: models/tf_mnist/runtime/Makefile
================================================
seldon_build_image_local:
docker build . -t seldonio/deepmnistclassifier_runtime:0.2
seldon_push_docker_hub:
docker push seldonio/deepmnistclassifier_runtime:0.2
================================================
FILE: models/tf_mnist/runtime/contract.json
================================================
{
"features":[
{
"name":"x",
"dtype":"FLOAT",
"ftype":"continuous",
"range":[0,1],
"repeat":784
}
],
"targets":[
{
"name":"class",
"dtype":"FLOAT",
"ftype":"continuous",
"range":[0,1],
"repeat":10
}
]
}
================================================
FILE: models/tf_mnist/runtime/requirements.txt
================================================
tensorflow==1.13.1
seldon-core>=0.2.5
================================================
FILE: models/tf_mnist/train/Dockerfile
================================================
FROM tensorflow/tensorflow:1.3.0
RUN mkdir training
COPY ./create_model.py /training/create_model.py
WORKDIR /training
CMD ["python","-u","create_model.py"]
================================================
FILE: models/tf_mnist/train/Makefile
================================================
build_model:
docker build --force-rm=true -t seldonio/deepmnistclassifier_trainer:0.1 .
push_image:
docker push seldonio/deepmnistclassifier_trainer:0.1
================================================
FILE: models/tf_mnist/train/create_model.py
================================================
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)
import tensorflow as tf
if __name__ == '__main__':
x = tf.placeholder(tf.float32, [None,784], name="x")
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b, name="y")
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict = {x: mnist.test.images, y_:mnist.test.labels}))
saver = tf.train.Saver()
saver.save(sess, "/data/deep_mnist_model")
================================================
FILE: nfs.md
================================================
# Example NFS Setup
The steps below are a consolidated set of steps following the guide [here](https://cloud.google.com/community/tutorials/gke-filestore-dynamic-provisioning).
Set the following variables
* `FS` : the name of your filestore
* `PROJECT` : Your Google Project
* `ZONE` : Your GCP Zone
Create a Google Filestore and install the helm chart for nfs-client-provisioner to use it.
```
PROJECT=seldon-demos
FS=mnist-data
ZONE=europe-west1-b
gcloud beta filestore instances create ${FS} --project=${PROJECT} --location=${ZONE} --tier=STANDARD --file-share=name="volumes",capacity=1TB --network=name="default",reserved-ip-range="10.0.0.0/29"
FSADDR=$(gcloud beta filestore instances describe ${FS} --project=${PROJECT} --location=${ZONE} --format="value(networks.ipAddresses[0])")
helm install stable/nfs-client-provisioner --name nfs-cp --set nfs.server=${FSADDR} --set nfs.path=/volumes
kubectl rollout status deploy/nfs-cp-nfs-client-provisioner -n kubeflow
```
To create the NFS claim save the following and apply to your kubernetes cluster
```
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: nfs-1
spec:
accessModes:
- ReadWriteMany
storageClassName: nfs-client
resources:
requests:
storage: 30Gi
```
================================================
FILE: notebooks/Makefile
================================================
SHELL=/bin/bash
tensorflow/core/framework/tensor.proto:
./create-protos.sh
.PHONY: create_protos
create_protos: tensorflow/core/framework/tensor.proto
.PHONY: clean
clean:
@rm -rfv tensorflow
================================================
FILE: notebooks/__init__.py
================================================
================================================
FILE: notebooks/create-protos.sh
================================================
#!/bin/bash
release=${1:-"master"}
echo Downloading proto files for ${release}
base=https://raw.githubusercontent.com/tensorflow
tensorflow_base=${base}/tensorflow/${release}
base_folder=tensorflow/core/framework/
mkdir -p ${base_folder}
curl -s ${tensorflow_base}/tensorflow/core/framework/types.proto > ${base_folder}/types.proto
curl -s ${tensorflow_base}/tensorflow/core/framework/resource_handle.proto > ${base_folder}/resource_handle.proto
curl -s ${tensorflow_base}/tensorflow/core/framework/tensor_shape.proto > ${base_folder}/tensor_shape.proto
curl -s ${tensorflow_base}/tensorflow/core/framework/tensor.proto > ${base_folder}/tensor.proto
================================================
FILE: notebooks/proto/__init__.py
================================================
================================================
FILE: notebooks/proto/prediction.proto
================================================
syntax = "proto3";
import "google/protobuf/struct.proto";
import "tensorflow/core/framework/tensor.proto";
package seldon.protos;
option java_package = "io.seldon.protos";
option java_outer_classname = "PredictionProtos";
option go_package = "github.com/seldonio/seldon-core/examples/wrappers/go/pkg/api";
// [START Messages]
message SeldonMessage {
Status status = 1;
Meta meta = 2;
oneof data_oneof {
DefaultData data = 3;
bytes binData = 4;
string strData = 5;
}
}
message DefaultData {
repeated string names = 1;
oneof data_oneof {
Tensor tensor = 2;
google.protobuf.ListValue ndarray = 3;
tensorflow.TensorProto tftensor = 4;
}
}
message Tensor {
repeated int32 shape = 1 [packed=true];
repeated double values = 2 [packed=true];
}
message Meta {
string puid = 1;
map<string,google.protobuf.Value> tags = 2;
map<string,int32> routing = 3;
map<string,string> requestPath = 4;
repeated Metric metrics = 5;
}
message Metric {
enum MetricType {
COUNTER = 0;
GAUGE = 1;
TIMER = 2;
}
string key = 1;
MetricType type = 2;
float value = 3;
map<string,string> tags = 4;
}
message SeldonMessageList {
repeated SeldonMessage seldonMessages = 1;
}
message Status {
enum StatusFlag {
SUCCESS = 0;
FAILURE = 1;
}
int32 code = 1;
string info = 2;
string reason = 3;
StatusFlag status = 4;
}
message Feedback {
SeldonMessage request = 1;
SeldonMessage response = 2;
float reward = 3;
SeldonMessage truth = 4;
}
message RequestResponse {
SeldonMessage request = 1;
SeldonMessage response = 2;
}
// [END Messages]
// [START Services]
service Generic {
rpc TransformInput(SeldonMessage) returns (SeldonMessage) {};
rpc TransformOutput(SeldonMessage) returns (SeldonMessage) {};
rpc Route(SeldonMessage) returns (SeldonMessage) {};
rpc Aggregate(SeldonMessageList) returns (SeldonMessage) {};
rpc SendFeedback(Feedback) returns (SeldonMessage) {};
}
service Model {
rpc Predict(SeldonMessage) returns (SeldonMessage) {};
rpc SendFeedback(Feedback) returns (SeldonMessage) {};
}
service Router {
rpc Route(SeldonMessage) returns (SeldonMessage) {};
rpc SendFeedback(Feedback) returns (SeldonMessage) {};
}
service Transformer {
rpc TransformInput(SeldonMessage) returns (SeldonMessage) {};
}
service OutputTransformer {
rpc TransformOutput(SeldonMessage) returns (SeldonMessage) {};
}
service Combiner {
rpc Aggregate(SeldonMessageList) returns (SeldonMessage) {};
}
service Seldon {
rpc Predict(SeldonMessage) returns (SeldonMessage) {};
rpc SendFeedback(Feedback) returns (SeldonMessage) {};
}
// [END Services]
================================================
FILE: notebooks/requirements.txt
================================================
matplotlib==3.0.3
grpcio==1.20.1
grpcio-tools==1.20.1
graphviz==0.10.1
================================================
FILE: notebooks/serving.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploying Various MNIST Models on Kubernetes \n",
"\n",
"Using:\n",
"\n",
" * kubeflow\n",
" * seldon-core\n",
" \n",
" \n",
"Follow the main README to setup kubeflow and seldon-core. This notebook will show various rolling deployments of the trained models\n",
"\n",
" * Single model\n",
" * AB Test between 2 models\n",
" * Multi-Armed Bandit over 3 models\n",
" \n",
"### Dependencies\n",
" \n",
" * Tensorflow\n",
" * grpcio package\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n",
"\n",
"Set kubectl to use the namespace where you installed kubeflow and seldon. In the README it is kubeflow."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl config set-context $(kubectl config current-context) --namespace=kubeflow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!make create_protos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python -m grpc.tools.protoc -I. --python_out=. --grpc_python_out=. ./proto/prediction.proto"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import utils\n",
"from visualizer import get_graph\n",
"mnist = utils.download_mnist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"**Ensure you have port forwarded the ambassador reverse proxy**\n",
"\n",
"```bash\n",
"kubectl port-forward $(kubectl get pods -n kubeflow -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n kubeflow 8002:80\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy Single Tensorflow Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"get_graph(\"../k8s_serving/serving_model.json\",'r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../k8s_serving/serving_model.json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl apply -f ../k8s_serving/serving_model.json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl get seldondeployments mnist-classifier -o jsonpath='{.status}'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utils.predict_rest_mnist(mnist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utils.predict_grpc_mnist(mnist)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Start load test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl label nodes $(kubectl get nodes -o jsonpath='{.items[0].metadata.name}') role=locust"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!helm install seldon-core-loadtesting --name loadtest \\\n",
" --namespace kubeflow \\\n",
" --repo https://storage.googleapis.com/seldon-charts \\\n",
" --set locust.script=mnist_rest_locust.py \\\n",
" --set locust.host=http://mnist-classifier:8000 \\\n",
" --set oauth.enabled=false \\\n",
" --set oauth.key=oauth-key \\\n",
" --set oauth.secret=oauth-secret \\\n",
" --set locust.hatchRate=1 \\\n",
" --set locust.clients=1 \\\n",
" --set loadtest.sendFeedback=1 \\\n",
" --set locust.minWait=0 \\\n",
" --set locust.maxWait=0 \\\n",
" --set replicaCount=1 \\\n",
" --set data.size=784\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rolling update to AB Test\n",
" Run an AB Test between 2 models:\n",
" * Tensorflow neural network model\n",
" * Scikit-learn random forest.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"get_graph(\"../k8s_serving/ab_test_sklearn_tensorflow.json\",'r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../k8s_serving/ab_test_sklearn_tensorflow.json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl apply -f ../k8s_serving/ab_test_sklearn_tensorflow.json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl get seldondeployments mnist-classifier -o jsonpath='{.status}'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utils.predict_rest_mnist(mnist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utils.evaluate_abtest(mnist,100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rolling Update to Multi-Armed Bandit\n",
"Run a epsilon-greey multi-armed bandit over 3 models:\n",
" * Tensorflow neural network model\n",
" * Scikit-learn random forest model\n",
" * R least-squares model\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"get_graph(\"../k8s_serving/epsilon_greedy_3way.json\",'r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../k8s_serving/epsilon_greedy_3way.json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl apply -f ../k8s_serving/epsilon_greedy_3way.json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl get seldondeployments mnist-classifier -o jsonpath='{.status}'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utils.predict_rest_mnist(mnist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utils.evaluate_egreedy(mnist,100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
================================================
FILE: notebooks/training.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train Various Models on MNIST using kubeflow and seldon-core\n",
"\n",
"Using:\n",
"\n",
" * kubeflow\n",
" * seldon-core\n",
" \n",
"The example will be the MNIST handwriiten digit classification task.\n",
"\n",
"![MNIST](mnist.png \"MNIST Digits\")\n",
"\n",
"### Dependencies\n",
"\n",
" * Argo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!kubectl config set-context $(kubectl config current-context) --namespace=kubeflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tensorflow Model\n",
" A simple neural network in Tensorflow."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training\n",
" * Create image from source\n",
" * Run training\n",
" \n",
"\n",
"Run with:\n",
" * ``` -p build-push-image=true``` to build image and push to repo, needed extra params:\n",
" * ``` -p version=<version>``` create ```<version>``` of model\n",
" * ``` -p github-user=<github-user>``` to download example-seldon source from ```<github-user>``` account\n",
" * ``` -p github-revision=<revision>``` to use the github branch ```<revision>```\n",
" * ``` -p docker-org=<docker-org>``` to use Docker repo ```<docker-org>``` to push image to. Needs docker credentials in secret as described in README."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../workflows/training-tf-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo submit ../workflows/training-tf-mnist-workflow.yaml -p tfjob-version-hack=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Runtime Image\n",
"\n",
"Run with:\n",
" * ``` -p build-push-image=true``` to build image and push to repo, needed extra params:\n",
" * ``` -p version=<version>``` create ```<version>``` of model\n",
" * ``` -p github-user=<github-user>``` to download example-seldon source from ```<github-user>``` account\n",
" * ``` -p github-revision=<revision>``` to use the github branch ```<revision>```\n",
" * ``` -p docker-org=<docker-org>``` to use Docker user ```<docker-org>``` to push image to. Needs docker credentials in secret as described in README.\n",
" * ``` -p deploy-model=true``` to deploy model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../workflows/serving-tf-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo submit ../workflows/serving-tf-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sklearn Model\n",
"A Random forest in sklearn."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training\n",
"\n",
" * For options see above Tensorflow example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../workflows/training-sk-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo submit ../workflows/training-sk-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Runtime Image\n",
" * For options see above Tensorflow example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../workflows/serving-sk-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo submit ../workflows/serving-sk-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# R Model\n",
"A partial least squares model in R."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training\n",
"\n",
" * For options see above Tensorflow example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../workflows/training-r-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo submit ../workflows/training-r-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Runtime Image\n",
" * For options see above Tensorflow example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pygmentize ../workflows/serving-r-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo submit ../workflows/serving-r-mnist-workflow.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!argo list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
================================================
FILE: notebooks/utils.py
================================================
import requests
from requests.auth import HTTPBasicAuth
from random import randint,random
from proto import prediction_pb2
from proto import prediction_pb2_grpc
import grpc
import json
from visualizer import get_graph
from matplotlib import pyplot as plt
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from google.protobuf.json_format import MessageToJson
AMBASSADOR_API_IP="localhost:8002"
def rest_request(deploymentName,request):
response = requests.post(
"http://"+AMBASSADOR_API_IP+"/seldon/"+deploymentName+"/api/v0.1/predictions",
json=request)
j = response.json()
return j
def rest_request_auth(deploymentName,data,username,password):
payload = {"data":{"ndarray":data.tolist()}}
response = requests.post(
"http://"+AMBASSADOR_API_IP+"/seldon/"+deploymentName+"/api/v0.1/predictions",
json=payload,
auth=HTTPBasicAuth(username, password))
print(response.status_code)
return response.json()
def grpc_request(deploymentName,data):
datadef = prediction_pb2.DefaultData(
names = ["a","b"],
tensor = prediction_pb2.Tensor(
shape = [1,784],
values = data
)
)
request = prediction_pb2.SeldonMessage(data = datadef)
channel = grpc.insecure_channel(AMBASSADOR_API_IP)
stub = prediction_pb2_grpc.SeldonStub(channel)
metadata = [('seldon',deploymentName)]
response = stub.Predict(request=request,metadata=metadata)
return response
def send_feedback_rest(deploymentName,request,response,reward):
feedback = {
"request": request,
"response": response,
"reward": reward
}
ret = requests.post(
"http://"+AMBASSADOR_API_IP+"/seldon/"+deploymentName+"/api/v0.1/feedback",
json=feedback)
return ret.text
def gen_image(arr):
two_d = (np.reshape(arr, (28, 28)) * 255).astype(np.uint8)
plt.imshow(two_d,cmap=plt.cm.gray_r, interpolation='nearest')
return plt
def download_mnist():
return input_data.read_data_sets("MNIST_data/", one_hot = True)
def predict_rest_mnist(mnist):
batch_xs, batch_ys = mnist.train.next_batch(1)
chosen=0
gen_image(batch_xs[chosen]).show()
data = batch_xs[chosen].reshape((1,784))
features = ["X"+str(i+1) for i in range (0,784)]
request = {"data":{"names":features,"ndarray":data.tolist()}}
predictions = rest_request("mnist-classifier",request)
print(json.dumps(predictions,indent=2))
#print("Route:"+json.dumps(predictions["meta"]["routing"],indent=2))
fpreds = [ '%.2f' % elem for elem in predictions["data"]["ndarray"][0] ]
m = dict(zip(predictions["data"]["names"],fpreds))
print("Returned probabilities")
print(json.dumps(m,indent=2))
def predict_grpc_mnist(mnist):
batch_xs, batch_ys = mnist.train.next_batch(1)
chosen=0
gen_image(batch_xs[chosen]).show()
data = batch_xs[chosen].reshape((784))
resp = grpc_request("mnist-classifier",data)
predictions = MessageToJson(resp)
predictions = json.loads(predictions)
print(json.dumps(predictions,indent=2))
fpreds = [ '%.2f' % elem for elem in predictions["data"]["tensor"]["values"] ]
m = dict(zip(predictions["data"]["names"],fpreds))
print("Returned probabilities")
print(json.dumps(m,indent=2))
def evaluate_abtest(mnist,sz=100):
batch_xs, batch_ys = mnist.train.next_batch(sz)
routes_history = []
for idx in range(sz):
if idx % 10 == 0:
print("{}/{}".format(idx,sz))
data = batch_xs[idx].reshape((1,784))
request = {"data":{"ndarray":data.tolist()}}
response = rest_request("mnist-classifier",request)
route = response.get("meta").get("routing").get("random-ab-test")
routes_history.append(route)
plt.figure(figsize=(15,6))
ax = plt.scatter(range(len(routes_history)),routes_history)
ax.axes.xaxis.set_label_text("Incoming Requests over Time")
ax.axes.yaxis.set_label_text("Selected Branch")
plt.yticks([0,1,2])
_ = plt.title("Branch Chosen for Incoming Requests")
def evaluate_egreedy(mnist,sz=100):
score = [0.0,0.0,0.0]
sz = 100
batch_xs, batch_ys = mnist.train.next_batch(sz)
routes_history = []
for idx in range(sz):
if idx % 10 == 0:
print("{}/{}".format(idx,sz))
data = batch_xs[idx].reshape((1,784))
request = {"data":{"ndarray":data.tolist()}}
response = rest_request("mnist-classifier",request)
route = response.get("meta").get("routing").get("eg-router")
proba = response["data"]["ndarray"][0]
predicted = proba.index(max(proba))
correct = np.argmax(batch_ys[idx])
if predicted == correct:
score[route] = score[route] + 1
send_feedback_rest("mnist-classifier",request,response,reward=1)
else:
send_feedback_rest("mnist-classifier",request,response,reward=0)
routes_history.append(route)
plt.figure(figsize=(15,6))
ax = plt.scatter(range(len(routes_history)),routes_history)
ax.axes.xaxis.set_label_text("Incoming Requests over Time")
ax.axes.yaxis.set_label_text("Selected Branch")
plt.yticks([0,1,2])
_ = plt.title("Branch Chosen for Incoming Requests")
print(score)
================================================
FILE: notebooks/visualizer.py
================================================
import graphviz
import json
def _populate_graph(dot, root, suffix=''):
name = root.get("name")
id = name+suffix
if root.get("implementation"):
dot.node(id, label=name, shape="box", style="filled", color="lightgrey")
else:
dot.node(id, label=name, shape="box")
endpoint_type = root.get("endpoint",{}).get("type")
if endpoint_type is not None:
dot.node(id+'endpoint', label=endpoint_type)
dot.edge(id,id+'endpoint')
for child in root.get("children",[]):
child_id = _populate_graph(dot,child)
dot.edge(id, child_id)
return id
def get_graph(filename,predictor=0):
deployment = json.load(open(filename,'r'))
predictors = deployment.get("spec").get("predictors")
dot = graphviz.Digraph()
with dot.subgraph(name="cluster_0") as pdot:
graph = predictors[0].get("graph")
_populate_graph(pdot, graph, suffix='0')
pdot.attr(label="predictor")
if len(predictors)>1:
with dot.subgraph(name="cluster_1") as cdot:
graph = predictors[1].get("graph")
_populate_graph(cdot, graph, suffix='1')
cdot.attr(label="canary")
return dot
================================================
FILE: scripts/README.md
================================================
# Create MNIST Demo
1. You will need all prerequisites (gcloud, kubectl, ks) in your path.
1. Copy `env-example.sh` to `env.sh` and edit with your own settings
1. run `create_demo.sh`
# Delete Demo
1. run `delete-demo.sh` - this will delete the GCP resources except the Filestore disk. You will need to delete this manually at present.
================================================
FILE: scripts/create_demo.sh
================================================
#!/usr/bin/env bash
set -o nounset
set -o errexit
set -o pipefail
create_src() {
mkdir -p ${KUBEFLOW_SRC}
cd ${KUBEFLOW_SRC}
curl https://raw.githubusercontent.com/kubeflow/kubeflow/${KUBEFLOW_TAG}/scripts/download.sh | bash
}
launch_kubeflow() {
KUBEFLOW_REPO=${KUBEFLOW_SRC} ${KUBEFLOW_SRC}/scripts/kfctl.sh init ${KFAPP} --platform gcp --project ${PROJECT}
cd ${KFAPP}
${KUBEFLOW_SRC}/scripts/kfctl.sh generate platform
${KUBEFLOW_SRC}/scripts/kfctl.sh apply platform
${KUBEFLOW_SRC}/scripts/kfctl.sh generate k8s
${KUBEFLOW_SRC}/scripts/kfctl.sh apply k8s
}
launch_seldon() {
cd ${KUBEFLOW_SRC}/${KFAPP}/ks_app
ks pkg install kubeflow/seldon
ks generate seldon seldon
ks apply default -c seldon
}
add_helm() {
kubectl -n kube-system create sa tiller
kubectl create clusterrolebinding tiller --clusterrole cluster-admin --serviceaccount=kube-system:tiller
helm init --service-account tiller
kubectl rollout status deploy/tiller-deploy -n kube-system
}
add_nfs_disk() {
set +e
FSADDR=$(gcloud beta filestore instances describe ${FS} --project=${PROJECT} --location=${ZONE} --format="value(networks.ipAddresses[0])")
if [ -z "$FSADDR" ]; then
echo "Creating filestore NFS volume"
gcloud beta filestore instances create ${FS} --project=${PROJECT} --location=${ZONE} --tier=STANDARD --file-share=name="volumes",capacity=1TB --network=name="default",reserved-ip-range="10.0.0.0/29"
fi
set -e
FSADDR=$(gcloud beta filestore instances describe ${FS} --project=${PROJECT} --location=${ZONE} --format="value(networks.ipAddresses[0])")
helm install stable/nfs-client-provisioner --name nfs-cp --set nfs.server=${FSADDR} --set nfs.path=/volumes
kubectl rollout status deploy/nfs-cp-nfs-client-provisioner -n kubeflow
kubectl apply -f ${STARTUP_DIR}/nfs-pvc.yaml -n kubeflow
}
add_argo_clusterrole() {
kubectl create clusterrolebinding my-cluster-admin-binding --clusterrole=cluster-admin --user=$(gcloud info --format="value(config.account)")
kubectl create clusterrolebinding default-admin2 --clusterrole=cluster-admin --serviceaccount=kubeflow:default
}
add_seldon_analytics() {
helm install seldon-core-analytics --name seldon-core-analytics --set grafana_prom_admin_password=password --set persistence.enabled=false --repo https://storage.googleapis.com/seldon-charts --namespace kubeflow
}
if [ ! -f env.sh ]; then
echo "Create env.sh by copying env-example.sh"
fi
source env.sh
create_src
launch_kubeflow
launch_seldon
add_helm
add_nfs_disk
add_argo_clusterrole
add_seldon_analytics
================================================
FILE: scripts/delete-demo.sh
================================================
#!/usr/bin/env bash
set -o nounset
set -o errexit
set -o pipefail
if [ ! -f env.sh ]; then
echo "Create env.sh by copying env-example.sh"
fi
source env.sh
cd ${KUBEFLOW_SRC}/${KFAPP}
${KUBEFLOW_SRC}/scripts/kfctl.sh delete all
================================================
FILE: scripts/env-example.sh
================================================
STARTUP_DIR="$( cd "$( dirname "$0" )" && pwd )"
KFAPP=my-kubeflow
PROJECT=seldon-demos
KUBEFLOW_SRC=${STARTUP_DIR}/kubeflow_src
FS=mnist-data
ZONE=europe-west1-b
# Next two lines are set from values created as discussed in https://www.kubeflow.org/docs/started/getting-started-gke/
export CLIENT_ID=<your-client-id>
export CLIENT_SECRET=<your-secret>
export KUBEFLOW_TAG=v0.5.1
================================================
FILE: scripts/nfs-pvc.yaml
================================================
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: nfs-1
spec:
accessModes:
- ReadWriteMany
storageClassName: nfs-client
resources:
requests:
storage: 30Gi
================================================
FILE: scripts/port-forwards.sh
================================================
#Argo
kubectl port-forward $(kubectl get pods -n kubeflow -l app=argo-ui -o jsonpath='{.items[0].metadata.name}') -n kubeflow 8001:8001 &
#Seldon Grafana
kubectl port-forward $(kubectl get pods -n kubeflow -l app=grafana-prom-server -o jsonpath='{.items[0].metadata.name}') -n kubeflow 3000:3000 &
#Ambassador reverse proxy
kubectl port-forward $(kubectl get pods -n kubeflow -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n kubeflow 8002:80 &
#Ambassador admin
kubectl port-forward $(kubectl get pods -n kubeflow -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n kubeflow 8877:8877 &
================================================
FILE: scripts/watch-mnist.sh
================================================
watch kubectl get pods -l seldon-app=mnist-classifier
================================================
FILE: workflows/serving-r-mnist-workflow.yaml
================================================
# This example demonstrates the use of a git repo as a hard-wired
# input artifact. The argo repo is cloned to its target destination
# at '/src' for the main container to consume.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: seldon-r-deploy-
spec:
entrypoint: workflow
arguments:
parameters:
- name: version
value: 0.1
- name: github-user
value: kubeflow
- name: github-revision
value: master
- name: docker-org
value: index.docker.io/seldonio
- name: build-push-image
value: false
- name: deploy-model
value: false
volumes:
- name: docker-config
secret:
secretName: docker-config # name of an existing k8s secret
volumeClaimTemplates:
- metadata:
name: workspace
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 0.5Gi
templates:
- name: workflow
steps:
- - name: get-source
template: get-source-code
- - name: build-push
template: build-and-push
when: "{{workflow.parameters.build-push-image}} == true"
- - name: serve
template: seldon
when: "{{workflow.parameters.deploy-model}} == true"
- name: get-source-code
inputs:
artifacts:
- name: argo-source
path: /src/example-seldon
git:
repo: https://github.com/{{workflow.parameters.github-user}}/example-seldon.git
revision: "{{workflow.parameters.github-revision}}"
container:
image: alpine:latest
command: [sh, -c]
args: ["cp /src/example-seldon/models/r_mnist/runtime/* /workspace/; ls /workspace/"]
volumeMounts:
- name: workspace
mountPath: /workspace
- name: build-and-push
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile","Dockerfile","--destination","{{workflow.parameters.docker-org}}/rmnistclassifier_runtime:{{workflow.parameters.version}}"]
workingDir: /src/example-seldon/models/r_mnist/runtime/
volumeMounts:
- name: docker-config
mountPath: "/root/.docker/"
- name: workspace
mountPath: /workspace
- name: seldon
resource: #indicates that this is a resource template
action: apply #can be any kubectl action (e.g. create, delete, apply, patch)
#successCondition: ?
manifest: | #put your kubernetes spec here
apiVersion: "machinelearning.seldon.io/v1alpha2"
kind: "SeldonDeployment"
metadata:
labels:
app: "seldon"
name: "mnist-classifier"
spec:
annotations:
deployment_version: "v1"
project_name: "MNIST Example"
name: "mnist-classifier"
predictors:
-
annotations:
predictor_version: "v1"
componentSpecs:
-
spec:
containers:
-
image: "{{workflow.parameters.docker-org}}/rmnistclassifier_runtime:{{workflow.parameters.version}}"
imagePullPolicy: "Always"
name: "mnist-classifier"
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
terminationGracePeriodSeconds: 1
volumes:
-
name: "persistent-storage"
volumeSource:
persistentVolumeClaim:
claimName: "nfs-1"
graph:
children: []
endpoint:
type: "REST"
name: "mnist-classifier"
type: "MODEL"
name: "mnist-classifier"
replicas: 1
================================================
FILE: workflows/serving-sk-mnist-workflow.yaml
================================================
# This example demonstrates the use of a git repo as a hard-wired
# input artifact. The argo repo is cloned to its target destination
# at '/src' for the main container to consume.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: seldon-sk-deploy-
spec:
entrypoint: workflow
arguments:
parameters:
- name: version
value: 0.1
- name: github-user
value: kubeflow
- name: github-revision
value: master
- name: docker-org
value: index.docker.io/seldonio
- name: build-push-image
value: false
- name: deploy-model
value: false
volumes:
- name: docker-config
secret:
secretName: docker-config # name of an existing k8s secret
volumeClaimTemplates:
- metadata:
name: workspace
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 0.5Gi
templates:
- name: workflow
steps:
- - name: get-source
template: get-source-code
- - name: build-push
template: build-and-push
when: "{{workflow.parameters.build-push-image}} == true"
- - name: serve
template: seldon
when: "{{workflow.parameters.deploy-model}} == true"
- name: get-source-code
inputs:
artifacts:
- name: argo-source
path: /src/example-seldon
git:
repo: https://github.com/{{workflow.parameters.github-user}}/example-seldon.git
revision: "{{workflow.parameters.github-revision}}"
container:
image: alpine:latest
command: [sh, -c]
args: ["cp /src/example-seldon/models/sk_mnist/runtime/* /workspace/; ls /workspace/"]
volumeMounts:
- name: workspace
mountPath: /workspace
- name: build-and-push
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile","Dockerfile","--destination","{{workflow.parameters.docker-org}}/skmnistclassifier_runtime:{{workflow.parameters.version}}"]
workingDir: /src/example-seldon/models/sk_mnist/runtime/
volumeMounts:
- name: docker-config
mountPath: "/root/.docker/"
- name: workspace
mountPath: /workspace
- name: seldon
resource: #indicates that this is a resource template
action: apply #can be any kubectl action (e.g. create, delete, apply, patch)
#successCondition: ?
manifest: | #put your kubernetes spec here
apiVersion: "machinelearning.seldon.io/v1alpha2"
kind: "SeldonDeployment"
metadata:
labels:
app: "seldon"
name: "mnist-classifier"
spec:
annotations:
deployment_version: "v1"
project_name: "MNIST Example"
name: "mnist-classifier"
predictors:
-
annotations:
predictor_version: "v1"
componentSpecs:
-
spec:
containers:
-
image: "{{workflow.parameters.docker-org}}/skmnistclassifier_runtime:{{workflow.parameters.version}}"
imagePullPolicy: "Always"
name: "mnist-classifier"
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
terminationGracePeriodSeconds: 1
volumes:
-
name: "persistent-storage"
volumeSource:
persistentVolumeClaim:
claimName: "nfs-1"
graph:
children: []
endpoint:
type: "REST"
name: "mnist-classifier"
type: "MODEL"
name: "mnist-classifier"
replicas: 1
================================================
FILE: workflows/serving-tf-mnist-workflow.md
================================================
# Example Argo Workflow to dockerize runtime model and deploy it for serving
Comments on the [serving-tf-mnist-workflow.yaml](serving-tf-mnist-workflow.yaml)
## Workflow Summary
To serve our runtime model we create:
* [```models/tf_mnist/runtime/Dockerfile```](../models/tf_mnist/runtime/Dockerfile) to wrap model using the seldon-core python wrapper.
* An Argo workflow to:
* Wrap the runtime model, builds a docker container for it and optionally push it to your repo
* Optionally starts a seldon deployment that will run and expose your model
## Workflow parameters
* version
* The version tag for the Docker image
* github-user
* The github user to use to clone this repo/fork
* github-revision
* The github revision to use for cloning the repo (can be a branch name)
* docker-org
* The Docker host and org/user/project to use when pushing an image to the registry
* build-push-image
* Whether to build and push the image to docker registry (true/false)
* deploy-model
* Whether to start a seldon deployment to run and expose your model (true/false)
================================================
FILE: workflows/serving-tf-mnist-workflow.yaml
================================================
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: seldon-tf-deploy-
spec:
entrypoint: workflow
arguments:
parameters:
- name: version
value: 0.1
- name: github-user
value: kubeflow
- name: github-revision
value: master
- name: docker-org
value: index.docker.io/seldonio
- name: build-push-image
value: false
- name: deploy-model
value: false
volumes:
- name: docker-config
secret:
secretName: docker-config # name of an existing k8s secret
volumeClaimTemplates:
- metadata:
name: workspace
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 0.5Gi
templates:
- name: workflow
steps:
- - name: get-source
template: get-source-code
- - name: build-push
template: build-and-push
when: "{{workflow.parameters.build-push-image}} == true"
- - name: serve
template: seldon
when: "{{workflow.parameters.deploy-model}} == true"
- name: get-source-code
inputs:
artifacts:
- name: argo-source
path: /src/example-seldon
git:
repo: https://github.com/{{workflow.parameters.github-user}}/example-seldon.git
revision: "{{workflow.parameters.github-revision}}"
container:
image: alpine:latest
command: [sh, -c]
args: ["cp /src/example-seldon/models/tf_mnist/runtime/* /workspace/; ls /workspace/"]
volumeMounts:
- name: workspace
mountPath: /workspace
- name: build-and-push
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile","Dockerfile","--destination","{{workflow.parameters.docker-org}}/deepmnistclassifier_runtime:{{workflow.parameters.version}}"]
workingDir: /src/example-seldon/models/tf_mnist/runtime/
volumeMounts:
- name: docker-config
mountPath: "/root/.docker/"
- name: workspace
mountPath: /workspace
- name: seldon
resource: #indicates that this is a resource template
action: apply #can be any kubectl action (e.g. create, delete, apply, patch)
#successCondition: ?
manifest: | #put your kubernetes spec here
apiVersion: "machinelearning.seldon.io/v1alpha2"
kind: "SeldonDeployment"
metadata:
labels:
app: "seldon"
name: "mnist-classifier"
spec:
annotations:
deployment_version: "v1"
project_name: "MNIST Example"
name: "mnist-classifier"
predictors:
-
annotations:
predictor_version: "v1"
componentSpecs:
-
spec:
containers:
-
image: "{{workflow.parameters.docker-org}}/deepmnistclassifier_runtime:{{workflow.parameters.version}}"
imagePullPolicy: "Always"
name: "mnist-classifier"
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
terminationGracePeriodSeconds: 1
volumes:
-
name: "persistent-storage"
volumeSource:
persistentVolumeClaim:
claimName: "nfs-1"
graph:
children: []
endpoint:
type: "REST"
name: "mnist-classifier"
type: "MODEL"
name: "mnist-classifier"
replicas: 1
================================================
FILE: workflows/training-r-mnist-workflow.yaml
================================================
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: kubeflow-r-train-
spec:
entrypoint: workflow
arguments:
parameters:
- name: version
value: 0.1
- name: github-user
value: kubeflow
- name: github-revision
value: master
- name: docker-org
value: seldonio
- name: build-push-image
value: false
volumes:
- name: docker-config
secret:
secretName: docker-config # name of an existing k8s secret
volumeClaimTemplates:
- metadata:
name: workspace
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 0.5Gi
templates:
- name: workflow
steps:
- - name: get-source
template: get-source-code
- - name: build-push
template: build-and-push
when: "{{workflow.parameters.build-push-image}} == true"
- - name: train
template: tfjob
- name: get-source-code
inputs:
artifacts:
- name: argo-source
path: /src/example-seldon
git:
repo: https://github.com/{{workflow.parameters.github-user}}/example-seldon.git
revision: "{{workflow.parameters.github-revision}}"
container:
image: alpine:latest
command: [sh, -c]
args: ["cp /src/example-seldon/models/r_mnist/train/* /workspace/; ls /workspace/"]
volumeMounts:
- name: workspace
mountPath: /workspace
- name: build-and-push
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile","Dockerfile","--destination","{{workflow.parameters.docker-org}}/rmnistclassifier_trainer:{{workflow.parameters.version}}"]
workingDir: /src/example-seldon/models/r_mnist/train/
volumeMounts:
- name: docker-config
mountPath: "/root/.docker/"
- name: workspace
mountPath: /workspace
- name: tfjob
resource: #indicates that this is a resource template
action: create #can be any kubectl action (e.g. create, delete, apply, patch)
successCondition: status.succeeded == 1
manifest: | #put your kubernetes spec here
apiVersion: "batch/v1"
kind: "Job"
metadata:
name: "r-train"
ownerReferences:
- apiVersion: argoproj.io/v1alpha1
kind: Workflow
controller: true
name: {{workflow.name}}
uid: {{workflow.uid}}
spec:
template:
metadata:
name: "r-train"
spec:
containers:
-
image: "{{workflow.parameters.docker-org}}/rmnistclassifier_trainer:{{workflow.parameters.version}}"
name: "r-train"
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
restartPolicy: "Never"
volumes:
-
name: "persistent-storage"
persistentVolumeClaim:
claimName: "nfs-1"
================================================
FILE: workflows/training-sk-mnist-workflow.yaml
================================================
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: kubeflow-sk-train-
spec:
entrypoint: workflow
arguments:
parameters:
- name: version
value: 0.2
- name: github-user
value: kubeflow
- name: github-revision
value: master
- name: docker-org
value: index.docker.io/seldonio
- name: build-push-image
value: false
volumes:
- name: docker-config
secret:
secretName: docker-config # name of an existing k8s secret
volumeClaimTemplates:
- metadata:
name: workspace
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 0.5Gi
templates:
- name: workflow
steps:
- - name: get-source
template: get-source-code
- - name: build-push
template: build-and-push
when: "{{workflow.parameters.build-push-image}} == true"
- - name: train
template: tfjob
- name: get-source-code
inputs:
artifacts:
- name: argo-source
path: /src/example-seldon
git:
repo: https://github.com/{{workflow.parameters.github-user}}/example-seldon.git
revision: "{{workflow.parameters.github-revision}}"
container:
image: alpine:latest
command: [sh, -c]
args: ["cp /src/example-seldon/models/sk_mnist/train/* /workspace/; ls /workspace/"]
volumeMounts:
- name: workspace
mountPath: /workspace
- name: build-and-push
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile","Dockerfile","--destination","{{workflow.parameters.docker-org}}/skmnistclassifier_trainer:{{workflow.parameters.version}}"]
workingDir: /src/example-seldon/models/sk_mnist/train/
volumeMounts:
- name: docker-config
mountPath: "/root/.docker/"
- name: workspace
mountPath: /workspace
- name: tfjob
resource: #indicates that this is a resource template
action: create #can be any kubectl action (e.g. create, delete, apply, patch)
successCondition: status.succeeded == 1
manifest: | #put your kubernetes spec here
apiVersion: "batch/v1"
kind: "Job"
metadata:
name: "sk-train"
ownerReferences:
- apiVersion: argoproj.io/v1alpha1
kind: Workflow
controller: true
name: {{workflow.name}}
uid: {{workflow.uid}}
spec:
template:
metadata:
name: "sk-train"
spec:
containers:
-
image: "{{workflow.parameters.docker-org}}/skmnistclassifier_trainer:{{workflow.parameters.version}}"
name: "sk-train"
imagePullPolicy: Always
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
restartPolicy: "Never"
volumes:
-
name: "persistent-storage"
persistentVolumeClaim:
claimName: "nfs-1"
================================================
FILE: workflows/training-tf-mnist-workflow.md
================================================
# Example Argo Workflow to dockerize and Train Model
Comments on the [training-tf-mnist-workflow.yaml](training-tf-mnist-workflow.yaml)
## Workflow summary
To dockerize our model training and run it we create:
* [```models/tf_mnist/train/build_and_push.sh```](../models/tf_mnist/train/build_and_push.sh) that will build an image for our Tensorflow training and push to our repo.
* An Argo workflow [```workflows/training-tf-mnist-workflow.yaml```](training-tf-mnist-workflow.yaml) is created which:
* Clones the project from github
* Runs the build and push script (using DockerInDocker)
* Starts a kubeflow TfJob to train the model and save the results to the persistent volume
## Workflow parameters
* version
* The version tag for the Docker image
* github-user
* The github user/org for which to clone this repo/fork
* github-revision
* The github revision to use for cloning the repo (can be a branch name)
* docker-org
* The Docker host and org/user/project to use when pushing an image to the registry
* tfjob-version-hack
* A temporary random integer for the tfjob ID
* build-push-image
* Whether to build and push the image to docker registry (true/false)
## Setup For Pushing Images
**To push to your own repo the Docker images you will need to setup your docker credentials as a Kubernetes secret containing a [config.json](https://www.projectatomic.io/blog/2016/03/docker-credentials-store/). To do this you can find your docker home (typically ~/.docker) and run `kubectl create secret generic docker-config --from-file=config.json=${DOCKERHOME}/config.json --type=kubernetes.io/config` to [create a secret](https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/#registry-secret-existing-credentials).**
================================================
FILE: workflows/training-tf-mnist-workflow.yaml
================================================
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: kubeflow-tf-train-
spec:
entrypoint: workflow
arguments:
parameters:
- name: version
value: 0.1
- name: github-user
value: kubeflow
- name: github-revision
value: master
- name: docker-org
value: index.docker.io/seldonio
- name: tfjob-version-hack
value: 1
- name: build-push-image
value: false
volumes:
- name: docker-config
secret:
secretName: docker-config # name of an existing k8s secret
volumeClaimTemplates:
- metadata:
name: workspace
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 0.5Gi
templates:
- name: workflow
steps:
- - name: get-source
template: get-source-code
- - name: build-push
template: build-and-push
when: "{{workflow.parameters.build-push-image}} == true"
- - name: train
template: tfjob
- name: get-source-code
inputs:
artifacts:
- name: argo-source
path: /src/example-seldon
git:
repo: https://github.com/{{workflow.parameters.github-user}}/example-seldon.git
revision: "{{workflow.parameters.github-revision}}"
container:
image: alpine:latest
command: [sh, -c]
args: ["cp /src/example-seldon/models/tf_mnist/train/* /workspace/; ls /workspace/"]
volumeMounts:
- name: workspace
mountPath: /workspace
- name: build-and-push
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile","Dockerfile","--destination","{{workflow.parameters.docker-org}}/deepmnistclassifier_trainer:{{workflow.parameters.version}}"]
workingDir: /src/example-seldon/models/tf_mnist/train/
volumeMounts:
- name: docker-config
mountPath: "/root/.docker/"
- name: workspace
mountPath: /workspace
- name: tfjob
resource: #indicates that this is a resource template
action: create #can be any kubectl action (e.g. create, delete, apply, patch)
#successCondition: status.tfReplicaStatuses.Worker.succeeded == 1
#successCondition: status.conditions.type == Succeeded
successCondition: status.replicaStatuses.Worker.succeeded == 1
manifest: | #put your kubernetes spec here
apiVersion: "kubeflow.org/v1beta1"
kind: "TFJob"
metadata:
name: mnist-train-{{workflow.parameters.tfjob-version-hack}}
ownerReferences:
- apiVersion: argoproj.io/v1alpha1
kind: Workflow
controller: true
name: {{workflow.name}}
uid: {{workflow.uid}}
spec:
tfReplicaSpecs:
Worker:
replicas: 1
template:
spec:
containers:
-
image: "{{workflow.parameters.docker-org}}/deepmnistclassifier_trainer:{{workflow.parameters.version}}"
name: "tensorflow"
volumeMounts:
-
mountPath: "/data"
name: "persistent-storage"
restartPolicy: "OnFailure"
volumes:
-
name: "persistent-storage"
persistentVolumeClaim:
claimName: "nfs-1"
tfReplicaType: "MASTER"
gitextract_39vdm35i/
├── .gitignore
├── LICENSE
├── OWNERS
├── README.md
├── VERSION
├── k8s_serving/
│ ├── ab_test_sklearn_tensorflow.json
│ ├── ambassador-auth-service-config.yaml
│ ├── ambassador-auth-service-setup.yaml
│ ├── epsilon_greedy.json
│ ├── epsilon_greedy_3way.json
│ ├── serving_model.json
│ ├── serving_r_model.json
│ └── serving_sk_model.json
├── k8s_train/
│ ├── sklearn_training_job.yaml
│ └── tfJob.json
├── models/
│ ├── r_mnist/
│ │ ├── runtime/
│ │ │ ├── Dockerfile
│ │ │ ├── Makefile
│ │ │ ├── install.R
│ │ │ └── mnist.R
│ │ └── train/
│ │ ├── Dockerfile
│ │ ├── Makefile
│ │ ├── get_data.sh
│ │ ├── install.R
│ │ ├── train.R
│ │ └── train.sh
│ ├── sk_mnist/
│ │ ├── runtime/
│ │ │ ├── Dockerfile
│ │ │ ├── Makefile
│ │ │ ├── SkMnist.py
│ │ │ ├── contract.json
│ │ │ └── requirements.txt
│ │ └── train/
│ │ ├── Dockerfile
│ │ ├── Makefile
│ │ ├── create_model.py
│ │ ├── requirements.txt
│ │ └── train.sh
│ └── tf_mnist/
│ ├── runtime/
│ │ ├── DeepMnist.py
│ │ ├── Dockerfile
│ │ ├── Makefile
│ │ ├── contract.json
│ │ └── requirements.txt
│ └── train/
│ ├── Dockerfile
│ ├── Makefile
│ └── create_model.py
├── nfs.md
├── notebooks/
│ ├── Makefile
│ ├── __init__.py
│ ├── create-protos.sh
│ ├── proto/
│ │ ├── __init__.py
│ │ └── prediction.proto
│ ├── requirements.txt
│ ├── serving.ipynb
│ ├── training.ipynb
│ ├── utils.py
│ └── visualizer.py
├── scripts/
│ ├── README.md
│ ├── create_demo.sh
│ ├── delete-demo.sh
│ ├── env-example.sh
│ ├── nfs-pvc.yaml
│ ├── port-forwards.sh
│ └── watch-mnist.sh
└── workflows/
├── serving-r-mnist-workflow.yaml
├── serving-sk-mnist-workflow.yaml
├── serving-tf-mnist-workflow.md
├── serving-tf-mnist-workflow.yaml
├── training-r-mnist-workflow.yaml
├── training-sk-mnist-workflow.yaml
├── training-tf-mnist-workflow.md
└── training-tf-mnist-workflow.yaml
SYMBOL INDEX (18 symbols across 4 files)
FILE: models/sk_mnist/runtime/SkMnist.py
class SkMnist (line 3) | class SkMnist(object):
method __init__ (line 4) | def __init__(self):
method predict (line 8) | def predict(self,X,feature_names):
FILE: models/tf_mnist/runtime/DeepMnist.py
class DeepMnist (line 6) | class DeepMnist(object):
method __init__ (line 7) | def __init__(self):
method predict (line 17) | def predict(self,X,feature_names):
FILE: notebooks/utils.py
function rest_request (line 16) | def rest_request(deploymentName,request):
function rest_request_auth (line 23) | def rest_request_auth(deploymentName,data,username,password):
function grpc_request (line 32) | def grpc_request(deploymentName,data):
function send_feedback_rest (line 47) | def send_feedback_rest(deploymentName,request,response,reward):
function gen_image (line 59) | def gen_image(arr):
function download_mnist (line 64) | def download_mnist():
function predict_rest_mnist (line 68) | def predict_rest_mnist(mnist):
function predict_grpc_mnist (line 85) | def predict_grpc_mnist(mnist):
function evaluate_abtest (line 99) | def evaluate_abtest(mnist,sz=100):
function evaluate_egreedy (line 119) | def evaluate_egreedy(mnist,sz=100):
FILE: notebooks/visualizer.py
function _populate_graph (line 4) | def _populate_graph(dot, root, suffix=''):
function get_graph (line 20) | def get_graph(filename,predictor=0):
Condensed preview — 69 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (112K chars).
[
{
"path": ".gitignore",
"chars": 464,
"preview": "# build\n/target/\n/public\ncluster-manager/.m2/\n\n.ipynb_checkpoints\n\n# eclipse\n.classpath\n.settings/\n.project\n\n# Netbeans "
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "OWNERS",
"chars": 97,
"preview": "approvers:\n - cliveseldon\n - jinchihe\n - ryandawsonuk\nreviewers:\n - cliveseldon\n - jinchihe\n"
},
{
"path": "README.md",
"chars": 5909,
"preview": "## :warning: **kubeflow/example-seldon is not maintained**\n\nThis repository has been deprecated and [archived](https://g"
},
{
"path": "VERSION",
"chars": 3,
"preview": "0.1"
},
{
"path": "k8s_serving/ab_test_sklearn_tensorflow.json",
"chars": 2224,
"preview": "{\n \"apiVersion\": \"machinelearning.seldon.io/v1alpha2\",\n \"kind\": \"SeldonDeployment\",\n \"metadata\": {\n\t\"labels\": {"
},
{
"path": "k8s_serving/ambassador-auth-service-config.yaml",
"chars": 430,
"preview": "---\napiVersion: v1\nkind: Service\nmetadata:\n name: example-auth\n annotations:\n getambassador.io/config: |\n \n "
},
{
"path": "k8s_serving/ambassador-auth-service-setup.yaml",
"chars": 707,
"preview": "---\napiVersion: v1\nkind: Service\nmetadata:\n name: example-auth\nspec:\n type: ClusterIP\n selector:\n app: example-aut"
},
{
"path": "k8s_serving/epsilon_greedy.json",
"chars": 2395,
"preview": "{\n \"apiVersion\": \"machinelearning.seldon.io/v1alpha2\",\n \"kind\": \"SeldonDeployment\",\n \"metadata\": {\n\t\"labels\": {"
},
{
"path": "k8s_serving/epsilon_greedy_3way.json",
"chars": 2918,
"preview": "{\n \"apiVersion\": \"machinelearning.seldon.io/v1alpha2\",\n \"kind\": \"SeldonDeployment\",\n \"metadata\": {\n\t\"labels\": {"
},
{
"path": "k8s_serving/serving_model.json",
"chars": 2098,
"preview": "{\n \"apiVersion\": \"machinelearning.seldon.io/v1alpha2\",\n \"kind\": \"SeldonDeployment\",\n \"metadata\": {\n \"lab"
},
{
"path": "k8s_serving/serving_r_model.json",
"chars": 1997,
"preview": "{\n \"apiVersion\": \"machinelearning.seldon.io/v1alpha2\",\n \"kind\": \"SeldonDeployment\",\n \"metadata\": {\n \"lab"
},
{
"path": "k8s_serving/serving_sk_model.json",
"chars": 2000,
"preview": "{\n \"apiVersion\": \"machinelearning.seldon.io/v1alpha2\",\n \"kind\": \"SeldonDeployment\",\n \"metadata\": {\n \"lab"
},
{
"path": "k8s_train/sklearn_training_job.yaml",
"chars": 546,
"preview": "apiVersion: \"batch/v1\"\nkind: \"Job\"\nmetadata: \n name: \"sk-train\"\n namespace: \"default\"\nspec: \n template: \n metadata"
},
{
"path": "k8s_train/tfJob.json",
"chars": 1369,
"preview": "{\n \"apiVersion\": \"kubeflow.org/v1alpha1\",\n \"kind\": \"TFJob\",\n \"metadata\": {\n \"name\": \"mnist-train\",\n "
},
{
"path": "models/r_mnist/runtime/Dockerfile",
"chars": 654,
"preview": "FROM rocker/r-apt:bionic\n\nRUN apt-get update && \\\n apt-get install -y -qq \\\n \tr-cran-plumber \\\n \tr-cran-jsonlit"
},
{
"path": "models/r_mnist/runtime/Makefile",
"chars": 160,
"preview": "\nseldon_build_image_local:\n\tdocker build . -t seldonio/rmnistclassifier_runtime:0.2\n\nseldon_push_docker_hub:\n\tdocker pus"
},
{
"path": "models/r_mnist/runtime/install.R",
"chars": 25,
"preview": "install.packages('pls')\n\n"
},
{
"path": "models/r_mnist/runtime/mnist.R",
"chars": 496,
"preview": "library(methods)\n\npredict.mnist <- function(mnist,newdata=list()) {\n cn <- 1:784\n for (i in seq_along(cn)){cn[i] <- pa"
},
{
"path": "models/r_mnist/train/Dockerfile",
"chars": 392,
"preview": "FROM rocker/r-apt:bionic\n\nRUN apt-get update && \\\n apt-get install -y -qq \\\n \tr-cran-caret \\\n \tr-cran-pls \\\n "
},
{
"path": "models/r_mnist/train/Makefile",
"chars": 154,
"preview": "\n\nbuild_model:\n\tdocker build --force-rm=true -t seldonio/rmnistclassifier_trainer:0.1 .\n\npush_image:\n\tdocker push seldon"
},
{
"path": "models/r_mnist/train/get_data.sh",
"chars": 394,
"preview": "wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\nwget http://yann.lecun.com/exdb/mnist/train-labels-idx1"
},
{
"path": "models/r_mnist/train/install.R",
"chars": 108,
"preview": "install.packages('caret')\ninstall.packages('doParallel')\ninstall.packages('pls')\ninstall.packages('e1071')\n\n"
},
{
"path": "models/r_mnist/train/train.R",
"chars": 2372,
"preview": "library(caret)\nlibrary(doParallel)\n\n# Enable parallel processing.\ncl <- makeCluster(detectCores())\nregisterDoParallel(cl"
},
{
"path": "models/r_mnist/train/train.sh",
"chars": 212,
"preview": "#!/usr/bin/env bash\n\n# exit when any command fails\nset -e\n\nuntil mountpoint -q /data; do\n echo \"$(date) - waiting for"
},
{
"path": "models/sk_mnist/runtime/Dockerfile",
"chars": 320,
"preview": "FROM python:3.7-slim\nCOPY . /app\nWORKDIR /app\nRUN pip install -r requirements.txt\nEXPOSE 5000\n\n# Define environment vari"
},
{
"path": "models/sk_mnist/runtime/Makefile",
"chars": 163,
"preview": "\nseldon_build_image_local:\n\tdocker build . -t seldonio/skmnistclassifier_runtime:0.2\n\nseldon_push_docker_hub:\n\tdocker pu"
},
{
"path": "models/sk_mnist/runtime/SkMnist.py",
"chars": 328,
"preview": "from sklearn.externals import joblib\n\nclass SkMnist(object):\n def __init__(self):\n self.class_names = [\"class:"
},
{
"path": "models/sk_mnist/runtime/contract.json",
"chars": 279,
"preview": "{\n \"features\":[\n\t{\n\t \"name\":\"x\",\n\t \"dtype\":\"FLOAT\",\n\t \"ftype\":\"continuous\",\n\t \"range\":[0,1],\n\t \"repeat"
},
{
"path": "models/sk_mnist/runtime/requirements.txt",
"chars": 52,
"preview": "scipy>= 0.13.3\nscikit-learn>=0.18\nseldon-core>=0.2.5"
},
{
"path": "models/sk_mnist/train/Dockerfile",
"chars": 353,
"preview": "FROM python:3.7-slim\n\nRUN apt-get update -y\nRUN apt-get install -y python-pip python-dev build-essential\n\nCOPY /requirem"
},
{
"path": "models/sk_mnist/train/Makefile",
"chars": 155,
"preview": "\n\nbuild_model:\n\tdocker build --force-rm=true -t seldonio/skmnistclassifier_trainer:0.2 .\n\npush_image:\n\tdocker push seldo"
},
{
"path": "models/sk_mnist/train/create_model.py",
"chars": 2198,
"preview": "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn import datasets, metrics\nfrom sklearn.utils import shuf"
},
{
"path": "models/sk_mnist/train/requirements.txt",
"chars": 29,
"preview": "scipy\nscikit-learn>=0.18\nsix\n"
},
{
"path": "models/sk_mnist/train/train.sh",
"chars": 223,
"preview": "#!/usr/bin/env bash\n\n# exit when any command fails\nset -e\n\nuntil mountpoint -q /data; do\n echo \"$(date) - wainting fo"
},
{
"path": "models/tf_mnist/runtime/DeepMnist.py",
"chars": 810,
"preview": "import tensorflow as tf\nimport logging\nlogging.basicConfig(format='%(asctime)s.%(msecs)03d %(levelname)s {%(module)s} [%"
},
{
"path": "models/tf_mnist/runtime/Dockerfile",
"chars": 321,
"preview": "FROM python:3.7-slim\nCOPY . /app\nWORKDIR /app\nRUN pip install -r requirements.txt\nEXPOSE 5000\n\n# Define environment vari"
},
{
"path": "models/tf_mnist/runtime/Makefile",
"chars": 165,
"preview": "\nseldon_build_image_local:\n\tdocker build . -t seldonio/deepmnistclassifier_runtime:0.2\n\nseldon_push_docker_hub:\n\tdocker "
},
{
"path": "models/tf_mnist/runtime/contract.json",
"chars": 279,
"preview": "{\n \"features\":[\n\t{\n\t \"name\":\"x\",\n\t \"dtype\":\"FLOAT\",\n\t \"ftype\":\"continuous\",\n\t \"range\":[0,1],\n\t \"repeat"
},
{
"path": "models/tf_mnist/runtime/requirements.txt",
"chars": 37,
"preview": "tensorflow==1.13.1\nseldon-core>=0.2.5"
},
{
"path": "models/tf_mnist/train/Dockerfile",
"chars": 159,
"preview": "FROM tensorflow/tensorflow:1.3.0\n\nRUN mkdir training\nCOPY ./create_model.py /training/create_model.py\nWORKDIR /training\n"
},
{
"path": "models/tf_mnist/train/Makefile",
"chars": 160,
"preview": "\n\nbuild_model:\n\tdocker build --force-rm=true -t seldonio/deepmnistclassifier_trainer:0.1 .\n\npush_image:\n\tdocker push sel"
},
{
"path": "models/tf_mnist/train/create_model.py",
"chars": 1133,
"preview": "from tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot = Tr"
},
{
"path": "nfs.md",
"chars": 1300,
"preview": "# Example NFS Setup\n\nThe steps below are a consolidated set of steps following the guide [here](https://cloud.google.com"
},
{
"path": "notebooks/Makefile",
"chars": 197,
"preview": "SHELL=/bin/bash\n\ntensorflow/core/framework/tensor.proto:\n\t./create-protos.sh\n\n.PHONY: create_protos\ncreate_protos: tenso"
},
{
"path": "notebooks/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "notebooks/create-protos.sh",
"chars": 656,
"preview": "#!/bin/bash\n\nrelease=${1:-\"master\"}\n\necho Downloading proto files for ${release}\n\nbase=https://raw.githubusercontent.com"
},
{
"path": "notebooks/proto/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "notebooks/proto/prediction.proto",
"chars": 2685,
"preview": "syntax = \"proto3\";\n\nimport \"google/protobuf/struct.proto\";\nimport \"tensorflow/core/framework/tensor.proto\";\n\npackage sel"
},
{
"path": "notebooks/requirements.txt",
"chars": 71,
"preview": "matplotlib==3.0.3\ngrpcio==1.20.1\ngrpcio-tools==1.20.1\ngraphviz==0.10.1\n"
},
{
"path": "notebooks/serving.ipynb",
"chars": 7508,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Deploying Various MNIST Models on"
},
{
"path": "notebooks/training.ipynb",
"chars": 6818,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Train Various Models on MNIST usi"
},
{
"path": "notebooks/utils.py",
"chars": 5398,
"preview": "import requests\nfrom requests.auth import HTTPBasicAuth\nfrom random import randint,random\nfrom proto import prediction_p"
},
{
"path": "notebooks/visualizer.py",
"chars": 1209,
"preview": "import graphviz\nimport json\n\ndef _populate_graph(dot, root, suffix=''):\n name = root.get(\"name\")\n id = name+suffix"
},
{
"path": "scripts/README.md",
"chars": 347,
"preview": "# Create MNIST Demo\n\n 1. You will need all prerequisites (gcloud, kubectl, ks) in your path.\n 1. Copy `env-example.sh` t"
},
{
"path": "scripts/create_demo.sh",
"chars": 2651,
"preview": "#!/usr/bin/env bash\n\nset -o nounset\nset -o errexit\nset -o pipefail\n\ncreate_src() {\n mkdir -p ${KUBEFLOW_SRC}\n cd $"
},
{
"path": "scripts/delete-demo.sh",
"chars": 236,
"preview": "#!/usr/bin/env bash\n\nset -o nounset\nset -o errexit\nset -o pipefail\n\n\nif [ ! -f env.sh ]; then\n echo \"Create env.sh by"
},
{
"path": "scripts/env-example.sh",
"chars": 383,
"preview": "STARTUP_DIR=\"$( cd \"$( dirname \"$0\" )\" && pwd )\"\nKFAPP=my-kubeflow \nPROJECT=seldon-demos\nKUBEFLOW_SRC=${STARTUP_DIR}/"
},
{
"path": "scripts/nfs-pvc.yaml",
"chars": 186,
"preview": "apiVersion: v1\nkind: PersistentVolumeClaim\nmetadata:\n name: nfs-1\nspec:\n accessModes:\n - ReadWriteMany\n storageCla"
},
{
"path": "scripts/port-forwards.sh",
"chars": 624,
"preview": "\n#Argo\nkubectl port-forward $(kubectl get pods -n kubeflow -l app=argo-ui -o jsonpath='{.items[0].metadata.name}') -n ku"
},
{
"path": "scripts/watch-mnist.sh",
"chars": 54,
"preview": "watch kubectl get pods -l seldon-app=mnist-classifier\n"
},
{
"path": "workflows/serving-r-mnist-workflow.yaml",
"chars": 3904,
"preview": "# This example demonstrates the use of a git repo as a hard-wired\n# input artifact. The argo repo is cloned to its targe"
},
{
"path": "workflows/serving-sk-mnist-workflow.yaml",
"chars": 3909,
"preview": "# This example demonstrates the use of a git repo as a hard-wired\n# input artifact. The argo repo is cloned to its targe"
},
{
"path": "workflows/serving-tf-mnist-workflow.md",
"chars": 1095,
"preview": "# Example Argo Workflow to dockerize runtime model and deploy it for serving\n\nComments on the [serving-tf-mnist-workflow"
},
{
"path": "workflows/serving-tf-mnist-workflow.yaml",
"chars": 3732,
"preview": "apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: seldon-tf-deploy-\nspec:\n entrypoint: workflow"
},
{
"path": "workflows/training-r-mnist-workflow.yaml",
"chars": 3076,
"preview": "apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: kubeflow-r-train-\nspec:\n entrypoint: workflow"
},
{
"path": "workflows/training-sk-mnist-workflow.yaml",
"chars": 3141,
"preview": "apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: kubeflow-sk-train-\nspec:\n entrypoint: workflo"
},
{
"path": "workflows/training-tf-mnist-workflow.md",
"chars": 1797,
"preview": "# Example Argo Workflow to dockerize and Train Model\n\nComments on the [training-tf-mnist-workflow.yaml](training-tf-mnis"
},
{
"path": "workflows/training-tf-mnist-workflow.yaml",
"chars": 3489,
"preview": "apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: kubeflow-tf-train-\nspec:\n entrypoint: workflo"
}
]
About this extraction
This page contains the full source code of the kubeflow/example-seldon GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 69 files (98.7 KB), approximately 27.1k tokens, and a symbol index with 18 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.