docs: Revise README and docs landing page
We probably want to add some more content to both places (the new content is
fairly short), but for now it's important to keep the GitHub/docs messaging
aligned with what's on the website.
Overview
**********
-Determined is an open-source Deep Learning Training Platform that
-incorporates cutting-edge research and years of practical experience to
-help deep learning teams **train models more quickly, easily share GPU
-resources, and effectively collaborate**. Determined allows deep
-learning engineers to focus on building and training models at scale,
-without needing to worry about DevOps or writing custom code for common
-tasks like fault tolerance or experiment tracking. More information
-about Determined can be found on the `website
-<https://determined.ai/>`_.
+Determined is an open-source deep learning training platform that makes building
+models fast and easy. Determined enables you to:
-**************************************
- What does Determined handle for you?
-**************************************
+- **Train models faster** using state-of-the-art :ref:`distributed training
+ <multi-gpu-training>`, without changing your model code
-You can think of Determined as a platform that bridges the gap between
-tools like TensorFlow and PyTorch---which work great for a single
-researcher with a single GPU---and the challenges that arise when doing
-deep learning at scale, as teams, clusters, and data sets all increase
-in size.
+- **Automatically find high-quality models** with advanced :ref:`hyperparameter
+ tuning <hyperparameter-tuning>` from the creators of Hyperband
-Determined's key capabilities include:
+- **Get more from your GPUs** with smart scheduling and cut cloud GPU costs by
+ seamlessly using preemptible instances
-- high-performance :ref:`distributed training <multi-gpu-training>`
- without any additional changes to your model code
+- **Track and reproduce your work** with experiment tracking that works
+ out-of-the-box, covering code versions, metrics, checkpoints, and
+ hyperparameters
-- intelligent :ref:`hyperparameter tuning <hyperparameter-tuning>`
- based on cutting-edge research
+Determined integrates these features into an easy-to-use, high-performance deep
+learning environment --- which means you can spend your time building models
+instead of managing infrastructure.
-- flexible GPU scheduling, including dynamically resizing training jobs
- on-the-fly, automatic management of cloud resources on AWS and GCP,
- and optional :ref:`support for Kubernetes <determined-on-kubernetes>`
+To use Determined, you can continue using popular DL frameworks such as
+TensorFlow and PyTorch; you just need to update your model code to integrate
+with the Determined API.
-- built-in experiment tracking, metrics visualization, and :ref:`model
- registry <organizing-models>`
-
-- automatic fault tolerance for deep learning training jobs
-
-- integrated support for :ref:`TensorBoard <how-to-tensorboard>` and
- GPU-powered :ref:`Jupyter notebooks <how-to-notebooks>`
-
-To use Determined, you can continue using popular deep learning
-frameworks such as TensorFlow and PyTorch; you just need to modify your
-model code to implement the Determined API.
+More information about Determined can be found on the `website
+<https://determined.ai/>`__.
*************
Quick Start
