https://github.com/ycliuhw/charm-kubeflow-tf-serving
Charm layer for TensorFlow Serving for Kubernetes models in Juju
https://github.com/ycliuhw/charm-kubeflow-tf-serving
Last synced: about 1 year ago
JSON representation
Charm layer for TensorFlow Serving for Kubernetes models in Juju
- Host: GitHub
- URL: https://github.com/ycliuhw/charm-kubeflow-tf-serving
- Owner: ycliuhw
- License: apache-2.0
- Created: 2018-08-23T07:17:26.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-08-13T14:31:48.000Z (almost 8 years ago)
- Last Synced: 2025-06-11T06:04:57.019Z (about 1 year ago)
- Language: Python
- Size: 10.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
TensorFlow Serving for Kubeflow
===============================
This charm deploys TensorFlow Serving configured for use with
Kubeflow to Kubernetes models in Juju.
Usage
-----
Deploy an instance of this charm for each model you wish to serve, providing
the path to the trained model via the `model` config option. It is also
useful to name the deployed application after the model. For example, to
deploy the demo `inception` model, you would use:
```
juju deploy cs:~johnsca/kubeflow-tf-serving inception --config model=gs://kubeflow-models/inception
```
Note that, while the Google Storage URL for the demo model is publicly
accessible, it still requires Google credentials to access. If you used
[`conjure-up canonical-kubernetes`][cdk] and deployed to [GCP][], this will
be setup automatically for you.
If you are using Kubernetes on a different provider, you can also attach
the model as a resource:
```
juju deploy cs:~johnsca/kubeflow-tf-serving inception --resource model=/path/to/model
```
[cdk]: https://kubernetes.io/docs/getting-started-guides/ubuntu/installation/#conjure-up
[GCP]: https://cloud.google.com/