Outlier Example¶
In this example we will:
Prerequisites¶
This notebooks needs to be run in the tempo-examples conda environment defined below. Create from project root folder:
conda env create --name tempo-examples --file conda/tempo-examples.yaml
Project Structure¶
!tree -P "*.py" -I "__init__.py|__pycache__" -L 2
[01;34m.[00m
├── [01;34martifacts[00m
│ ├── [01;34mmodel[00m
│ ├── [01;34moutlier[00m
│ └── [01;34msvc[00m
├── [01;34mk8s[00m
│ └── [01;34mrbac[00m
├── [01;34mREADME_files[00m
├── [01;34msrc[00m
│ ├── constants.py
│ ├── data.py
│ ├── outlier.py
│ ├── tempo.py
│ └── utils.py
└── [01;34mtests[00m
└── test_tempo.py
9 directories, 6 files
Train Models¶
This section is where as a data scientist you do your work of training models and creating artfacts.
For this example we train sklearn and xgboost classification models for the iris dataset.
import os
import logging
import numpy as np
import tempo
from tempo.utils import logger
from src.constants import ARTIFACTS_FOLDER
logger.setLevel(logging.ERROR)
logging.basicConfig(level=logging.ERROR)
from src.data import Cifar10
data = Cifar10()
(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)
Download pretrained Resnet32 Tensorflow model for CIFAR10
!rclone --config ./rclone-gcs.conf copy gs://seldon-models/tfserving/cifar10/resnet32 ./artifacts/model
Download or train an outlier detector on CIFAR10 data
load_pretrained = True
if load_pretrained: # load pre-trained detector
!rclone --config ./rclone-gcs.conf copy gs://seldon-models/tempo/cifar10/outlier/cifar10 ./artifacts/outlier/cifar10
else:
from src.outlier import train_outlier_detector
train_outlier_detector(data, ARTIFACTS_FOLDER)
Create Tempo Artifacts¶
from src.tempo import create_outlier_cls, create_model, create_svc_cls
cifar10_model = create_model()
OutlierModel = create_outlier_cls()
outlier = OutlierModel()
Cifar10Svc = create_svc_cls(outlier, cifar10_model)
svc = Cifar10Svc()
Loading from /home/clive/work/mlops/fork-tempo/docs/examples/outlier/artifacts/outlier
# %load src/tempo.py
import json
import os
import numpy as np
from alibi_detect.base import NumpyEncoder
from src.constants import ARTIFACTS_FOLDER, MODEL_FOLDER, OUTLIER_FOLDER
from tempo.protocols.v2 import V2Protocol
from tempo.protocols.tensorflow import TensorflowProtocol
from tempo.serve.metadata import ModelFramework
from tempo.serve.model import Model
from tempo.serve.pipeline import PipelineModels
from tempo.serve.utils import model, pipeline, predictmethod
def create_outlier_cls():
@model(
name="outlier",
platform=ModelFramework.Custom,
protocol=V2Protocol(),
uri="s3://tempo/outlier/cifar10/outlier",
local_folder=os.path.join(ARTIFACTS_FOLDER, OUTLIER_FOLDER),
)
class OutlierModel(object):
def __init__(self):
from alibi_detect.utils.saving import load_detector
model = self.get_tempo()
models_folder = model.details.local_folder
print(f"Loading from {models_folder}")
self.od = load_detector(os.path.join(models_folder, "cifar10"))
@predictmethod
def outlier(self, payload: np.ndarray) -> dict:
od_preds = self.od.predict(
payload,
outlier_type="instance", # use 'feature' or 'instance' level
return_feature_score=True,
# scores used to determine outliers
return_instance_score=True,
)
return json.loads(json.dumps(od_preds, cls=NumpyEncoder))
return OutlierModel
def create_model():
cifar10_model = Model(
name="resnet32",
protocol=TensorflowProtocol(),
platform=ModelFramework.Tensorflow,
uri="gs://seldon-models/tfserving/cifar10/resnet32",
local_folder=os.path.join(ARTIFACTS_FOLDER, MODEL_FOLDER),
)
return cifar10_model
def create_svc_cls(outlier, model):
@pipeline(
name="cifar10-service",
protocol=V2Protocol(),
uri="s3://tempo/outlier/cifar10/svc",
local_folder=os.path.join(ARTIFACTS_FOLDER, "svc"),
models=PipelineModels(outlier=outlier, cifar10=model),
)
class Cifar10Svc(object):
@predictmethod
def predict(self, payload: np.ndarray) -> np.ndarray:
r = self.models.outlier(payload=payload)
if r["data"]["is_outlier"][0]:
return np.array([])
else:
return self.models.cifar10(payload)
return Cifar10Svc
Unit Tests¶
Here we run our unit tests to ensure the orchestration works before running on the actual models.
# %load tests/test_tempo.py
import numpy as np
from src.tempo import create_model, create_outlier_cls, create_svc_cls
def test_svc_outlier():
model = create_model()
OutlierModel = create_outlier_cls()
outlier = OutlierModel()
Cifar10Svc = create_svc_cls(outlier, model)
svc = Cifar10Svc()
svc.models.outlier = lambda payload: {"data": {"is_outlier": [1]}}
svc.models.cifar10 = lambda input: np.array([[0.2]])
res = svc(np.array([1]))
assert res.shape[0] == 0
def test_svc_inlier():
model = create_model()
OutlierModel = create_outlier_cls()
outlier = OutlierModel()
Cifar10Svc = create_svc_cls(outlier, model)
svc = Cifar10Svc()
svc.models.outlier = lambda payload: {"data": {"is_outlier": [0]}}
svc.models.cifar10 = lambda input: np.array([[0.2]])
res = svc(np.array([1]))
assert res.shape[0] == 1
!python -m pytest tests/
[1m============================= test session starts ==============================[0m
platform linux -- Python 3.7.9, pytest-6.2.0, py-1.10.0, pluggy-0.13.1
rootdir: /home/clive/work/mlops/fork-tempo, configfile: setup.cfg
plugins: cases-3.4.6, cov-2.12.1, asyncio-0.14.0
collected 2 items [0m[1m
tests/test_tempo.py [32m.[0m[32m.[0m[33m [100%][0m
[33m=============================== warnings summary ===============================[0m
../../../../../../anaconda3/envs/tempo-examples/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py:22
/home/clive/anaconda3/envs/tempo-examples/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py:22: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
../../../../../../anaconda3/envs/tempo-examples/lib/python3.7/site-packages/packaging/version.py:130
/home/clive/anaconda3/envs/tempo-examples/lib/python3.7/site-packages/packaging/version.py:130: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release
DeprecationWarning,
-- Docs: https://docs.pytest.org/en/stable/warnings.html
[33m======================== [32m2 passed[0m, [33m[1m2 warnings[0m[33m in 4.69s[0m[33m =========================[0m
Unresolved object in checkpoint: (root).encoder.fc_mean.kernel
Unresolved object in checkpoint: (root).encoder.fc_mean.bias
Unresolved object in checkpoint: (root).encoder.fc_log_var.kernel
Unresolved object in checkpoint: (root).encoder.fc_log_var.bias
A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
Unresolved object in checkpoint: (root).encoder.fc_mean.kernel
Unresolved object in checkpoint: (root).encoder.fc_mean.bias
Unresolved object in checkpoint: (root).encoder.fc_log_var.kernel
Unresolved object in checkpoint: (root).encoder.fc_log_var.bias
A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
Save Outlier and Svc Environments¶
tempo.save(OutlierModel)
Collecting packages...
Packing environment at '/home/clive/anaconda3/envs/tempo-c4fe11aa-1cd6-43dd-9fab-0dcb4fca7a62' to '/home/clive/work/mlops/fork-tempo/docs/examples/outlier/artifacts/outlier/environment.tar.gz'
[########################################] | 100% Completed | 1min 21.6s
tempo.save(Cifar10Svc)
Collecting packages...
Packing environment at '/home/clive/anaconda3/envs/tempo-cfface3b-1080-47d2-a3b6-113db8e286e5' to '/home/clive/work/mlops/fork-tempo/docs/examples/outlier/artifacts/svc/environment.tar.gz'
[########################################] | 100% Completed | 16.1s
Test Locally on Docker¶
Here we test our models using production images but running locally on Docker. This allows us to ensure the final production deployed model will behave as expected when deployed.
from tempo import deploy_local
remote_model = deploy_local(svc)
from src.utils import show_image
show_image(data.X_test[0:1])
remote_model.predict(payload=data.X_test[0:1])
array([[3.92254496e-09, 1.20455460e-11, 2.66011191e-09, 9.99992609e-01,
2.52213306e-10, 5.40860242e-07, 6.75954425e-06, 4.75119076e-12,
6.90874735e-09, 1.07275586e-11]])
from src.utils import create_cifar10_outlier
outlier_img = create_cifar10_outlier(data)
show_image(outlier_img)
remote_model.predict(payload=outlier_img)
array([], dtype=float64)
remote_model.undeploy()
Production Option 1 (Deploy to Kubernetes with Tempo)¶
Here we illustrate how to run the final models in “production” on Kubernetes by using Tempo to deploy
Prerequisites¶
Create a Kind Kubernetes cluster with Minio and Seldon Core installed using Ansible as described here.
!kubectl apply -f k8s/rbac -n production
secret/minio-secret configured
serviceaccount/tempo-pipeline unchanged
role.rbac.authorization.k8s.io/tempo-pipeline unchanged
rolebinding.rbac.authorization.k8s.io/tempo-pipeline-rolebinding unchanged
from tempo.examples.minio import create_minio_rclone
import os
create_minio_rclone(os.getcwd()+"/rclone-minio.conf")
tempo.upload(cifar10_model)
tempo.upload(outlier)
tempo.upload(svc)
from tempo.serve.metadata import SeldonCoreOptions
runtime_options = SeldonCoreOptions(**{
"remote_options": {
"namespace": "production",
"authSecretName": "minio-secret"
}
})
from tempo import deploy_remote
remote_model = deploy_remote(svc, options=runtime_options)
from src.utils import show_image
show_image(data.X_test[0:1])
remote_model.predict(payload=data.X_test[0:1])
array([[3.92254496e-09, 1.20455460e-11, 2.66011191e-09, 9.99992609e-01,
2.52213306e-10, 5.40860242e-07, 6.75954425e-06, 4.75119076e-12,
6.90874735e-09, 1.07275586e-11]])
from src.utils import create_cifar10_outlier
outlier_img = create_cifar10_outlier(data)
show_image(outlier_img)
remote_model.predict(payload=outlier_img)
array([], dtype=float64)
remote_model.undeploy()
Production Option 2 (Gitops)¶
We create yaml to provide to our DevOps team to deploy to a production cluster
We add Kustomize patches to modify the base Kubernetes yaml created by Tempo
from tempo import manifest
from tempo.serve.metadata import SeldonCoreOptions
runtime_options = SeldonCoreOptions(**{
"remote_options": {
"namespace": "production",
"authSecretName": "minio-secret"
}
})
yaml_str = manifest(svc, options=runtime_options)
with open(os.getcwd()+"/k8s/tempo.yaml","w") as f:
f.write(yaml_str)
!kustomize build k8s
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
annotations:
seldon.io/tempo-description: ""
seldon.io/tempo-model: '{"model_details": {"name": "cifar10-service", "local_folder":
"/home/clive/work/mlops/fork-tempo/docs/examples/outlier/artifacts/svc", "uri":
"s3://tempo/outlier/cifar10/svc", "platform": "tempo", "inputs": {"args": [{"ty":
"numpy.ndarray", "name": "payload"}]}, "outputs": {"args": [{"ty": "numpy.ndarray",
"name": null}]}, "description": ""}, "protocol": "tempo.kfserving.protocol.KFServingV2Protocol",
"runtime_options": {"runtime": "tempo.seldon.SeldonKubernetesRuntime", "state_options":
{"state_type": "LOCAL", "key_prefix": "", "host": "", "port": ""}, "insights_options":
{"worker_endpoint": "", "batch_size": 1, "parallelism": 1, "retries": 3, "window_time":
0, "mode_type": "NONE", "in_asyncio": false}, "ingress_options": {"ingress":
"tempo.ingress.istio.IstioIngress", "ssl": false, "verify_ssl": true}, "replicas":
1, "minReplicas": null, "maxReplicas": null, "authSecretName": "minio-secret",
"serviceAccountName": null, "add_svc_orchestrator": false, "namespace": "production"}}'
labels:
seldon.io/tempo: "true"
name: cifar10-service
namespace: production
spec:
predictors:
- annotations:
seldon.io/no-engine: "true"
componentSpecs:
- spec:
containers:
- name: classifier
resources:
limits:
cpu: 1
memory: 1Gi
requests:
cpu: 500m
memory: 500Mi
graph:
envSecretRefName: minio-secret
implementation: TEMPO_SERVER
modelUri: s3://tempo/outlier/cifar10/svc
name: cifar10-service
serviceAccountName: tempo-pipeline
type: MODEL
name: default
replicas: 1
protocol: kfserving
---
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
annotations:
seldon.io/tempo-description: ""
seldon.io/tempo-model: '{"model_details": {"name": "outlier", "local_folder":
"/home/clive/work/mlops/fork-tempo/docs/examples/outlier/artifacts/outlier",
"uri": "s3://tempo/outlier/cifar10/outlier", "platform": "custom", "inputs":
{"args": [{"ty": "numpy.ndarray", "name": "payload"}]}, "outputs": {"args":
[{"ty": "builtins.dict", "name": null}]}, "description": ""}, "protocol": "tempo.kfserving.protocol.KFServingV2Protocol",
"runtime_options": {"runtime": "tempo.seldon.SeldonKubernetesRuntime", "state_options":
{"state_type": "LOCAL", "key_prefix": "", "host": "", "port": ""}, "insights_options":
{"worker_endpoint": "", "batch_size": 1, "parallelism": 1, "retries": 3, "window_time":
0, "mode_type": "NONE", "in_asyncio": false}, "ingress_options": {"ingress":
"tempo.ingress.istio.IstioIngress", "ssl": false, "verify_ssl": true}, "replicas":
1, "minReplicas": null, "maxReplicas": null, "authSecretName": "minio-secret",
"serviceAccountName": null, "add_svc_orchestrator": false, "namespace": "production"}}'
labels:
seldon.io/tempo: "true"
name: outlier
namespace: production
spec:
predictors:
- annotations:
seldon.io/no-engine: "true"
componentSpecs:
- spec:
containers:
- args: []
env:
- name: MLSERVER_HTTP_PORT
value: "9000"
- name: MLSERVER_GRPC_PORT
value: "9500"
- name: MLSERVER_MODEL_IMPLEMENTATION
value: tempo.mlserver.InferenceRuntime
- name: MLSERVER_MODEL_NAME
value: outlier
- name: MLSERVER_MODEL_URI
value: /mnt/models
- name: TEMPO_RUNTIME_OPTIONS
value: '{"runtime": "tempo.seldon.SeldonKubernetesRuntime", "state_options":
{"state_type": "LOCAL", "key_prefix": "", "host": "", "port": ""}, "insights_options":
{"worker_endpoint": "", "batch_size": 1, "parallelism": 1, "retries":
3, "window_time": 0, "mode_type": "NONE", "in_asyncio": true}, "ingress_options":
{"ingress": "tempo.ingress.istio.IstioIngress", "ssl": false, "verify_ssl":
true}, "replicas": 1, "minReplicas": null, "maxReplicas": null, "authSecretName":
"minio-secret", "serviceAccountName": null, "add_svc_orchestrator":
false, "namespace": "production"}'
image: seldonio/mlserver:0.3.2
name: outlier
graph:
envSecretRefName: minio-secret
implementation: TEMPO_SERVER
modelUri: s3://tempo/outlier/cifar10/outlier
name: outlier
serviceAccountName: tempo-pipeline
type: MODEL
name: default
replicas: 1
protocol: kfserving
---
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
annotations:
seldon.io/tempo-description: ""
seldon.io/tempo-model: '{"model_details": {"name": "resnet32", "local_folder":
"/home/clive/work/mlops/fork-tempo/docs/examples/outlier/artifacts/model", "uri":
"gs://seldon-models/tfserving/cifar10/resnet32", "platform": "tensorflow", "inputs":
{"args": [{"ty": "numpy.ndarray", "name": null}]}, "outputs": {"args": [{"ty":
"numpy.ndarray", "name": null}]}, "description": ""}, "protocol": "tempo.kfserving.protocol.KFServingV1Protocol",
"runtime_options": {"runtime": "tempo.seldon.SeldonKubernetesRuntime", "state_options":
{"state_type": "LOCAL", "key_prefix": "", "host": "", "port": ""}, "insights_options":
{"worker_endpoint": "", "batch_size": 1, "parallelism": 1, "retries": 3, "window_time":
0, "mode_type": "NONE", "in_asyncio": false}, "ingress_options": {"ingress":
"tempo.ingress.istio.IstioIngress", "ssl": false, "verify_ssl": true}, "replicas":
1, "minReplicas": null, "maxReplicas": null, "authSecretName": "minio-secret",
"serviceAccountName": null, "add_svc_orchestrator": false, "namespace": "production"}}'
labels:
seldon.io/tempo: "true"
name: resnet32
namespace: production
spec:
predictors:
- annotations:
seldon.io/no-engine: "true"
graph:
envSecretRefName: minio-secret
implementation: TENSORFLOW_SERVER
modelUri: gs://seldon-models/tfserving/cifar10/resnet32
name: resnet32
type: MODEL
name: default
replicas: 1
protocol: tensorflow