This release is a pre-release and may not be stable for production use.
Overview
Welcome to Instill Python SDK, where the world of AI-first application comes alive in the form of Python.
Before you jump into creating your first application with this SDK tool, we recommend you to get familiar with the core concepts of Instill Product first. You can check out our documentation here:
Setup
Requirements
- Python 3.8+
Installation
$ GRPC_PYTHON_LDFLAGS=" -framework CoreFoundation" pip install grpcio --no-binary :all:
Install it directly into an activated virtual environment:
$ pip install instill-sdk
or add it to your Poetry project:
$ poetry add instill-sdk
Check import
After installation, you can check if it has been installed correctly:
$ python
>>> import instill
>>> instill.__version__
Config Instill Core or Instill Cloud instance
Before we can start using this SDK, you will need to create a config file under this path ${HOME}/.config/instill/sdk/python/config.yaml, and within that path you will need to fill in some basic parameters for your desired host.1
Within the config file, you can define multiple instances with the alias of your liking, later in the SDK you can refer to this alias to switch between instances.2
hosts:
alias1:
url: str
secure: bool
token: str
alias2:
url: str
secure: bool
token: str
...
...
Example:
hosts:
default:
url: localhost:8080
secure: false
token: instill_sk***
cloud:
url: api.instill.tech
secure: true
token: instill_sk***
Usage
Create client
You can also find some notebook examples here
Simply import the get_client function to get the client that are connected to all services with the config you setup previously.
from instill.clients import get_client
client = get_client()
If you have not set up Instill VDP or Instill Model, you will get a warning like this:
2023-09-27 18:49:04,871.871 WARNING Instill VDP is not serving, VDP functionalities will not work
2023-09-27 18:49:04,907.907 WARNING Instill Model is not serving, Model functionalities will not work
You can check the readiness of each service:
client.mgmt_service.is_serving()
# True
client.connector_service.is_serving()
# True
client.pipeline_service.is_serving()
# True
client.model_service.is_serving()
# True
You can also switch to other instances
client.set_instance("your-instance-in-config")
client.mgmt_service.instance
# 'your-instance-in-config'
After making sure all desired services are serving, we can check the user status by:
client.mgmt_service.get_user()
If you have a valid api_token in your config file, you should see something like this:
name: "users/admin"
uid: "4767b74d-640a-4cdf-9c6d-7bb0e36098a0"
id: "admin"
type: OWNER_TYPE_USER
create_time {
seconds: 1695589596
nanos: 36522000
}
update_time {
seconds: 1695589749
nanos: 544980000
}
email: "hello@instill.tech"
first_name: "Instill"
last_name: "AI"
org_name: "Instill AI"
role: "hobbyist"
newsletter_subscription: true
cookie_token: ""
Now we can proceed to create resources
Create Model
Let's say we want to serve a yolov7 model from github with the following configs
model = {
"model_name": "yolov7",
"model_repo": "instill-ai/model-yolov7-dvc",
"model_tag": "v1.0-cpu",
}
Simply import the GithubModel resource and fill in the corresponding fields
from instill.resources.model import GithubModel
yolov7 = GithubModel(
client=client,
name=model["model_name"],
model_repo=model["model_repo"],
model_tag=model["model_tag"],
)
After the creation is done, we can check the state of the model3
yolov7.get_state()
# 1
# means STATE_OFFLINE
Now we can deploy the model
yolov7.deploy()
Check the status
yolov7.get_state()
# 2
# means STATE_ONLINE
Trigger the model with the correct task type4
from instill.resources import model_pb, task_detection
task_inputs = [
model_pb.TaskInput(
detection=task_detection.DetectionInput(
image_url="https://artifacts.instill.tech/imgs/dog.jpg"
)
),
model_pb.TaskInput(
detection=task_detection.DetectionInput(
image_url="https://artifacts.instill.tech/imgs/bear.jpg"
)
),
model_pb.TaskInput(
detection=task_detection.DetectionInput(
image_url="https://artifacts.instill.tech/imgs/polar-bear.jpg"
)
),
]
outputs = yolov7(task_inputs=task_inputs)
Now if you print the outputs, you will get a list of specific task output, in this case is a list of TASK_DETECTION output
[detection {
objects {
category: "dog"
score: 0.958271801
bounding_box {
top: 102
left: 324
width: 208
height: 403
}
}
objects {
category: "dog"
score: 0.945684791
bounding_box {
top: 198
left: 130
width: 198
height: 236
}
}
}
, detection {
objects {
category: "bear"
score: 0.968335629
bounding_box {
top: 85
left: 291
width: 554
height: 756
}
}
}
, detection {
objects {
category: "bear"
score: 0.948612273
bounding_box {
top: 458
left: 1373
width: 1298
height: 2162
}
}
}
]
Create connector
With similiar conecpt as creating model, below is the steps to create a instill model connector
First import our predefined InstillModelConnector
from instill.resources import InstillModelConnector, connector_pb
Then we set up the connector resource information5
# specify the `Instill Model` host url
instill_model = InstillModelConnector(
client,
name="instill",
server_url="http://api-gateway:8080",
)
After the connector is created, the state should be STATE_DISCONNECTED
instill_model.get_state() == connector_pb.ConnectorResource.STATE_DISCONNECTED
# True
Now we can test the connection for the newly configured connector, to make sure the connection with the host can be established
instill_model.test() == connector_pb.ConnectorResource.STATE_CONNECTED
# True
Create pipeline
Since we have created a Instill Model Connector that connect to our Instill Model instance, we can now create a pipeline that utilize both Instill VDP and Instill Model
First we import Pipeline class and other helper functions
from instill.resources import (
Pipeline,
pipeline_pb,
create_start_operator,
create_end_operator,
create_recipe,
)
To Form a pipeine, it required a start operator and a end operator, we have helper functions to create both
# create start operator
start_operator_component = create_start_operator(
{"metadata": {"input": {"title": "input", "type": "image"}}}
)
# create end operator
end_operator_component = create_end_operator(
config={
"input": {"output": "{ yolov7.output.objects }"},
"metadata": {"output": {}},
}
)
Now that we have both start operator and end operator ready, we can move out to create a model component. From the already defined instill Model Connector, we can now utilize the models serving on Instill Model, import them as a component.
# create model connector component from the connector resource we had previously
# here we need to specify which model we want to use on our `Instill Model` instance
# in this case there is only one model we deployed, which is the yolov7 model
instill_model_connector_component = instill_model.create_component(
name="yolov7",
config={
"input": {
"image_base64": "{ start.input }",
"model_namespace": "admin",
"model_id": "yolov7",
},
"task": "TASK_DETECTION",
},
)
We now have all the components ready for the pipeline. Next, we add them into the recipe and create a pipeline.
# create a recipe to construct the pipeline
recipe = create_recipe([start_operator_component, instill_model_connector_component, end_operator_component])
# create pipeline
instill_model_pipeline = Pipeline(
client=client, name="instill-model-pipeline", recipe=recipe
)
Finally the pipeline is done, now let us test it by triggering it!
# we can trigger the pipeline now
i = Struct()
i.update(
{
"input": base64.b64encode(
requests.get(
"https://artifacts.instill.tech/imgs/dog.jpg", timeout=5
).content
).decode("ascii")
}
)
# verify the output
instill_model_pipeline([i])[0][0]["output"][0]["category"] == "dog"
Contributing
Please refer to the Contributing Guidelines for more details.
Community support
Please refer to the community repository.
License
See the LICENSE file for licensing information.
-
You can obtain an
api_token, by simply going to Settings > API Tokens page from the console, no matter it isInstill CoreorInstill Cloud. ↩ -
SDK is default to look for instance named
defaultfirst, and will fall back to the first instance entry in the config file ifdefaultnot found ↩ -
Check out our task protocol to learn more, or read our json schema directly ↩
-
Find out the resource definition in our json schema ↩
1 maintainer
Metadata
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