diff --git a/doc/source/raysgd/raysgd.rst b/doc/source/raysgd/raysgd.rst index 939e1499e..7e5478691 100644 --- a/doc/source/raysgd/raysgd.rst +++ b/doc/source/raysgd/raysgd.rst @@ -7,6 +7,8 @@ RaySGD: Distributed Deep Learning RaySGD is a lightweight library for distributed deep learning, providing thin wrappers around framework-native modules for data parallel training. +.. tip:: Help us make RaySGD better; take this 1 minute `User Survey `_! + The main features are: - Ease of use: Scale Pytorch's native ``DistributedDataParallel`` and TensorFlow's ``tf.distribute.MirroredStrategy`` without needing to monitor individual nodes. diff --git a/doc/source/raysgd/raysgd_pytorch.rst b/doc/source/raysgd/raysgd_pytorch.rst index 409943fde..3f8ace1d2 100644 --- a/doc/source/raysgd/raysgd_pytorch.rst +++ b/doc/source/raysgd/raysgd_pytorch.rst @@ -3,6 +3,8 @@ RaySGD Pytorch .. warning:: This is still an experimental API and is subject to change in the near future. +.. tip:: Help us make RaySGD better; take this 1 minute `User Survey `_! + Ray's ``PyTorchTrainer`` simplifies distributed model training for PyTorch. The ``PyTorchTrainer`` is a wrapper around ``torch.distributed.launch`` with a Python API to easily incorporate distributed training into a larger Python application, as opposed to needing to execute training outside of Python. ---------- diff --git a/doc/source/raysgd/raysgd_tensorflow.rst b/doc/source/raysgd/raysgd_tensorflow.rst index 585735657..dde5e92f2 100644 --- a/doc/source/raysgd/raysgd_tensorflow.rst +++ b/doc/source/raysgd/raysgd_tensorflow.rst @@ -1,6 +1,10 @@ RaySGD TensorFlow ================= +.. warning:: This is still an experimental API and is subject to change in the near future. + +.. tip:: Help us make RaySGD better; take this 1 minute `User Survey `_! + RaySGD's ``TFTrainer`` simplifies distributed model training for Tensorflow. The ``TFTrainer`` is a wrapper around ``MultiWorkerMirroredStrategy`` with a Python API to easily incorporate distributed training into a larger Python application, as opposed to write custom logic of setting environments and starting separate processes. .. important:: This API has only been tested with TensorFlow2.0rc and is still highly experimental. Please file bug reports if you run into any - thanks! diff --git a/doc/source/tune-tutorial.rst b/doc/source/tune-tutorial.rst index 71cb40626..14b41c17b 100644 --- a/doc/source/tune-tutorial.rst +++ b/doc/source/tune-tutorial.rst @@ -1,8 +1,6 @@ Tune Walkthrough ================ -.. tip:: Help make Tune better by taking our 3 minute `Ray Tune User Survey `_! - This tutorial will walk you through the following process to setup a Tune experiment. Specifically, we'll leverage ASHA and Bayesian Optimization (via HyperOpt) via the following steps: 1. Integrating Tune into your workflow diff --git a/doc/source/tune-usage.rst b/doc/source/tune-usage.rst index ffc96852e..04c1bd08b 100644 --- a/doc/source/tune-usage.rst +++ b/doc/source/tune-usage.rst @@ -1,8 +1,6 @@ Tune User Guide =============== -.. tip:: Help make Tune better by taking our 3 minute `Ray Tune User Survey `_! - Tune Overview ------------- diff --git a/doc/source/tune.rst b/doc/source/tune.rst index 06c1931d6..98fd96eab 100644 --- a/doc/source/tune.rst +++ b/doc/source/tune.rst @@ -1,8 +1,6 @@ Tune: A Scalable Hyperparameter Tuning Library ============================================== -.. tip:: Help make Tune better by taking our 3 minute `Ray Tune User Survey `_! - .. image:: images/tune.png :scale: 30% :align: center