doc: added links to RTD in README.me, some restructuring

README.md

@@ -1,21 +1,29 @@
-![dmlcloud logo](./misc/logo/dmlcloud_color.png)
+![Dmlcloud Logo](./misc/logo/dmlcloud_color.png)
 ---------------
-[![](https://img.shields.io/pypi/v/dmlcloud)](https://pypi.org/project/dmlcloud/)
-[![](https://img.shields.io/github/actions/workflow/status/sehoffmann/dmlcloud/run_tests.yml?label=tests&logo=github)](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_tests.yml)
-[![](https://img.shields.io/github/actions/workflow/status/sehoffmann/dmlcloud/run_linting.yml?label=lint&logo=github)](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_linting.yml)
+[![PyPI Status](https://img.shields.io/pypi/v/dmlcloud)](https://pypi.org/project/dmlcloud/)
+[![Documentation Status](https://readthedocs.org/projects/dmlcloud/badge/?version=latest)](https://dmlcloud.readthedocs.io/en/latest/?badge=latest)
+[![Test Status](https://img.shields.io/github/actions/workflow/status/sehoffmann/dmlcloud/run_tests.yml?label=tests&logo=github)](https://github.com/sehoffmann/dmlcloud/actions/workflows/run_tests.yml)
 
-*Flexibel, easy-to-use, opinionated*
+A torch library for easy distributed deep learning on HPC clusters. Supports both slurm and MPI. No unnecessary abstractions and overhead. Simple, yet powerful, API.
 
-*dmlcloud* is a library for **distributed training** of deep learning models with *torch*. Unlike other similar frameworks, dmcloud adds as little additional complexity and abstraction as possible. It is tailored towards a carefully selected set of libraries and workflows.
+## Highlights
+- Simple, yet powerful, API
+- Easy initialization of `torch.distributed`
+- Distributed checkpointing and metrics
+- Extensive logging and diagnostics
+- Wandb support
+- A wealth of useful utility functions
 
 ## Installation
 ```
 pip install dmlcloud
 ```
 
-## Why dmlcloud?
-- Easy initialization of `torch.distributed` (supports *slurm* and *MPI*).
-- Simple, yet powerful, API. No unnecessary abstractions and complications.
-- Checkpointing and metric tracking (distributed)
-- Extensive logging and diagnostics out-of-the-box. Greatly improve reproducability and traceability.
-- A wealth of useful utility functions required for distributed training (e.g. for data set sharding)
+## Minimal Example
+*TODO*
+
+## Documentation
+
+You can find the official documentation at [Read the Docs](https://dmlcloud.readthedocs.io/en/latest/)
+
+