---
title: 'Command line interface (CLI)'
linkTitle: 'CLI'
weight: 28
description: 'Guide to working with CVAT tasks in the command line interface. This section on [GitHub](https://github.com/openvinotoolkit/cvat/tree/develop/utils/cli).'
---

## Description

A simple command line interface for working with CVAT tasks. At the moment it
implements a basic feature set but may serve as the starting point for a more
comprehensive CVAT administration tool in the future.

Overview of functionality:

- Create a new task (supports name, bug tracker, project, labels JSON, local/share/remote files)
- Delete tasks (supports deleting a list of task IDs)
- List all tasks (supports basic CSV or JSON output)
- Download JPEG frames (supports a list of frame IDs)
- Dump annotations (supports all formats via format string)
- Upload annotations for a task in the specified format (e.g. 'YOLO ZIP 1.0')
- Export and download a whole task
- Import a task

## Usage

To access the CLI, you need to have python in environment,
as well as a clone of the CVAT repository and the necessary modules:

```bash
git clone https://github.com/openvinotoolkit/cvat.git
cd cvat/utils/cli
pip install -r requirements.txt
```

You will get help with `cli.py`.

```
usage: cli.py [-h] [--auth USER:[PASS]] [--server-host SERVER_HOST]
              [--server-port SERVER_PORT] [--debug]
              {create,delete,ls,frames,dump,upload,export,import} ...

Perform common operations related to CVAT tasks.

positional arguments:
  {create,delete,ls,frames,dump,upload,export,import}

optional arguments:
  -h, --help            show this help message and exit
  --auth USER:[PASS]    defaults to the current user and supports the PASS
                        environment variable or password prompt.
  --server-host SERVER_HOST
                        host (default: localhost)
  --server-port SERVER_PORT
                        port (default: 8080)
  --https
                        using https connection (default: False)
  --debug               show debug output
```

You can get help for each positional argument, e.g. `ls`:

```bash
cli.py ls -h
```
```
usage: cli.py ls [-h] [--json]

List all CVAT tasks in simple or JSON format.

optional arguments:
  -h, --help  show this help message and exit
  --json      output JSON data
```

## Examples

### Create

Description of the options you can find in
[Creating an annotation task](/docs/manual/basics/creating_an_annotation_task/) section.

For create a task you need file contain labels in the `json` format, you can create a JSON label specification
by using the [label constructor](/docs/manual/basics/creating_an_annotation_task/#labels).
<details>
<summary>Example JSON labels file</summary>

  ```json
  [
      {
          "name": "cat",
          "attributes": []
      },
      {
          "name": "dog",
          "attributes": []
      }
  ]
  ```
</details>
<br>

- Create a task named "new task" on the default server "localhost:8080", labels from the file "labels.json"
  and local images "file1.jpg" and "file2.jpg", the task will be created as current user:
  ```bash
  cli.py create "new task" --labels labels.json local file1.jpg file2.jpg
  ```
- Create a task named "task 1" on the server "example.com" labels from the file "labels.json"
  and local image "image1.jpg", the task will be created as user "user-1":
  ```bash
  cli.py --server-host example.com --auth user-1 create "task 1" \
  --labels labels.json local image1.jpg
  ```
- Create a task named "task 1", labels from the project with id 1 and with a remote video file,
  the task will be created as user "user-1":
  ```bash
  cli.py --auth user-1:password create "task 1" --project_id 1 \
  remote https://github.com/opencv/opencv/blob/master/samples/data/vtest.avi?raw=true
  ```
- Create a task named "task 1 sort random", with labels "cat" and "dog", with chunk size 8,
  with sorting-method random, frame step 10, copy the data on the CVAT server,
  with use zip chunks and the video file will be taken from the shared resource:
  ```bash
  cli.py create "task 1 sort random" --labels '[{"name": "cat"},{"name": "dog"}]' --chunk_size 8 \
  --sorting-method random --frame_step 10 --copy_data --use_zip_chunks share //share/dataset_1/video.avi
  ```
- Create a task named "task from dataset_1", labels from the file "labels.json", with link to bug tracker,
  image quality will be reduced to 75, annotation in the format "CVAT 1.1" will be taken
  from the file "annotation.xml", the data will be loaded from "dataset_1/images/",
  the task will be created as user "user-2", and the password will need to be entered additionally:
  ```bash
  cli.py --auth user-2 create "task from dataset_1" --labels labels.json \
  --bug_tracker https://bug-tracker.com/0001 --image_quality 75 --annotation_path annotation.xml \
  --annotation_format "CVAT 1.1" local dataset_1/images/
  ```
- Create a task named "segmented task 1", labels from the file "labels.json", with overlay size 5,
  segment size 100, with frames 5 through 705, using cache and with a remote video file:
  ```bash
  cli.py create "segmented task 1" --labels labels.json --overlap 5 --segment_size 100 \
  --start_frame 5 --stop_frame 705 --use_cache \
  remote https://github.com/opencv/opencv/blob/master/samples/data/vtest.avi?raw=true
  ```
- Create a task named "task 1 with sync annotation", with label "person",
  with annotation storage in `git` repository, enable `lfs` and the image files from the shared resource:
  ```bash
  cli.py create "task 1 with sync annotation" --labels '[{"name": "person"}]' \
  --dataset_repository_url https://github.com/user/dataset/blob/main/annotation/anno_file_name.zip \
  --lfs share //share/large_dataset/images/
  ```

### Delete

- Delete tasks with id "100", "101", "102" , the command will be executed from "user-1" having delete permissions:
  ```bash
  cli.py --auth user-1:password delete 100 101 102
  ```

### List

- List all tasks:
  ```bash
  cli.py ls
  ```
- Save list of all tasks into file "list_of_tasks.json":
  ```bash
  cli.py ls --json > list_of_tasks.json
  ```

### Frames

- Save frame 12, 15, 22 from task with id 119, into "images" folder with compressed quality:
  ```bash
  cli.py frames --outdir images --quality compressed 119 12 15 22
  ```

### Dump annotation

- Dump annotation task with id 103, in the format `CVAT for images 1.1` and save to the file "output.zip":
  ```bash
  cli.py dump --format "CVAT for images 1.1" 103 output.zip
  ```
- Dump annotation task with id 104, in the format `COCO 1.0` and save to the file "output.zip":
  ```bash
  cli.py dump --format "COCO 1.0" 104 output.zip
  ```

### Upload annotation

- Upload annotation into task with id 105, in the format `CVAT 1.1` from the file "annotation.xml":
  ```bash
  cli.py upload --format "CVAT 1.1" 105 annotation.xml
  ```

### Export task

- Export task with id 136 to file "task_136.zip":
  ```bash
  cli.py export 136 task_136.zip
  ```

### Import

- Import task from file "task_backup.zip":
  ```bash
  cli.py import task_backup.zip
  ```