[](https://dagshub.com)
<br/>
[](https://github.com/DAGsHub/client/actions/workflows/python-package.yml)
[](https://pypi.org/project/dagshub)
[](/LICENSE)
[](https://dagshub.com/docs)
[](https://dagshub.com/docs/client)
[](https://dagshub.com/user/sign_up?redirect_to=)
[](https://discord.com/invite/9gU36Y6)
[](https://twitter.com/TheRealDAGsHub)
# What is DagsHub?
**DagsHub** is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
1. **Version code, data, and models** in one place. Use the free provided DagsHub storage or connect it to your cloud storage
2. **Track Experiments** using Git, DVC or MLflow, to provide a fully reproducible environment
3. **Visualize** pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
4. **Label** your data directly on the platform using Label Studio
5. **Share** your work with your team members
6. **Stream and upload** your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
* Git
* [DVC](https://github.com/iterative/dvc)
* [MLflow](https://github.com/mlflow/mlflow)
* [Label Studio](https://github.com/heartexlabs/label-studio)
* Standard data formats like YAML, JSON, CSV
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
# DagsHub Client API & CLI
__This client library is meant to help you get started quickly with DagsHub__. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
1. [Installation & Setup](https://github.com/DagsHub/client/blob/master/docs/index.md#installation-and-setup)
2. [Data Streaming](https://github.com/DagsHub/client/blob/master/docs/index.md#data-streaming)
3. [Data Upload](https://github.com/DagsHub/client/blob/master/docs/index.md#data-upload)
4. [Experiment Tracking](https://github.com/DagsHub/client/blob/master/docs/index.md#experiment-tracking-logger)
1. [Autologging](https://github.com/DagsHub/client/blob/master/docs/index.md#autologging-integrations-with-ml-frameworks)
5. [Data Engine](https://github.com/DagsHub/client/blob/master/docs/data_engine.md)
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the [relevant doc page](https://dagshub.com/docs/feature_guide/direct_data_access/#use-cases).
## Installation
```bash
pip install dagshub
```
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
```bash
dagshub login
```
## Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
1. Your DagsHub project,
2. Copy the following 2 lines of code into your Python code which accesses your data:
```python
from dagshub.streaming import install_hooks
install_hooks()
```
3. That’s it! You now have streaming access to all your project files.
**🤩 Check out this colab to see an example of this Data Streaming work end to end:**
[](https://colab.research.google.com/drive/1CtBmcDtZnxZKVIhNvPagX-8UFWHZ5HAg?usp=sharing)
## Next Steps
You can dive into the expanded [documentation](docs/index.md), to learn more about data streaming, data upload and
experiment tracking with DagsHub
---
### Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the `DAGSHUB_DISABLE_ANALYTICS` environment variable to any value.
Made with 🐶 by [DagsHub](https://dagshub.com/).
Raw data
{
"_id": null,
"home_page": "https://github.com/DagsHub/client",
"name": "dagshub",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": null,
"author": "DagsHub",
"author_email": "contact@dagshub.com",
"download_url": "https://files.pythonhosted.org/packages/04/e0/3f3427ec7fed1f147c4f8c370b633e355b9d8c836145dd46a183e030cbd7/dagshub-0.4.2.tar.gz",
"platform": null,
"description": "[](https://dagshub.com)\n\n<br/>\n\n[](https://github.com/DAGsHub/client/actions/workflows/python-package.yml)\n[](https://pypi.org/project/dagshub)\n[](/LICENSE)\n\n[](https://dagshub.com/docs)\n[](https://dagshub.com/docs/client)\n\n\n[](https://dagshub.com/user/sign_up?redirect_to=)\n[](https://discord.com/invite/9gU36Y6)\n[](https://twitter.com/TheRealDAGsHub)\n\n# What is DagsHub?\n**DagsHub** is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.\nWith DagsHub you can:\n1. **Version code, data, and models** in one place. Use the free provided DagsHub storage or connect it to your cloud storage\n2. **Track Experiments** using Git, DVC or MLflow, to provide a fully reproducible environment\n3. **Visualize** pipelines, data, and notebooks in and interactive, diff-able, and dynamic way\n4. **Label** your data directly on the platform using Label Studio\n5. **Share** your work with your team members\n6. **Stream and upload** your data in an intuitive and easy way, while preserving versioning and structure.\n\nDagsHub is built firmly around open, standard formats for your project. In particular:\n* Git\n* [DVC](https://github.com/iterative/dvc)\n* [MLflow](https://github.com/mlflow/mlflow)\n* [Label Studio](https://github.com/heartexlabs/label-studio)\n* Standard data formats like YAML, JSON, CSV\n\nTherefore, you can work with DagsHub regardless of your chosen programming language or frameworks.\n\n# DagsHub Client API & CLI\n\n__This client library is meant to help you get started quickly with DagsHub__. It is made up of Experiment tracking and\nDirect Data Access (DDA), a component to let you stream and upload your data.\n\nFor more details on the different functions of the client, check out the docs segments:\n1. [Installation & Setup](https://github.com/DagsHub/client/blob/master/docs/index.md#installation-and-setup)\n2. [Data Streaming](https://github.com/DagsHub/client/blob/master/docs/index.md#data-streaming)\n3. [Data Upload](https://github.com/DagsHub/client/blob/master/docs/index.md#data-upload)\n4. [Experiment Tracking](https://github.com/DagsHub/client/blob/master/docs/index.md#experiment-tracking-logger)\n 1. [Autologging](https://github.com/DagsHub/client/blob/master/docs/index.md#autologging-integrations-with-ml-frameworks)\n5. [Data Engine](https://github.com/DagsHub/client/blob/master/docs/data_engine.md)\n\nSome functionality is supported only in Python.\n\nTo read about some of the awesome use cases for Direct Data Access, check out\nthe [relevant doc page](https://dagshub.com/docs/feature_guide/direct_data_access/#use-cases).\n\n## Installation\n```bash\npip install dagshub\n```\n\nDirect Data Access (DDA) functionality requires authentication, which you can easily do by running the following command\nin your terminal:\n```bash\ndagshub login\n```\n\n## Quickstart for Data Streaming\nThe easiest way to start using DagsHub is via the Python Hooks method. To do this:\n1. Your DagsHub project,\n2. Copy the following 2 lines of code into your Python code which accesses your data:\n ```python\n from dagshub.streaming import install_hooks\n install_hooks()\n ```\n3. That\u2019s it! You now have streaming access to all your project files.\n\n**\ud83e\udd29 Check out this colab to see an example of this Data Streaming work end to end:**\n\n[](https://colab.research.google.com/drive/1CtBmcDtZnxZKVIhNvPagX-8UFWHZ5HAg?usp=sharing)\n\n## Next Steps\nYou can dive into the expanded [documentation](docs/index.md), to learn more about data streaming, data upload and\nexperiment tracking with DagsHub\n\n---\n\n\n### Analytics\nTo improve your experience, we collect analytics on client usage. If you want to disable analytics collection,\nset the `DAGSHUB_DISABLE_ANALYTICS` environment variable to any value.\n\nMade with \ud83d\udc36 by [DagsHub](https://dagshub.com/).\n",
"bugtrack_url": null,
"license": null,
"summary": "DagsHub client libraries",
"version": "0.4.2",
"project_urls": {
"Homepage": "https://github.com/DagsHub/client"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "6426d5e9d29f547dd90bc1f1c176a184f29dff49b5f6fe7ae29436ab2e8e002d",
"md5": "7bcfa9650db1ac49394c180c661549b7",
"sha256": "1071bfbcd7ca5e3a3d193b1be7d6805d97fd5733305e96d13e29bd1bddaaf08a"
},
"downloads": -1,
"filename": "dagshub-0.4.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7bcfa9650db1ac49394c180c661549b7",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 255559,
"upload_time": "2024-12-24T10:46:11",
"upload_time_iso_8601": "2024-12-24T10:46:11.993688Z",
"url": "https://files.pythonhosted.org/packages/64/26/d5e9d29f547dd90bc1f1c176a184f29dff49b5f6fe7ae29436ab2e8e002d/dagshub-0.4.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "04e03f3427ec7fed1f147c4f8c370b633e355b9d8c836145dd46a183e030cbd7",
"md5": "9d205ab3b3e6dcd1525008177b862bb4",
"sha256": "f7d8a4f7e2777788a3a8bc65c204f7aa11b1836111d39cbfab9249e737857991"
},
"downloads": -1,
"filename": "dagshub-0.4.2.tar.gz",
"has_sig": false,
"md5_digest": "9d205ab3b3e6dcd1525008177b862bb4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 231619,
"upload_time": "2024-12-24T10:46:15",
"upload_time_iso_8601": "2024-12-24T10:46:15.154914Z",
"url": "https://files.pythonhosted.org/packages/04/e0/3f3427ec7fed1f147c4f8c370b633e355b9d8c836145dd46a183e030cbd7/dagshub-0.4.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-24 10:46:15",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "DagsHub",
"github_project": "client",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "dagshub"
}
JSON
