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Deployment Spec

This page provides a reference guide for Gradient Deployments.

Overview

A deployment is maintained by a spec file which is used to change the state of the deployment.

image: lucone83/streamlit-nginx # container image used to run your deployment
containerRegistry: container-registry-name # optional reference to a private container registry
command: # optional command to override the container image's default command
- my
- command
port: 8080 # local container port that your service is running on
env: # environment variables that are applied at run time
- name: ENV
value: VAR
models:
- id: model-handle # the handle of a model from the Gradient Model Registry
path: /opt/models # the local filepath you want to sync it to inside your deployment
repositories:
dataset: dataset-ref # the ID of the dataset you want to sync the repository with
mountPath: /opt/repos # the local filepath you want to sync the repos to inside your deployment
repositories:
- url: https://github.com/tensorflow/serving.git # the URL of the repository
ref: main # optionally, a git branch, commit, or tag to check out
name: tf-serving # the folder name you want to clone the repo into
username: example # optionally, the username to authenticate to the repo with
password: SECRET:secret_name # optionally, the Gradient Secret containing the password for your Git user
resources:
replicas: 1 # number of replicas used for your deployment
instanceType: C4 # instance type that your deployment will run on

Model integration

You can also automatically pull in any models that you have registered through the Gradient Model Registry at runtime. To do this, we just add a models parameter to our deployment spec where we can specify the model(s) we want to pull in by their ID, and optionally also the filepath inside our deployed container where we want to mount them into. If the path is not specified, it will default to /opt/models.

image: tensorflow/serving
port: 8501
models:
- id: model-id
path: /opt/models
env:
- name: MODEL_BASE_PATH
value: /opt/models
- name: MODEL_NAME
value: my-tf-model
resources:
replicas: 1
instanceType: C4

Cloning Repositories into a Deployment

If you don't want to package everything into a Docker container, you can also clone any one or more Git repositories into your Deployment at runtime. To do this, just supply the optional repositories configuration in your deployment spec, and specify:

  • the dataset ID you want to use to sync the repositories into;
  • the folder that you want to access your repositories from within the Deployment; and
  • the URL for one or more Git repositories that you want to clone

Note that if you are using a private Git repository, you can configure authenticated access by also supplying the optional username and password parameters. The password field will need to reference a Gradient Secret and should not contain the plaintext password itself.

You must also have already created a Gradient Dataset to use this feature. We clone your repository into a Dataset for local storage and access within the cluster by your Deployment and are not able to automatically create one for you at this time.

For example, let's say that I want to clone two repositories, one that is public, and one that is private and requires a username and password. I might use a spec like the following:

image: ubuntu:20.04
port: 8501
resources:
replicas: 1
instanceType: C4
repositories:
dataset: dstflhstfexl6w3
mountPath: /opt/repositories
repositories:
- url: https://github.com/tensorflow/serving.git
name: tf-serving
- url: https://github.com/paperspace/ml-private.git
name: paperspace-ml
username: paperspace-user
password: SECRET:paperspace-github-token

Once this completes, I will have access to the TensorFlow Serving repository at /opt/repos/tf-serving, and to a repo of private ML code at /opt/repos/paperspace-ml.

Container Registry integration

Private Docker images can be used by referencing your Container Registry which contains credentials to access that image.

To configure a Container Registry, follow this short guide.

Once configured, we add a containerRegistry parameter to our deployment spec and specify the name of the Container Registry.

image: paperspace/ml_server
containerRegistry: dockerhub-prod
port: 8501
models:
- id: model-id
path: /opt/models
env:
- name: MODEL_BASE_PATH
value: /opt/models
- name: MODEL_NAME
value: my-tf-model
resources:
replicas: 1
instanceType: C4