xpl.ai client SDK.
Project description
Request early access from xpl.ai to get login credentials. ##CLI Usage
Authentication
Login.
xpl auth login
Options:
-u, --username
-s, --secret
Logout
xpl auth logout
Tasks
Create task draft
xpl task draft
Options:
-n, --name
-m, --modality: # 'image', 'video', 'audio', 'text'
# Currently supported: 'image'
-c, --csv: # A path to csv file containing training examples that will be used to pretreain the model.
Initialize Active learning pipeline from the task.
- Model for the task will be assembled,
- Data pipelines will be initialized
- Sample examples for pretraining will be uploaded
- Training from provided examples will start
xpl task commit
List tasks
xpl task list
Describe task's detail information
xpl task describe
Options:
-t, --task_id: # an id of the task. Can be obtained from "xpl task list" command
Annotations
List active annotation jobs that are associated with the task. This will return list of urls, corresponding toi annotation jobs. Url directs to the annotation UI.
xpl annotations list
Options:
-t, --task_id: # an id of the task. Can be obtained from "xpl task list" command
Concepts
Search XPL Platform for the concepts by the lemma
xpl concept search
Options:
-l, --lemma: # A single word human-readable lemma that describes concept in English
##End-user integration The core of end-user integration is a Task object. ###Instantiate Task object
task = Task(task_id='b622d1e8bd0d43d0be184239bf9ce53d',
task_api_key='a714e3ce19594872be7fb9042d02134b')
# task_api_key can be obtained from cli: "xpl task describe"
Instantiating Task object will:
- verify validity of the task's api key,
- check the active version of the model
- if needed: fetch the model from storage using secured http channel and cache it on the disk
- load model and will be ready to accept data for inferences ###Run task on data of corresponding modality
from xplai import Task
image_path = '/data/image.png'
concepts_detected = task.run(source=image_path)
# do something useful
for concept in concepts_detected:
print(f'detected {concept.display_name} in {image_path}')
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