Supervisely
About SuperviselyEcosystemContact usSlack
  • 💻Supervisely Developer Portal
  • 🎉Getting Started
    • Installation
    • Basics of authentication
    • Intro to Python SDK
    • Environment variables
    • Supervisely annotation format
      • Project Structure
      • Project Meta: Classes, Tags, Settings
      • Objects
      • Tags
      • Image Annotation
      • Video Annotation
      • Point Clouds Annotation
      • Point Cloud Episode Annotation
      • Volumes Annotation
    • Python SDK tutorials
      • Images
        • Images
        • Image and object tags
        • Spatial labels on images
        • Keypoints (skeletons)
        • Multispectral images
        • Multiview images
        • Advanced: Optimized Import
        • Advanced: Export
      • Videos
        • Videos
        • Video and object tags
        • Spatial labels on videos
      • Point Clouds
        • Point Clouds (LiDAR)
        • Point Cloud Episodes and object tags
        • 3D point cloud object segmentation based on sensor fusion and 2D mask guidance
        • 3D segmentation masks projection on 2D photo context image
      • Volumes
        • Volumes (DICOM)
        • Spatial labels on volumes
      • Common
        • Iterate over a project
        • Iterate over a local project
        • Progress Bar tqdm
        • Cloning projects for development
    • Command Line Interface (CLI)
      • Enterprise CLI Tool
        • Instance administration
        • Workflow automation
      • Supervisely SDK CLI
    • Connect your computer
      • Linux
      • Windows WSL
      • Troubleshooting
  • 🔥App development
    • Basics
      • Create app from any py-script
      • Configuration file
        • config.json
        • Example 1. Headless
        • Example 2. App with GUI
        • v1 - Legacy
          • Example 1. v1 Modal Window
          • Example 2. v1 app with GUI
      • Add private app
      • Add public app
      • App Compatibility
    • Apps with GUI
      • Hello World!
      • App in the Image Labeling Tool
      • App in the Video Labeling Tool
      • In-browser app in the Labeling Tool
    • Custom import app
      • Overview
      • From template - simple
      • From scratch - simple
      • From scratch GUI - advanced
      • Finding directories with specific markers
    • Custom export app
      • Overview
      • From template - simple
      • From scratch - advanced
    • Neural Network integration
      • Overview
      • Serving App
        • Introduction
        • Instance segmentation
        • Object detection
        • Semantic segmentation
        • Pose estimation
        • Point tracking
        • Object tracking
        • Mask tracking
        • Image matting
        • How to customize model inference
        • Example: Custom model inference with probability maps
      • Serving App with GUI
        • Introduction
        • How to use default GUI template
        • Default GUI template customization
        • How to create custom user interface
      • Inference API
      • Training App
        • Overview
        • Tensorboard template
        • Object detection
      • High level scheme
      • Custom inference pipeline
      • Train and predict automation model pipeline
    • Advanced
      • Advanced debugging
      • How to make your own widget
      • Tutorial - App Engine v1
        • Chapter 1 Headless
          • Part 1 — Hello world! [From your Python script to Supervisely APP]
          • Part 2 — Errors handling [Catching all bugs]
          • Part 3 — Site Packages [Customize your app]
          • Part 4 — SDK Preview [Lemons counter app]
          • Part 5 — Integrate custom tracker into Videos Annotator tool [OpenCV Tracker]
        • Chapter 2 Modal Window
          • Part 1 — Modal window [What is it?]
          • Part 2 — States and Widgets [Customize modal window]
        • Chapter 3 UI
          • Part 1 — While True Script [It's all what you need]
          • Part 2 — UI Rendering [Simplest UI Application]
          • Part 3 — APP Handlers [Handle Events and Errors]
          • Part 4 — State and Data [Mutable Fields]
          • Part 5 — Styling your app [Customizing the UI]
        • Chapter 4 Additionals
          • Part 1 — Remote Developing with PyCharm [Docker SSH Server]
      • Custom Configuration
        • Fixing SSL Certificate Errors in Supervisely
        • Fixing 400 HTTP errors when using HTTP instead of HTTPS
      • Autostart
      • Coordinate System
      • MLOps Workflow integration
    • Widgets
      • Input
        • Input
        • InputNumber
        • InputTag
        • BindedInputNumber
        • DatePicker
        • DateTimePicker
        • ColorPicker
        • TimePicker
        • ClassesMapping
        • ClassesColorMapping
      • Controls
        • Button
        • Checkbox
        • RadioGroup
        • Switch
        • Slider
        • TrainValSplits
        • FileStorageUpload
        • Timeline
        • Pagination
      • Text Elements
        • Text
        • TextArea
        • Editor
        • Copy to Clipboard
        • Markdown
        • Tooltip
        • ElementTag
        • ElementTagsList
      • Media
        • Image
        • LabeledImage
        • GridGallery
        • Video
        • VideoPlayer
        • ImagePairSequence
        • Icons
        • ObjectClassView
        • ObjectClassesList
        • ImageSlider
        • Carousel
        • TagMetaView
        • TagMetasList
        • ImageAnnotationPreview
        • ClassesMappingPreview
        • ClassesListPreview
        • TagsListPreview
        • MembersListPreview
      • Selection
        • Select
        • SelectTeam
        • SelectWorkspace
        • SelectProject
        • SelectDataset
        • SelectItem
        • SelectTagMeta
        • SelectAppSession
        • SelectString
        • Transfer
        • DestinationProject
        • TeamFilesSelector
        • FileViewer
        • Dropdown
        • Cascader
        • ClassesListSelector
        • TagsListSelector
        • MembersListSelector
        • TreeSelect
        • SelectCudaDevice
      • Thumbnails
        • ProjectThumbnail
        • DatasetThumbnail
        • VideoThumbnail
        • FolderThumbnail
        • FileThumbnail
      • Status Elements
        • Progress
        • NotificationBox
        • DoneLabel
        • DialogMessage
        • TaskLogs
        • Badge
        • ModelInfo
        • Rate
        • CircleProgress
      • Layouts and Containers
        • Card
        • Container
        • Empty
        • Field
        • Flexbox
        • Grid
        • Menu
        • OneOf
        • Sidebar
        • Stepper
        • RadioTabs
        • Tabs
        • TabsDynamic
        • ReloadableArea
        • Collapse
        • Dialog
        • IFrame
      • Tables
        • Table
        • ClassicTable
        • RadioTable
        • ClassesTable
        • RandomSplitsTable
        • FastTable
      • Charts and Plots
        • LineChart
        • GridChart
        • HeatmapChart
        • ApexChart
        • ConfusionMatrix
        • LinePlot
        • GridPlot
        • ScatterChart
        • TreemapChart
        • PieChart
      • Compare Data
        • MatchDatasets
        • MatchTagMetas
        • MatchObjClasses
        • ClassBalance
        • CompareAnnotations
      • Widgets demos on github
  • 😎Advanced user guide
    • Objects binding
    • Automate with Python SDK & API
      • Start and stop app
      • User management
      • Labeling Jobs
  • 🖥️UI widgets
    • Element UI library
    • Supervisely UI widgets
    • Apexcharts - modern & interactive charts
    • Plotly graphing library
  • 📚API References
    • REST API Reference
    • Python SDK Reference
Powered by GitBook
On this page
  • Motivation
  • Decorator functionality
  • Main
  • Simple example
  • Inference

Was this helpful?

Edit on GitHub
  1. App development
  2. Advanced

Autostart

Autostart for your app with GUI and more

Motivation

There are situations when you want your application to launch immediately and not require any manipulation in GUI, but with a possibility to change some parameters of a launched app later. For example serving app deploys the default model which can be changed later or a labeling app starts processing some test dataset before some big project is selected. For this purpose, a simple decorator @sly.app.call_on_autostart() was created.

Decorator functionality

Main

The decorator wraps the given function so that it can only be called if the special flag in the environment is setted. This flag can be set in the modal window of an application with a simple checkbox or any part of your code using sly.env.set_autostart("1") and removed with sly.env.set_autostart(None). If you want your application to always start with setted autostart flag, add this in config.json:

"modal_template_state": {
    "autostart": true
}

Simple example

Let's create a simple app with an activate-deactivate button. Imagine that button launch some other program. We want to indicate a successful launch by changing of button color from blue to green.

import os
from dotenv import load_dotenv

import supervisely as sly
from supervisely.app.widgets import Container, Button, Card

load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))

# blue button if not activated
activate_btn = Button("Activate")

# green button if activated successfully
deactivate_btn = Button("Deactivate", button_type="success")
deactivate_btn.hide()

Add some logic and create layout for app:

# activation
@activate_btn.click
def activate_process():
    # launch some background task
    activate_btn.hide()
    deactivate_btn.show()

#deactivation
@deactivate_btn.click
def deactivate_process():
    # stop some background task
    deactivate_btn.hide()
    activate_btn.show()

btns = Container([activate_btn, deactivate_btn])
layout = Card("Activate-deactivate process", content=btns)
app = sly.Application(layout=layout)

At startup, we will see an inactive (blue) button which can be activated by clicking on it.

Now we would like to change the state of the button to active without interaction with GUI. Let's add activate_on_autostart() function, wrap it with sly.app.call_on_autostart() decorator and call at the end of our program.

@sly.app.call_on_autostart()
def activate_on_autostart():
    # launch some background task
    activate_btn.hide()
    deactivate_btn.show()

activate_on_autostart()

We will see that there are no changes in GUI.

This is because the environment variable autostart is not set. Add the environment setting before the call of activate_on_autostart().

sly.env.set_autostart("1")
activate_on_autostart()

Now our button will become active (green) immediately after start and we can control this behavior with autostart environment.

The final code:

import os
from dotenv import load_dotenv

import supervisely as sly
from supervisely.app.widgets import Container, Button, Card

load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))

# blue button if not activated
activate_btn = Button("Activate")

# green button if activated successfully
deactivate_btn = Button("Deactivate", button_type="success")
deactivate_btn.hide()


# activation
@activate_btn.click
def activate_process():
    # launch some background task
    activate_btn.hide()
    deactivate_btn.show()


#deactivation
@deactivate_btn.click
def deactivate_process():
    # stop some background task
    deactivate_btn.hide()
    activate_btn.show()


btns = Container([activate_btn, deactivate_btn])
layout = Card("Activate-deactivate process", content=btns)
app = sly.Application(layout=layout)


@sly.app.call_on_autostart()
def activate_on_autostart():
    # launch some background task
    activate_btn.hide()
    deactivate_btn.show()


sly.env.set_autostart("1")
activate_on_autostart()

Inference

if sly.is_production() and not os.environ.get("DEBUG_WITH_SLY_NET"):
    m = ClickSegModel(use_gui=True, custom_inference_settings=inference_settings_path)
    # Change default parameter from 0 to DEFAULT_ROW_IDX
    m.gui._models_table.select_row(ClickSegModel.DEFAULT_ROW_IDX)
    m.serve()
PreviousFixing 400 HTTP errors when using HTTP instead of HTTPSNextCoordinate System

Last updated 1 year ago

Was this helpful?

Not activated
Activated

Every subclass of Inference class can already deploy some default model without interaction with GUI (if any). If you use default InferenceGUI (or your GUI inherits from it), all parameters will be taken from the default widget values while autostarting. To override default parameters you can use widget functionality. The example below is taken from application: we override the default value of the RadioTable widget by calling m.gui._models_table.select_row()

🔥
ClickSeg