Improve README

README.md

@@ -13,15 +13,54 @@
 
 We developed `StreamingDataset` to optimize training of large datasets stored on the cloud while prioritizing speed, affordability, and scalability.
 
-Specifically crafted for multi-node, distributed training with large models, it enhances accuracy, performance, and user-friendliness. Now, training efficiently is possible regardless of the data's location. Simply stream in the required data when needed.
+Specifically crafted for multi-gpu & multi-node (with [DDP](https://lightning.ai/docs/pytorch/stable/accelerators/gpu_intermediate.html), [FSDP](https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/fsdp.html), etc...), distributed training with large models, it enhances accuracy, performance, and user-friendliness. Now, training efficiently is possible regardless of the data's location. Simply stream in the required data when needed.
 
-The `StreamingDataset` is compatible with any data type, including **images, text, video, and multimodal data** and it is a drop-in replacement for your PyTorch [IterableDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset) class. For example, it is used by [Lit-GPT](https://github.com/Lightning-AI/lit-gpt/blob/main/pretrain/tinyllama.py) to pretrain LLMs.
+The `StreamingDataset` is compatible with any data type, including **images, text, video, audio, geo-spatial, and multimodal data** and it is a drop-in replacement for your PyTorch [IterableDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset) class. For example, it is used by [Lit-GPT](https://github.com/Lightning-AI/lit-gpt/blob/main/pretrain/tinyllama.py) to pretrain LLMs.
 
-Finally, the `StreamingDataset` is fast! Check out our [benchmark](https://lightning.ai/lightning-ai/studios/benchmark-cloud-data-loading-libraries).
+# 🚀 Benchmarks
 
-Here is an illustration showing how the `StreamingDataset` works.
+[Imagenet-1.2M](https://www.image-net.org/) is a commonly used dataset to compare computer vision models. Its training dataset contains `1,281,167 images`. 
 
-![An illustration showing how the Streaming Dataset works.](https://pl-flash-data.s3.amazonaws.com/streaming_dataset.gif)
+In this benchmark, we measured the streaming speed (`images per second`) loaded from [AWS S3](https://aws.amazon.com/s3/) for several frameworks. 
+
+Find the full reproducible [Lightning Studio Benchmark](https://lightning.ai/) [here](https://lightning.ai/lightning-ai/studios/benchmark-cloud-data-loading-libraries).
+
+### Imagenet-1.2M Streaming from AWS S3
+
+| Framework | Images / sec  1st Epoch (float32)  | Images / sec   2nd Epoch (float32) | Images / sec 1st Epoch (torch16) | Images / sec 2nd Epoch (torch16) |
+|---|---|---|---|---|
+| PL Data  | ${\textbf{\color{Fuchsia}5800.34}}$ | ${\textbf{\color{Fuchsia}6589.98}}$  | ${\textbf{\color{Fuchsia}6282.17}}$  | ${\textbf{\color{Fuchsia}7221.88}}$  |
+| Web Dataset  | 3134.42 | 3924.95 | 3343.40 | 4424.62 |
+| Mosaic ML  | 2898.61 | 5099.93 | 2809.69 | 5158.98 |
+
+Higher is better.
+
+### Imagenet-1.2M Conversion
+
+| Framework |Train Conversion Time | Val Conversion Time | Dataset Size | # Files |
+|---|---|---|---|---|
+| PL Data  |  ${\textbf{\color{Fuchsia}10:05 min}}$ | ${\textbf{\color{Fuchsia}00:30 min}}$ |  ${\textbf{\color{Fuchsia}143.1 GB}}$  | 2.339  |
+| Web Dataset  | 32:36 min | 01:22 min | 147.8 GB | 1.144 |
+| Mosaic ML  | 49:49 min | 01:04 min | ${\textbf{\color{Fuchsia}143.1 GB}}$ | 2.298 |
+
+The dataset needs to be converted into an optimized format for cloud streaming. We measured how fast the 1.2 million images are converted.
+
+Faster is better.
+
+# 📚 Real World Examples
+
+We have built end-to-end free [Studios](https://lightning.ai) showing all the steps to prepare the following datasets:
+
+| Dataset                                                                                                                                      |      Data type      |                                                                                                                                  Studio |
+| -------------------------------------------------------------------------------------------------------------------------------------------- | :-----------------: | --------------------------------------------------------------------------------------------------------------------------------------: |
+| [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/)                                                                                  | Image & description |            [Use or explore LAION-400MILLION dataset](https://lightning.ai/lightning-ai/studios/use-or-explore-laion-400million-dataset) |
+| [Chesapeake Roads Spatial Context](https://github.com/isaaccorley/chesapeakersc)                                                             |    Image & Mask     | [Convert GeoSpatial data to Lightning Streaming](https://lightning.ai/lightning-ai/studios/convert-spatial-data-to-lightning-streaming) |
+| [Imagenet 1M](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171)                                               |    Image & Label    |              [Benchmark cloud data-loading libraries](https://lightning.ai/lightning-ai/studios/benchmark-cloud-data-loading-libraries) |
+| [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B) & [StartCoder](https://huggingface.co/datasets/bigcode/starcoderdata) |        Text         |              [Prepare the TinyLlama 1T token dataset](https://lightning.ai/lightning-ai/studios/prepare-the-tinyllama-1t-token-dataset) |
+| [English Wikepedia](https://huggingface.co/datasets/wikipedia)                                                                               |        Text         |            [Embed English Wikipedia under 5 dollars](https://lightning.ai/lightning-ai/studios/embed-english-wikipedia-under-5-dollars) |
+| Generated                                                                                                                                    |    Parquet Files    |            [Convert parquets to Lightning Streaming](https://lightning.ai/lightning-ai/studios/convert-parquets-to-lightning-streaming) |
+
+[Lightning Studios](https://lightning.ai) are fully reproducible cloud IDE with data, code, dependencies, etc...
 
 # 🎬 Getting Started
 
@@ -102,6 +141,10 @@ cls = sample['class']
 dataloader = DataLoader(dataset)
 ```
 
+Here is an illustration showing how the `StreamingDataset` works under the hood.
+
+![An illustration showing how the Streaming Dataset works.](https://pl-flash-data.s3.amazonaws.com/streaming_dataset.gif)
+
 ## Transform data
 
 Similar to `optimize`, the `map` operator can be used to transform data by applying a function over a list of item and persist all the files written inside the output directory.
@@ -154,21 +197,6 @@ if __name__ == "__main__":
     )
 ```
 
-# 📚 End-to-end Lightning Studio Templates
-
-We have end-to-end free [Studios](https://lightning.ai) showing all the steps to prepare the following datasets:
-
-| Dataset                                                                                                                                      |      Data type      |                                                                                                                                  Studio |
-| -------------------------------------------------------------------------------------------------------------------------------------------- | :-----------------: | --------------------------------------------------------------------------------------------------------------------------------------: |
-| [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/)                                                                                  | Image & description |            [Use or explore LAION-400MILLION dataset](https://lightning.ai/lightning-ai/studios/use-or-explore-laion-400million-dataset) |
-| [Chesapeake Roads Spatial Context](https://github.com/isaaccorley/chesapeakersc)                                                             |    Image & Mask     | [Convert GeoSpatial data to Lightning Streaming](https://lightning.ai/lightning-ai/studios/convert-spatial-data-to-lightning-streaming) |
-| [Imagenet 1M](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171)                                               |    Image & Label    |              [Benchmark cloud data-loading libraries](https://lightning.ai/lightning-ai/studios/benchmark-cloud-data-loading-libraries) |
-| [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B) & [StartCoder](https://huggingface.co/datasets/bigcode/starcoderdata) |        Text         |              [Prepare the TinyLlama 1T token dataset](https://lightning.ai/lightning-ai/studios/prepare-the-tinyllama-1t-token-dataset) |
-| [English Wikepedia](https://huggingface.co/datasets/wikipedia)                                                                               |        Text         |            [Embed English Wikipedia under 5 dollars](https://lightning.ai/lightning-ai/studios/embed-english-wikipedia-under-5-dollars) |
-| Generated                                                                                                                                    |    Parquet Files    |            [Convert parquets to Lightning Streaming](https://lightning.ai/lightning-ai/studios/convert-parquets-to-lightning-streaming) |
-
-[Lightning Studios](https://lightning.ai) are fully reproducible cloud IDE with data, code, dependencies, etc... Finally reproducible science.
-
 # 📈 Easily scale data processing
 
 To scale data processing, create a free account on [lightning.ai](https://lightning.ai/) platform. With the platform, the `optimize` and `map` can start multiple machines to make data processing drastically faster as follows: