Metadata-Version: 2.1
Name: deepdataspace
Version: 0.3.0
Summary: A tool for CV dataset labeling, visualizing and analysing
Home-page: https://github.com/IDEA-Research/deepdataspace
Author: cvr@idea
License: UNKNOWN
Description: # DeepDataSpace
        
        
        ---
        
        <div align="center">
        <p align="center">
        
        <!-- prettier-ignore -->
        <img src="https://deepdataspace.oss-cn-shenzhen.aliyuncs.com/public/dds-log-sm.png" height="55px"> &nbsp;
        
        **The Go-To Choice for CV Data Visualization, Annotation, and Model Analysis.**
        
        ---
        
        <!-- prettier-ignore -->
        <a href="https://deepdataspace.com">Website</a> •
        <a href="https://docs.deepdataspace.com">Docs</a> •
        <a href="https://docs.deepdataspace.com/tutorials">Tutorials</a> •
        
        ![codecov](https://codecov.io/gh/deepdataspace/deepdataspace/branch/main/graph/badge.svg?token=7KEQMEAAD2)
        [![Website](https://img.shields.io/website?url=https%3A%2F%2Fdeepdataspace.com)](https://deepdataspace.com)
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
        
        [![PyPI python](https://img.shields.io/pypi/pyversions/deepdataspace)](https://pypi.org/project/deepdataspace)
        [![PyPI version](https://img.shields.io/pypi/v/deepdataspace)](https://pypi.org/project/deepdataspace)
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/deepdataspace)
        
        [![DeepDataSpace](https://user-images.githubusercontent.com/10917115/240789070-6c8b53c1-8689-41da-a978-a791b6e293ec.png)](https://deepdataspace.com)
        
        </p>
        </div>
        
        ---
        
        Deep Data Space (DDS) is an open-source dataset tool with these features out-of-box:
        
        - [x] interactive dataset visualization and exploration
        - [x] intelligent annotation with a collaborative workflow
        - [ ] efficient model management and performance analysis
        
        ## 1. Installation
        
        ### 1.1 Prerequisites
        DeepDataSpace(DDS) requires **Python 3.8 - 3.10** and runs on the following platforms:
        - Mac OS: ✅ x86/x64, ✅ arm64
        - Windows 10: ✅ x86/x64, ❌ arm64
        - Ubuntu LTS since 18.04: ✅ x86/x64, ❌ arm64
        - Docker: ✅ x86/x64, ❌ arm64
        
        ### 1.2 Installing from PyPI
        
        ```bash
        python3 -m pip install pip --upgrade
        python3 -m pip install deepdataspace
        ```
        
        ## 2. Quick Start
        
        The `dds` command will be available once the `deepdataspace` is installed, with which you can quickly start the DDS
        tool.
        
        ```bash
        dds --quickstart
        
        # Started DDS[${pid}] at http://127.0.0.1:8765.
        # The DDS tool is importing datasets inside dir in the background: $HOME/.deepdataspace/dataset-samples.
        # Explore other useful commands by: ddsop --help.
        # You can quit the DDS tool with Ctrl+C.
        ```
        
        It takes a while the first time you start the DDS tool, as it is downloading extra dependencies to set up a runtime
        environment.  
        Once the DDS tool is started, visit [http://127.0.0.1:8765](http://127.0.0.1:8765) and you will see the flowing sample datasets:  
        
        https://user-images.githubusercontent.com/10917115/240788538-f1fa8d52-7d93-4fe3-bf42-55284074febd.mp4
        
        ## 3. Alternative Installation Methods
        
        ### 3.1 Installing from Source Code
        
        ```bash
        
        # clone the source code
        git clone https://github.com/IDEA-Research/deepdataspace.git
        
        # prepare the node environment（if you haven't installed the Pnpm and Node environment yet）
        curl -fsSL https://get.pnpm.io/install.sh | sh -
        pnpm env use --global lts
        
        # compile frontend files
        pnpm i
        pnpm run build:app
        
        # copy frontend files to python package dir
        rm -rf deepdataspace/server/static/*
        cp -R packages/app/dist/* deepdataspace/server/static/
        cp deepdataspace/server/static/index.html deepdataspace/server/templates/
        
        # install the package
        python3 -m pip install pip --upgrade
        python3 -m pip install -r requirements.txt
        python3 setup.py install
        ```
        
        After the installation, you can start DDS the same way as above:
        ```
        dds --quickstart
        ```
        
        ### 3.2 Installing from Docker Image
        #### Step 1: Preparation
        
        ```shell
        # pull the latest docker image
        docker pull deepdataspace/dds
        
        # create a docker volume for dds to persistent data
        docker volume create dds-runtime
        
        # choose a visiting port for DDS
        export DDS_PORT=8765
        ```
        
        #### Step 2: Start DDS in quickstart mode
        
        ```shell
        # start the DDS in quickstart mode
        # DDS will download some sample datasets and import them
        docker run -it --name dds --rm \
            -p $DDS_PORT:8765  \
            -v dds-runtime:/dds/runtime \
            deepdataspace/dds \
            dds --quickstart -V
        ```
        
        If everything goes well, you can start visiting DDS at [http://127.0.0.1:8765](http://127.0.0.1:8765)
        
        #### Step 3: Mount your dataset directory(**Optional**)
        
        If you start DDS in `quickstart` mode, DDS will try to download the sample datasets and import them for you.  
        But most frequently, you want DDS to import your local dataset files. This is possible by mounting your local dataset directory to `/dds/datasets` inside container.
        
        ```
        # assume $PWD/datasets is your local dataset directory
        mkdir -p datasets
        
        # start the container without quickstart mode
        docker run -it --name dds --rm \
            -p 54321:8765  \
            -v dds-runtime:/dds/runtime \
            -v $PWD/datasets:/dds/datasets:ro \
            deepdataspace/dds \
            dds -V
        ```
        
        ## 4. Documentation
        
        Visit our [documentation](https://docs.deepdataspace.com) for more details on how to utilize the powers of DDS.
        
        - [Quick Start](https://docs.deepdataspace.com/quick-start)
        - [Tutorials](https://docs.deepdataspace.com/tutorials)
        - [API Reference](https://python-docs.deepdataspace.com)
        
        ## 5. Uninstallation
        
        For users who installed DDS from PyPi or source code, just uninstall DDS with `pip` and delete the runtime files. 
        ```shell
        pip uninstall deepdataspace
        
        rm -rf ~/.deepdataspace/* # use with caution, it will delete all datasets imported before
        ```
        
        For users who installed DDS from docker image, just stop the container and remove the docker image and volume.  
        ```
        docker stop dds
        docker rmi deepdataspace/dds
        docker volume remove dds-runtime # use with caution, it will delete all datasets imported before
        ```
        
        ## 6. License
        This project is released under the [Apache 2.0 License](https://github.com/IDEA-Research/deepdataspace/blob/main/LICENSE).
        ```text
        Copyright 2023-present, IDEA
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
