[TASK] Remove LSTM runtime from model

GitOrigin-RevId: 402e8add0164939694ead858f9da74034f550fbf

README.md

@@ -37,14 +37,12 @@ This section will guide you through installation of `gretel-synthetics` and depe
 By default, we do not install certain core requirements, the following dependencies should be installed _external to the installation_
 of `gretel-synthetics`, depending on which model(s) you plan to use.
 
-- Tensorflow: Used by the LSTM model, we recommend version 2.12.x
 - Torch: Used by Timeseries DGAN and ACTGAN (for ACTGAN, Torch is installed by SDV), we recommend version 2.0
 - SDV (Synthetic Data Vault): Used by ACTGAN, we recommend version 0.17.x
 
 These dependencies can be installed by doing the following:
 
 ```
-pip install tensorflow==2.12.1 # for LSTM
 pip install sdv<0.18 # for ACTGAN
 pip install torch==2.0 # for Timeseries DGAN
 ```
@@ -114,54 +112,3 @@ Keep in mind that this will also install several dependencies like PyTorch that
 versions installed for use with other models like Timeseries DGAN.
 
 The ACTGAN interface is a superset of the CTGAN interface. To see the additional features, please take a look at the ACTGAN demo notebook in the `examples` directory of this repo.
-
-## LSTM 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.
-
-### 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.
-
-### 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 capable 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 compatibility 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.
-
-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.
-
-### Utilities
-
-In addition to the four primary components, the `gretel-synthetics` package also ships with a set of utilities that are helpful for training advanced synthetics models and evaluating synthetic datasets.
-
-Some of this functionality carries large dependencies, so they are shipped as an extra called `utils`. To install these dependencies, you may run
-
-```
-pip install gretel-synthetics[utils]
-```
-
-For additional details, please refer to the [Utility module API docs](https://synthetics.docs.gretel.ai/en/latest/utils/index.html).
-
-### Differential Privacy
-
-Differential privacy support for our TensorFlow mode is built on the great work being done by the Google TF team and their [TensorFlow Privacy library](https://github.com/tensorflow/privacy).
-
-When utilizing DP, we currently recommend using the character tokenizer as it will only create a vocabulary of single tokens and removes the risk of sensitive data being memorized as actual tokens that can be replayed during generation.
-
-There are also a few configuration options that are notable such as:
-
-- `predict_batch_size` should be set to 1
-- `dp` should be enabled
-- `learning_rate`, `dp_noise_multiplier`, `dp_l2_norm_clip`, and `dp_microbatches` can be adjusted to achieve various epsilon values.
-- `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.

requirements/base.txt

@@ -1,10 +1,7 @@
 category-encoders==2.4.0
-joblib==1.4.2
 numpy>=1.18.0,<1.24
-packaging<22
 pandas>=1.1.0,<2
+protobuf>=4,<=4.24.0
 rdt>=1.2,<1.3
 sdv>=0.17,<0.18
-sentencepiece==0.2.0
-smart_open>=2.1.0,<8.0
 tqdm<5.0

setup.py

@@ -33,8 +33,7 @@ def reqs(file, without=None):
 utils_reqs = reqs("requirements/utils.txt")
 test_reqs = reqs("requirements/test.txt")
 torch_reqs = reqs("requirements/torch.txt")
-tf_reqs = reqs("requirements/tensorflow.txt")
-all_reqs = [base_reqs, utils_reqs, torch_reqs, tf_reqs]
+all_reqs = [base_reqs, utils_reqs, torch_reqs]
 
 setup(
     name="gretel-synthetics",
@@ -56,7 +55,6 @@ def reqs(file, without=None):
         "utils": utils_reqs,
         "test": test_reqs,
         "torch": torch_reqs,
-        "tensorflow": tf_reqs,
         "docs": doc_reqs,
     },
     classifiers=[

src/gretel_synthetics/config.py

@@ -13,13 +13,8 @@
 from pathlib import Path
 from typing import Callable, Optional, TYPE_CHECKING
 
-import tensorflow as tf
-
 import gretel_synthetics.const as const
 
-from gretel_synthetics.tensorflow.generator import TensorFlowGenerator
-from gretel_synthetics.tensorflow.train import train_rnn
-
 logging.basicConfig(
     format="%(asctime)s : %(threadName)s : %(levelname)s : %(message)s",
     level=logging.INFO,
@@ -280,12 +275,6 @@ class TensorFlowConfig(BaseConfig):
 
     def __post_init__(self):
         if self.dp:
-            major, minor, _ = tf.__version__.split(".")
-            if (int(major), int(minor)) < (2, 4):
-                raise RuntimeError(
-                    "Running in differential privacy mode requires TensorFlow 2.4.x or greater. "
-                    "Please see the README for details"
-                )
 
             # TODO: To enable micro-batch size greater than 1, we need to update the differential privacy
             #  optimizer loss function to compute the vector of per-example losses, rather than the mean
@@ -319,15 +308,13 @@ def __post_init__(self):
         super().__post_init__()
 
     def get_generator_class(self):
-        return TensorFlowGenerator
+        return None
 
     def get_training_callable(self):
-        return train_rnn
+        return None
 
     def gpu_check(self):
-        device_name = tf.test.gpu_device_name()
-        if not device_name.startswith("/device:GPU:"):
-            logging.warning("***** GPU not found, CPU will be used instead! *****")
+        pass
 
 
 #################