ENGPLAT-106: update synthetics readme

GitOrigin-RevId: 000edfccc7d20a37eea0c6e5dfad42fbbd780963

README.md

@@ -1,4 +1,5 @@
 # Gretel Synthetics
+
 <p align="center">
     <a href="https://gretel.ai"><img width="128px" src="https://gretel-public-website.s3.amazonaws.com/assets/gobs_the_cat_@1x.png" alt="Gobs the Gretel.ai cat" /></a><br />
     <i>An open source synthetic data library from Gretel.ai</i>
@@ -14,13 +15,14 @@
 [![Slack](https://img.shields.io/badge/Slack%20Workspace-Join%20now!-36C5F0?logo=slack)](https://gretel.ai/slackinvite)
 
 ## Documentation
-* [Get started with gretel-synthetics](https://gretel-synthetics.readthedocs.io/en/stable/)
-* [Configuration](https://gretel-synthetics.readthedocs.io/en/stable/api/config.html)
-* [Train your model](https://gretel-synthetics.readthedocs.io/en/stable/api/train.html)
-* [Generate synthetic records](https://gretel-synthetics.readthedocs.io/en/stable/api/generate.html)
 
+- [Get started with gretel-synthetics](https://gretel-synthetics.readthedocs.io/en/stable/)
+- [Configuration](https://gretel-synthetics.readthedocs.io/en/stable/api/config.html)
+- [Train your model](https://gretel-synthetics.readthedocs.io/en/stable/api/train.html)
+- [Generate synthetic records](https://gretel-synthetics.readthedocs.io/en/stable/api/generate.html)
 
 ## Try it out now!
+
 If you want to quickly discover gretel-synthetics, simply click the button below and follow the tutorials!
 
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gretelai/gretel-synthetics/blob/master/examples/synthetic_records.ipynb)
@@ -56,7 +58,7 @@ If you want to install `gretel-synthetics` locally and use a GPU (recommended):
 1. Create a virtual environment (e.g. using `conda`)
 
 ```
-$ conda create --name tf python=3.8
+$ conda create --name tf python=3.9
 ```
 
 2. Activate the virtual environment
@@ -67,32 +69,40 @@ $ conda activate tf
 
 3. Run the setup script `./setup-utils/setup-gretel-synthetics-tensorflow24-with-gpu.sh`
 
-The last step will install all the necessary software packages for GPU usage, `tensorflow=2.4` and `gretel-synthetics`.
+The last step will install all the necessary software packages for GPU usage, `tensorflow=2.8` and `gretel-synthetics`.
 Note that this script works only for Ubuntu 18.04. You might need to modify it for other OS versions.
 
+### Getting started with the timeseries DGAN model
+
+The [timeseries DGAN module](https://synthetics.docs.gretel.ai/en/stable/models/timeseries_dgan.html#timeseries-dgan) contains a PyTorch implementation of a DoppelGANger model that is optimized for timeseries data. Similar to tensorflow, you will need to manually install pytorch
+
+```
+pip install torch==1.11.0
+```
+
 ## Overview
 
-This package allows developers to quickly get immersed with synthetic data generation through the use of neural networks. The more complex pieces of working with libraries like Tensorflow and differential privacy are bundled into friendly Python classes and functions.  There are two high level modes that can be utilized.
+This package allows developers to quickly get immersed with synthetic data generation through the use of neural networks. The more complex pieces of working with libraries like Tensorflow and differential privacy are bundled into friendly Python classes and functions. There are two high level modes that can be utilized.
 
 ### Simple Mode
 
-The simple mode will train line-per-line on an input file of text.  When generating data, the generator will yield a custom object that can be used a variety of different ways based on your use case.  [This notebook](https://github.com/gretelai/gretel-synthetics/blob/master/examples/tensorflow/simple-character-model.ipynb) demonstrates this mode.
+The simple mode will train line-per-line on an input file of text. When generating data, the generator will yield a custom object that can be used a variety of different ways based on your use case. [This notebook](https://github.com/gretelai/gretel-synthetics/blob/master/examples/tensorflow/simple-character-model.ipynb) demonstrates this mode.
 
 ### DataFrame Mode
 
-This library supports CSV / DataFrames natively using the DataFrame "batch" mode. This module provided a wrapper around our simple mode that is geared for working with tabular data.  Additionally, it is capabable of handling a high number of columns by breaking the input DataFrame up into "batches" of columns and training a model on each batch.  [This notebook](https://github.com/gretelai/gretel-synthetics/blob/master/examples/dataframe_batch.ipynb) shows an overview of using this library with DataFrames natively.
+This library supports CSV / DataFrames natively using the DataFrame "batch" mode. This module provided a wrapper around our simple mode that is geared for working with tabular data. Additionally, it is capabable of handling a high number of columns by breaking the input DataFrame up into "batches" of columns and training a model on each batch. [This notebook](https://github.com/gretelai/gretel-synthetics/blob/master/examples/dataframe_batch.ipynb) shows an overview of using this library with DataFrames natively.
 
 ### Components
 
 There are four primary components to be aware of when using this library.
 
-1) Configurations. Configurations are classes that are specific to an underlying ML engine used to train and generate data.  An example would be using `TensorFlowConfig` to create all the necessary parameters to train a model based on TF. `LocalConfig` is aliased to `TensorFlowConfig` for backwards compatability with older versions of the library.  A model is saved to a designated directory, which can optionally be archived and utilized later.
+1. Configurations. Configurations are classes that are specific to an underlying ML engine used to train and generate data. An example would be using `TensorFlowConfig` to create all the necessary parameters to train a model based on TF. `LocalConfig` is aliased to `TensorFlowConfig` for backwards compatability with older versions of the library. A model is saved to a designated directory, which can optionally be archived and utilized later.
 
-2) Tokenizers. Tokenizers convert input text into integer based IDs that are used by the underlying ML engine. These tokenizers can be created and sent to the training input. This is optional, and if no specific tokenizer is specified then a default one will be used. You can find [an example](https://github.com/gretelai/gretel-synthetics/blob/master/examples/tensorflow/batch-df-char-tokenizer.ipynb) here that uses a simple char-by-char tokenizer to build a model from an input CSV. When training in a non-differentially private mode, we suggest using the default `SentencePiece` tokenizer, an unsupervised tokenizer that learns subword units (e.g., **byte-pair-encoding (BPE)** [[Sennrich et al.](http://www.aclweb.org/anthology/P16-1162)]) and **unigram language model** [[Kudo.](https://arxiv.org/abs/1804.10959)]) for faster training and increased accuracy of the synthetic model.
+2. Tokenizers. Tokenizers convert input text into integer based IDs that are used by the underlying ML engine. These tokenizers can be created and sent to the training input. This is optional, and if no specific tokenizer is specified then a default one will be used. You can find [an example](https://github.com/gretelai/gretel-synthetics/blob/master/examples/tensorflow/batch-df-char-tokenizer.ipynb) here that uses a simple char-by-char tokenizer to build a model from an input CSV. When training in a non-differentially private mode, we suggest using the default `SentencePiece` tokenizer, an unsupervised tokenizer that learns subword units (e.g., **byte-pair-encoding (BPE)** [[Sennrich et al.](http://www.aclweb.org/anthology/P16-1162)]) and **unigram language model** [[Kudo.](https://arxiv.org/abs/1804.10959)]) for faster training and increased accuracy of the synthetic model.
 
-3) Training.  Training a model combines the configuration and tokenizer and builds a model, which is stored in the designated directory, that can be used to generate new records.
+3. Training. Training a model combines the configuration and tokenizer and builds a model, which is stored in the designated directory, that can be used to generate new records.
 
-4) Generation. Once a model is trained, any number of new lines or records can be generated. Optionally, a record validator can be provided to ensure that the generated data meets any constraints that are necessary.  See our notebooks for examples on validators.
+4. Generation. Once a model is trained, any number of new lines or records can be generated. Optionally, a record validator can be provided to ensure that the generated data meets any constraints that are necessary. See our notebooks for examples on validators.
 
 #### Utilities
 
@@ -120,4 +130,3 @@ There are also a few configuration options that are notable such as:
 - `reset_states` should be disabled
 
 Please see our [example Notebook](https://github.com/gretelai/gretel-synthetics/blob/master/examples/tensorflow/diff_privacy.ipynb) for training a DP model based on the [Netflix Prize](https://en.wikipedia.org/wiki/Netflix_Prize) dataset.
-