https://github.com/dagshub/client
DagsHub client libraries
https://github.com/dagshub/client
ai data data-science data-streaming dvc hacktoberfest hacktoberfest2023 keras machine-learning machinelearning mlops python pytorch tensorflow
Last synced: about 1 month ago
JSON representation
DagsHub client libraries
- Host: GitHub
- URL: https://github.com/dagshub/client
- Owner: DagsHub
- License: mit
- Created: 2019-11-12T12:39:54.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-05-12T14:55:15.000Z (about 2 months ago)
- Last Synced: 2025-05-16T10:06:04.553Z (about 1 month ago)
- Topics: ai, data, data-science, data-streaming, dvc, hacktoberfest, hacktoberfest2023, keras, machine-learning, machinelearning, mlops, python, pytorch, tensorflow
- Language: Python
- Homepage: https://dagshub.com/docs/client/
- Size: 3.57 MB
- Stars: 93
- Watchers: 7
- Forks: 23
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://dagshub.com)
[](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/).