Website edits (#398)

Co-authored-by: Jeff Rasley <jerasley@microsoft.com>

README.md

@@ -37,7 +37,7 @@ information [here](https://innovation.microsoft.com/en-us/exploring-ai-at-scale)
   * [Up to 5x less communication and 3.4x faster training through 1-bit Adam](https://www.deepspeed.ai/news/2020/09/08/onebit-adam-news.html)
   * [10x bigger model training on a single GPU with ZeRO-Offload](https://www.deepspeed.ai/news/2020/09/08/ZeRO-Offload.html)
 * [2020/08/07] [DeepSpeed Microsoft Research Webinar](https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html) is now available on-demand
-* [2020/07/24] [DeepSpeed Microsoft Research Webinar](https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html) on August 6th, 2020 
+* [2020/07/24] [DeepSpeed Microsoft Research Webinar](https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html) on August 6th, 2020
   [![DeepSpeed webinar](docs/assets/images/webinar-aug2020.png)](https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-Live.html)
 * [2020/05/19] [ZeRO-2 & DeepSpeed: Shattering Barriers of Deep Learning Speed & Scale](https://www.microsoft.com/en-us/research/blog/zero-2-deepspeed-shattering-barriers-of-deep-learning-speed-scale/)
 * [2020/05/19] [An Order-of-Magnitude Larger and Faster Training with ZeRO-2](https://www.deepspeed.ai/news/2020/05/18/zero-stage2.html)
@@ -88,7 +88,7 @@ overview](https://www.deepspeed.ai/features/) for descriptions and usage.
   * Support 10B model training on a single GPU
 * [Ultra-fast dense transformer kernels](https://www.deepspeed.ai/news/2020/05/18/bert-record.html)
 * [Sparse attention](https://www.deepspeed.ai/news/2020/09/08/sparse-attention.html)
-  * Memory- and compute-efficient sparse kernels 
+  * Memory- and compute-efficient sparse kernels
   * Support 10x long sequences than dense
   * Flexible support to different sparse structures
 * [1-bit Adam](https://www.deepspeed.ai/news/2020/09/08/onebit-adam-blog-post.html)

docs/_pages/features.md

@@ -30,17 +30,22 @@ deepspeed --hostfile=<hostfile> \
 ```
 The script `<client_entry.py>` will execute on the resources specified in `<hostfile>`.
 
+## Pipeline Parallelism
+DeepSpeed provides [pipeline parallelism](/tutorials/pipeline/) for memory-
+and communication- efficient training. DeepSpeed supports a hybrid
+combination of data, model, and pipeline parallelism and has scaled to over
+[one trillion parameters using 3D parallelism]({{ site.press_release_v3 }}).
+Pipeline parallelism can also improve communication efficiency and has
+accelerated training by up to 7x on low-banwdith clusters.
 
-## Model Parallelism
 
+## Model Parallelism
 ### Support for Custom Model Parallelism
-DeepSpeed supports all forms of model parallelism including tensor slicing based
-approaches such as the [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), or
-pipelined parallelism approaches such as
-[PipeDream](https://github.com/msr-fiddle/pipedream) and
-[GPipe](https://github.com/kakaobrain/torchgpipe). It does so by only requiring the model
-parallelism framework to provide a *model parallelism unit* (`mpu`) that implements a few
-bookkeeping functionalities:
+DeepSpeed supports all forms of model parallelism including tensor slicing
+based approaches such as the
+[Megatron-LM](https://github.com/NVIDIA/Megatron-LM). It does so by only
+requiring the model parallelism framework to provide a *model parallelism
+unit* (`mpu`) that implements a few bookkeeping functionalities:
 
 ```python
 mpu.get_model_parallel_rank()
@@ -57,6 +62,8 @@ DeepSpeed is fully compatible with [Megatron](https://github.com/NVIDIA/Megatron
 Please see the [Megatron-LM tutorial](/tutorials/megatron/) for details.
 
 
+
+
 ## The Zero Redundancy Optimizer
 The Zero Redundancy Optimizer ([ZeRO](https://arxiv.org/abs/1910.02054)) is at
 the heart of DeepSpeed and enables large model training at a scale that is

docs/_posts/2020-09-09-ZeRO-Offload.md

@@ -7,7 +7,7 @@ new_post: true
 date: 2020-09-09 00:00:00
 ---
 
-We introduce a new technology called ZeRO-Offload to enable **10X bigger model training on a single GPU**. ZeRO-Offload extends ZeRO-2 to leverage both CPU and GPU memory for training large models. Using a machine with **a single GPU**, our users now can run **models of up to 13 billion parameters** without running out of memory, 10x bigger than the existing approaches, while obtaining competitive throughput. This feature democratizes multi-billion-parameter model training and opens the window for many deep learning practitioners to explore bigger and better models. 
+We introduce a new technology called ZeRO-Offload to enable **10X bigger model training on a single GPU**. ZeRO-Offload extends ZeRO-2 to leverage both CPU and GPU memory for training large models. Using a machine with **a single GPU**, our users now can run **models of up to 13 billion parameters** without running out of memory, 10x bigger than the existing approaches, while obtaining competitive throughput. This feature democratizes multi-billion-parameter model training and opens the window for many deep learning practitioners to explore bigger and better models.
 
 * For more information on ZeRO-Offload, see our [press release]( {{ site.press_release_v3 }} ).
 * For more information on how to use ZeRO-Offload, see our [ZeRO-Offload tutorial](https://www.deepspeed.ai/tutorials/zero-offload/).

docs/index.md

@@ -30,7 +30,7 @@ information [here](https://innovation.microsoft.com/en-us/exploring-ai-at-scale)
 # What's New?
 * [2020/09/10] [DeepSpeed: Extreme-scale model training for everyone]({{ site.press_release_v3 }})
   * [Powering 10x longer sequences and 6x faster execution through DeepSpeed Sparse Attention](https://www.deepspeed.ai/news/2020/09/08/sparse-attention-news.html)
-  * [Training a trillion parameters with pipeline parallelism](https://www.deepspeed.ai/news/2020/09/09/pipeline-parallelism.html)
+  * [Training a trillion parameters with pipeline parallelism](https://www.deepspeed.ai/news/2020/09/08/pipeline-parallelism.html)
   * [Up to 5x less communication and 3.4x faster training through 1-bit Adam](https://www.deepspeed.ai/news/2020/09/08/onebit-adam-news.html)
   * [10x bigger model training on a single GPU with ZeRO-Offload](https://www.deepspeed.ai/news/2020/09/08/ZeRO-Offload.html)
 
@@ -168,7 +168,7 @@ Below we provide a brief feature list, see our detailed [feature overview](https
   * Support 10B model training on a single GPU
 * [Ultra-fast dense transformer kernels](https://www.deepspeed.ai/news/2020/05/18/bert-record.html)
 * [Sparse attention](https://www.deepspeed.ai/news/2020/09/08/sparse-attention.html)
-  * Memory- and compute-efficient sparse kernels 
+  * Memory- and compute-efficient sparse kernels
   * Support 10x long sequences than dense
   * Flexible support to different sparse structures
 * [1-bit Adam](https://www.deepspeed.ai/news/2020/09/08/onebit-adam-blog-post.html)