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7 changed files with 32 additions and 7 deletions
@@ -2,4 +2,5 @@ cpr_model_server.py
entrypoint.py
state_dict.pth
config.json
**/__pycache__
**/__pycache__
!testdata/**
@@ -2,7 +2,7 @@
## About CPR
CPR ([custom prediction routines](https://github.com/googleapis/python-aiplatform/blob/custom-prediction-routine/google/cloud/aiplatform/prediction/README.md)) is a framework designed by Google Cloud developers to make it easier to combine machine learning models with custom preprocessing and postprocessing logic in a real-time serving application.
CPR ([custom prediction routines](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/prediction/README.md)) is a framework designed by Google Cloud developers to make it easier to combine machine learning models with custom preprocessing and postprocessing logic in a real-time serving application.
## Using this example
@@ -34,6 +34,23 @@ Finally, install the Python modules required to build and run the model server:
pip install -r requirements.txt
```
### Auth
This example uses Google Cloud Storage for hosting model artifacts and Artifact Registry to store the container image.
You'll need to authorize yourself before you can interact with these.
First, log in to GCP with application default credentials:
```sh
gcloud auth application-default login
```
Next, if you haven't done so already, set up the [gcloud credential helper](https://cloud.google.com/artifact-registry/docs/docker/authentication)
for the Artifact Registry region where you intend to host the image.
```
gcloud auth configure-docker <region>-docker.pkg.dev
```
### Predictor
The `TimmPredictor` class in `timm_serving/predictor.py` implements most of the important logic for the server.
@@ -60,9 +60,9 @@ class CPRConfig(object):
image: str = "timm_predictor:latest"
artifact_local_dir: str = ""
region: str = "us-central1"
project_id: str = "samthrasher-experimental"
project_id: str = "<your project ID here>"
repository: str = "cpr-images"
artifact_gcs_dir: str = "gs://samthrasher-cpr-example/timm-vit224/"
artifact_gcs_dir: str = "gs://<your bucket ID here>/timm-vit224/"
model_name: str = ""
endpoint_name: str = ""
machine_type: str = "n1-standard-2"
@@ -5,4 +5,4 @@ timm==0.5.4
smart_open==6.0.0
google-cloud-storage>=1.26.0,<2.0.0dev
google-cloud-aiplatform[prediction] @ git+https://github.com/googleapis/python-aiplatform.git@custom-prediction-routine
google-cloud-aiplatform[prediction]>=1.16.0
@@ -70,7 +70,10 @@ class PredictorUnitTests(absltest.TestCase):
def setUp(self):
super().setUp()
self.config = CPRConfig()
self.config.load()
try:
self.config.load()
except FileNotFoundError:
logging.info("No saved config file found, using default values.")
self.predictor = predictor.TimmPredictor()
def test_load_from_saved_state_dict_ok(self):
@@ -170,7 +173,10 @@ class ServerEndToEndTests(absltest.TestCase):
def setUp(self):
super().setUp()
self.config = CPRConfig()
self.config.load()
try:
self.config.load()
except FileNotFoundError:
logging.info("No saved config file found, using default values.")
self.local_model = cpr.LocalModel(
serving_container_spec=aiplatform.gapic.ModelContainerSpec(
image_uri=self.config.image
@@ -0,0 +1 @@
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