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https://github.com/alexeygrigorev/kfserving-keras-transformer
A transformer for KFServing that users keras_image_helper
https://github.com/alexeygrigorev/kfserving-keras-transformer
deep-learning keras kfserving kubeflow
Last synced: 20 days ago
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A transformer for KFServing that users keras_image_helper
- Host: GitHub
- URL: https://github.com/alexeygrigorev/kfserving-keras-transformer
- Owner: alexeygrigorev
- Created: 2021-02-27T22:52:33.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-02-28T11:04:40.000Z (almost 4 years ago)
- Last Synced: 2024-11-16T12:34:54.788Z (3 months ago)
- Topics: deep-learning, keras, kfserving, kubeflow
- Language: Python
- Homepage:
- Size: 4.88 KB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# KFServing Keras Transformer
A transformer for KFServing that users [keras_image_helper](https://github.com/alexeygrigorev/keras-image-helper).
## Conext for running locally
Suppose we deployed a model with KFServing without a tranformer:
```yaml
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
name: "clothing-model"
spec:
default:
predictor:
serviceAccountName: sa
tensorflow:
storageUri: "s3://mlbookcamp-models/clothing-model"
```
And the model is deployed to clothing-model.default.kubeflow.mlbookcamp.com.
## Running locally
Running the service
```bash
export MODEL_INPUT_SIZE="299,299"
export KERAS_MODEL_NAME="xception"
export MODEL_LABELS="dress,hat,longsleeve,outwear,pants,shirt,shoes,shorts,skirt,t-shirt"
python image_transformer.py \
--predictor_host="clothing-model.default.kubeflow.mlbookcamp.com" \
--model_name="clothing-model"
```
Testing it
```python
import requests
data = {
"instances": [
{"url": "http://bit.ly/mlbookcamp-pants"},
]
}
url = 'http://localhost:8080/v1/models/clothing-model:predict'
result = requests.post(url, json=data).json()
print(result)
```
Or run `python test.py`
## Running with Docker
Build it:
```bash
LOCAL_TAG="kfserving-keras-transformer:0.0.1"
docker build -t ${LOCAL_TAG} .
```
Running it:
```bash
docker run -it \
-p 8080:8080 \
-e MODEL_INPUT_SIZE="299,299" \
-e KERAS_MODEL_NAME="xception" \
-e MODEL_LABELS="dress,hat,longsleeve,outwear,pants,shirt,shoes,shorts,skirt,t-shirt" \
${LOCAL_TAG} \
--predictor_host="clothing-model.default.kubeflow.mlbookcamp.com" \
--model_name="clothing-model"
```
Testing:
```bash
python test.py
```
Publishing:
```bash
REMOTE_TAG="agrigorev/${LOCAL_TAG}"
docker tag ${LOCAL_TAG} ${REMOTE_TAG}
docker push ${REMOTE_TAG}
```
## Using it with KFServing
```yaml
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
name: "clothing-model"
spec:
default:
predictor:
serviceAccountName: sa
tensorflow:
storageUri: "s3://mlbookcamp-models/clothing-model"
transformer:
custom:
container:
image: "agrigorev/kfserving-keras-transformer:0.0.1"
name: user-container
env:
- name: MODEL_INPUT_SIZE
value: "299,299"
- name: KERAS_MODEL_NAME
value: "xception"
- name: MODEL_LABELS
value: "dress,hat,longsleeve,outwear,pants,shirt,shoes,shorts,skirt,t-shirt"
```
Testing it:
```python
import requests
data = {
"instances": [
{"url": "http://bit.ly/mlbookcamp-pants"},
]
}
url = 'https://clothing-model.default.kubeflow.mlbookcamp.com/v1/models/clothing-model:predict'
result = requests.post(url, json=data).json()
print(result)
```