From scratch - advanced

A step-by-step tutorial of how to create custom export app without using template from SDK (from scratch).


It is more recommended way to use SDK export template class to create custom export app (we learned it in the previous tutorial - learn more here). However, if your use case is not covered by our export template, you can create your own app from scratch without the template.

We will go through the following steps:

Step 0. Set up the working environment.

Step 1 How to write an export script.

Step 2. How to debug export app.

Step 3. Advanced debug.

Step 4. How to run it in Supervisely.

Everything you need to reproduce this tutorial is on GitHub: source code and additional app files.

Overview of the simple (illustrative) example we will use in tutorial

In this tutorial, we will create a custom export app that exports data from Supervisely into a .tar archive. See the overview of this example here

Step 0. Set up the working environment

Before we begin, please clone this export-custom-format repository and set up the working environment - here is a link with a description of the steps.

Step 1. How to write an export script

Find source code for this example here

Step 1. Import libraries

import json, os
import supervisely as sly

from dotenv import load_dotenv
from tqdm import tqdm

Step 2. Load environment variables

Load ENV variables for debug, has no effect in production


Get variables

TASK_ID = sly.env.task_id()
TEAM_ID = sly.env.team_id()
WORKSPACE_ID = sly.env.workspace_id()
PROJECT_ID = sly.env.project_id()
DATASET_ID = sly.env.dataset_id(raise_not_found=False)
IS_PRODUCTION = sly.is_production()

Step 3. Write Export script

STORAGE_DIR =  # path to directory for temp files and result archive
PROJECT_DIR = os.path.join(STORAGE_DIR, str(PROJECT_ID))  # project directory path, True)
ANN_FILE_NAME = "labels.json"

app = sly.Application() # run app

# get project info from server
project_info = api.project.get_info_by_id(id=PROJECT_ID)

if project_info is None:
    raise ValueError(
        f"Project with ID: '{PROJECT_ID}' either doesn't exist, archived or you don't have access to it"
    f"Exporting Project: id={}, name={}, type={project_info.type}",

meta_json = api.project.get_meta(id=PROJECT_ID)
project_meta = sly.ProjectMeta.from_json(meta_json)

# Check if the app runs from the context menu of the dataset.
if DATASET_ID is not None:
    # If so, get the dataset info from the server.
    dataset_infos = [api.dataset.get_info_by_id(DATASET_ID)]
    if dataset_infos is None:
        raise ValueError(
            f"Dataset with ID: '{DATASET_ID}' either doesn't exist, archived or you don't have access to it"
        )"Exporting Dataset: id={dataset_infos[0].id}, name={dataset_infos[0].name}")
    # If it does not, obtain all datasets infos from the current project.
    dataset_infos = api.dataset.get_list(PROJECT_ID)"Exporting all datasets from project.")

# track progress datasets processing using Tqdm
with tqdm(total=len(dataset_infos)) as ds_pbar:
    # iterate over datasets in project
    for dataset in dataset_infos:
        result_anns = {}

        # get dataset images info
        images_infos = api.image.get_list(

        # track progress using Tqdm
        with tqdm(total=dataset.items_count) as pbar:
            # iterate over images in dataset
            for image_info in images_infos:
                labels = []

                # create path for each image and download it from server
                image_path = os.path.join(PROJECT_DIR,,
      , image_path)

                # download annotation for current image
                ann_json = api.annotation.download_json(
                ann = sly.Annotation.from_json(ann_json, project_meta)

                # iterate over labels in current annotation
                for label in ann.labels:
                    # get obj class name
                    name =

                    # get bounding box coordinates for label
                    bbox = label.geometry.to_bbox()
                            "class_name": name,
                            "coordinates": [

                result_anns[] = labels

                # increment the current images progress counter by 1
        # increment the current dataset progress counter by 1

        # create JSON annotation in new format
        filename = os.path.join(PROJECT_DIR,, ANN_FILE_NAME)
        with open(filename, "w") as file:
            json.dump(result_anns, file, indent=2)

# prepare archive from result project dir
archive_path = f"{PROJECT_DIR}.tar"
sly.fs.archive_directory(PROJECT_DIR, archive_path)
PROJECT_DIR = archive_path

# upload project to Supervsiely in production mode 
    progress = tqdm(
        desc=f"Uploading '{os.path.basename(PROJECT_DIR)}'",
    remote_path = os.path.join(
        "Supervisely App",

    file_info = api.file.upload(
    )"Remote file: id={}, name={}")
    sly.fs.silent_remove(PROJECT_DIR) # remove local directory

app.shutdown() # stop app

Step 2. How to debug export app

In this tutorial, we will be using the Run & Debug section of the VSCode to debug our export app.

The export template has 2 launch options for debugging: Debug and Advanced Debug. The settings for these options are configured in the launch.json file. Lets start from oprion #1 - Debug

This option is a good starting point. In this case, the resulting archive or folder with the exported data will remain on your computer and be saved in the path that we defined in the local.env file (SLY_APP_DATA_DIR="results/").

Output of this python program:

{"message": "Exporting Project: id=20934, name=Model predictions, type=images", "timestamp": "2023-05-08T11:30:06.341Z", "level": "info"}
{"message": "Exporting Dataset: id=64895, name=Week # 1", "timestamp": "2023-05-08T11:30:06.651Z", "level": "info"}
Processing: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 6/6 [00:06<00:00,  1.12s/it]

Step 3. Advanced debug

In addition to the regular debug option, this template also includes setting for Advanced debugging.

The advanced debugging option is somewhat identical, however it will upload result archive or folder with data to Team Files instead (Path to result archive - /tmp/supervisely/export/Supervisely App/<SESSION ID>/<PROJECT_ID>_<PROJECT_NAME>.tar). This option is an example of how production apps work in Supervisely platform.

Output of this python program:

{"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Processing", "current": 0, "total": 1, "timestamp": "2023-05-08T17:25:24.404Z", "level": "info"}
{"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Processing", "current": 0, "total": 6, "timestamp": "2023-05-08T17:25:24.726Z", "level": "info"}
{"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Processing", "current": 1, "total": 6, "timestamp": "2023-05-08T17:25:25.949Z", "level": "info"}
{"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Processing", "current": 1, "total": 1, "timestamp": "2023-05-08T17:25:33.269Z", "level": "info"}
{"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Uploading '20934.tar'", "current": 0, "total": 4567440, "current_label": "0.0 B", "total_label": "0.0 B", "timestamp": "2023-05-08T17:26:05.111Z", "level": "info"}
{"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Uploading '20934.tar'", "current": 4567440, "total": 4567440, "current_label": "4.4 MiB", "total_label": "4.4 MiB", "timestamp": "2023-05-08T17:13:19.529Z", "level": "info"}
{"message": "Remote file: id=1274760, name=20934.tar", "timestamp": "2023-05-08T17:13:20.646Z", "level": "info"}

Step 4. How to run it in Supervisely

Submitting an app to the Supervisely Ecosystem isn’t as simple as pushing code to github repository, but it’s not as complicated as you may think of it either.

Please follow this link for instructions on adding your app. We have produced a step-by-step guide on how to add your application to the Supervisely Ecosystem.

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