Add image documentation (#238)

* Add partial image implementation Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Refactor requirements Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix bugs Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Change from_map to map_partitions Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add super constructor Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add kwargs for load_object_on_worker Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Get proper epoch size Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Complete embedding creation loop Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Change devices Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add device Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Refactor embedding creation and add classifier Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix bugs in classifiers Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Refactor model names Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add model name Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix classifier bugs Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Allow postprocessing for classifiers Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix name and add print Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix variable name Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add NSFW Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update init for import Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix embedding size Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add fused classifiers Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix missing index Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update metdata for fused classifiers Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add export to webdataset Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix missing id col Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Sort embeddings by id Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add timm Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update init file Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add autocast to timm Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update requirements and transform Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add additional interpolation support Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix transform normalization Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Remove open_clip Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add index path support to wds Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Address Vibhu's feedback Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add import guard for image dataset Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Change default device Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Remove commented code Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Remove device id Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix index issue Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add docstrings and standardize variable names Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add image curation tutorial Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add initial image docs Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Remove tutorial Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add dataset docs Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add embedder documentation Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Revert embedding column name change Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update user guide for images Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update README Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Update README with RAPIDS nightly instructions Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix formatting issues in image documentation Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Remove extra newline in README Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Address most of Sarah's feedback Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Add section summary Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix errors and REWORD GPU bullets in README Signed-off-by: Ryan Wolf <rywolf@nvidia.com> * Fix how table of contents displays with new sections Signed-off-by: Ryan Wolf <rywolf@nvidia.com> --------- Signed-off-by: Ryan Wolf <rywolf@nvidia.com>
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diff --git a/README.md b/README.md
@@ -9,51 +9,43 @@
 </div>
 
 # NeMo Curator
-🚀 **The GPU-Accelerated Open Source Framework for Efficient Large Language Model Data Curation** 🚀
+🚀 **The GPU-Accelerated Open Source Framework for Efficient Generative AI Model Data Curation** 🚀
 
-<p align="center">
-  <img src="./docs/user-guide/images/diagram.png" alt="diagram"/>
-</p>
-
-NeMo Curator is a Python library specifically designed for fast and scalable dataset preparation and curation for [large language model (LLM)](https://www.nvidia.com/en-us/glossary/large-language-models/) use-cases such as foundation model pretraining, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and paramter-efficient fine-tuning (PEFT). It greatly accelerates data curation by leveraging GPUs with [Dask](https://www.dask.org/) and [RAPIDS](https://developer.nvidia.com/rapids), resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline expansion and accelerating model convergence through the preparation of high-quality tokens.
-
-At the core of the NeMo Curator is the `DocumentDataset` which serves as the the main dataset class. It acts as a straightforward wrapper around a Dask `DataFrame`. The Python library offers easy-to-use methods for expanding the functionality of your curation pipeline while eliminating scalability concerns.
+NeMo Curator is a Python library specifically designed for fast and scalable dataset preparation and curation for generative AI use cases such as foundation language model pretraining, text-to-image model training, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT). It greatly accelerates data curation by leveraging GPUs with [Dask](https://www.dask.org/) and [RAPIDS](https://developer.nvidia.com/rapids), resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline expansion and accelerating model convergence through the preparation of high-quality tokens.
 
 ## Key Features
 
-NeMo Curator provides a collection of scalable data-mining modules. Some of the key features include:
-
-- [Data download and text extraction](docs/user-guide/download.rst)
-
-  - Default implementations for downloading and extracting Common Crawl, Wikipedia, and ArXiv data
-  - Easily customize the download and extraction and extend to other datasets
-
-- [Language identification and separation](docs/user-guide/languageidentificationunicodeformatting.rst) with [fastText](https://fasttext.cc/docs/en/language-identification.html) and [pycld2](https://pypi.org/project/pycld2/)
-
-- [Text reformatting and cleaning](docs/user-guide/languageidentificationunicodeformatting.rst) to fix unicode decoding errors via [ftfy](https://ftfy.readthedocs.io/en/latest/)
-
-- [Quality filtering](docs/user-guide/qualityfiltering.rst)
-
-  - Multilingual heuristic-based filtering
-  - Classifier-based filtering via [fastText](https://fasttext.cc/)
-
-- [Document-level deduplication](docs/user-guide/gpudeduplication.rst)
-
-  - exact and fuzzy (near-identical) deduplication are accelerated using cuDF and Dask
-  - For fuzzy deduplication, our implementation follows the method described in [Microsoft Turing NLG 530B](https://arxiv.org/abs/2201.11990)
-  - For semantic deduplication,  our implementation follows the method described in [SemDeDup](https://arxiv.org/pdf/2303.09540) by Meta AI (FAIR) [facebookresearch/SemDeDup](https://github.com/facebookresearch/SemDeDup)
-
-- [Multilingual downstream-task decontamination](docs/user-guide/taskdecontamination.rst) following the approach of [OpenAI GPT3](https://arxiv.org/pdf/2005.14165.pdf) and [Microsoft Turing NLG 530B](https://arxiv.org/abs/2201.11990)
-
-- [Distributed data classification](docs/user-guide/distributeddataclassification.rst)
-
-  - Multi-node, multi-GPU classifier inference
-  - Provides sophisticated domain and quality classification
-  - Flexible interface for extending to your own classifier network
-
-- [Personal identifiable information (PII) redaction](docs/user-guide/personalidentifiableinformationidentificationandremoval.rst) for removing addresses, credit card numbers, social security numbers, and more
-
-These modules offer flexibility and permit reordering, with only a few exceptions. In addition, the [NeMo Framework Launcher](https://github.com/NVIDIA/NeMo-Megatron-Launcher) provides pre-built pipelines that can serve as a foundation for your customization use cases.
+NeMo Curator provides a collection of scalable data curation modules for text and image curation.
+
+### Text Curation
+All of our text pipelines have great multilingual support.
+
+- [Download and Extraction](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/download.html)
+  - Default implementations for Common Crawl, Wikipedia, and ArXiv sources
+  - Easily customize and extend to other sources
+- [Language Identification](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/languageidentificationunicodeformatting.html)
+- [Unicode Reformatting](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/languageidentificationunicodeformatting.html)
+- [Heuristic Filtering](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/qualityfiltering.html)
+- Classifier Filtering
+  - [fastText](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/qualityfiltering.html)
+  - GPU-Accelerated models: [Domain, Quality, and Safety Classification](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/distributeddataclassification.html)
+- **GPU-Accelerated Deduplication**
+  - [Exact Deduplication](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/gpudeduplication.html)
+  - [Fuzzy Deduplication](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/gpudeduplication.html) via MinHash Locality Sensitive Hashing
+  - [Semantic Deduplication](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/semdedup.html)
+- [Downstream-task Decontamination](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/taskdecontamination.html)
+- [Personal Identifiable Information (PII) Redaction](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/personalidentifiableinformationidentificationandremoval.html)
+
+### Image Curation
+
+- [Embedding Creation](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/image/classifiers/embedders.html)
+- Classifier Filtering
+  - [Aesthetic](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/image/classifiers/aesthetic.html) and [NSFW](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/image/classifiers/nsfw.html) Classification
+- GPU Deduplication
+  - [Semantic](https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/semdedup.html)
+
+These modules offer flexibility and permit reordering, with only a few exceptions.
+All the modules automatically scale to multiple nodes to increase throughput.
 
 ## Resources
 
@@ -83,59 +75,52 @@ Before installing NeMo Curator, ensure that the following requirements are met:
   - Volta™ or higher ([compute capability 7.0+](https://developer.nvidia.com/cuda-gpus))
   - CUDA 12 (or above)
 
-You can install NeMo-Curator
-1. from PyPi
-2. from source
-3. get it through the [NeMo Framework container](https://github.com/NVIDIA/NeMo?tab=readme-ov-file#docker-containers).
-
-
+You can get NeMo-Curator in 3 ways.
+1. PyPi
+2. Source
+3. NeMo Framework Container
 
-#### From PyPi
-
-To install the CPU-only modules:
+#### PyPi
 
 ```bash
 pip install cython
-pip install nemo-curator
+pip install --extra-index-url https://pypi.nvidia.com nemo-curator[all]
 ```
 
-To install the CPU and CUDA-accelerated modules:
-
+#### Source
 ```bash
+git clone https://github.com/NVIDIA/NeMo-Curator.git
 pip install cython
-pip install --extra-index-url https://pypi.nvidia.com nemo-curator[cuda12x]
+pip install ./NeMo-Curator[all]
 ```
 
-#### From Source
+#### NeMo Framework Container
 
-1. Clone the NeMo Curator repository in GitHub.
-
-    ```bash
-    git clone https://github.com/NVIDIA/NeMo-Curator.git
-    cd NeMo-Curator
-    ```
-
-2. Install the modules that you need.
+The latest release of NeMo Curator comes preinstalled in the [NeMo Framework Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). If you want the latest commit inside the container, you can reinstall NeMo Curator using:
 
-    To install the CPU-only modules:
+```bash
+pip uninstall nemo-curator
+rm -r /opt/NeMo-Curator
+git clone https://github.com/NVIDIA/NeMo-Curator.git /opt/NeMo-Curator
+pip install --extra-index-url https://pypi.nvidia.com /opt/NeMo-Curator[all]
+```
 
-    ```bash
-    pip install cython
-    pip install .
-    ```
+#### Extras
+NeMo Curator has a set of extras you can use to only install the necessary modules for your workload.
+These extras are available for all installation methods provided.
 
-    To install the CPU and CUDA-accelerated modules:
+```bash
+pip install nemo-curator # Installs CPU-only text curation modules
+pip install --extra-index-url https://pypi.nvidia.com nemo-curator[cuda12x] # Installs CPU + GPU text curation modules
+pip install --extra-index-url https://pypi.nvidia.com nemo-curator[image] # Installs CPU + GPU text and image curation modules
+pip install --extra-index-url https://pypi.nvidia.com nemo-curator[all] # Installs all of the above
+```
 
-    ```bash
-    pip install cython
-    pip install --extra-index-url https://pypi.nvidia.com ".[cuda12x]"
-    ```
 
 #### Using Nightly Dependencies for RAPIDS
 
 You can also install NeMo Curator using the [RAPIDS Nightly Builds](https://docs.rapids.ai/install). To do so, you can set the environment variable `RAPIDS_NIGHTLY=1`.
 
-
 ```bash
 # installing from pypi
 RAPIDS_NIGHTLY=1 pip install --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple "nemo-curator[cuda12x]"
@@ -146,18 +131,6 @@ RAPIDS_NIGHTLY=1 pip install --extra-index-url=https://pypi.anaconda.org/rapidsa
 
 When the `RAPIDS_NIGHTLY` variable is set to 0 (which is the default), it will use the stable version of RAPIDS.
 
-#### From the NeMo Framework Container
-
-The latest release of NeMo Curator comes preinstalled in the [NeMo Framework Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). If you want the latest commit inside the container, you can reinstall NeMo Curator using:
-
-```bash
-pip uninstall nemo-curator
-rm -r /opt/NeMo-Curator
-git clone https://github.com/NVIDIA/NeMo-Curator.git /opt/NeMo-Curator
-pip install --extra-index-url https://pypi.nvidia.com /opt/NeMo-Curator[cuda12x]
-```
-And follow the instructions for installing from source from [above](#from-source).
-
 ## Use NeMo Curator
 ### Python API Quick Example
 
@@ -189,6 +162,7 @@ To get started with NeMo Curator, you can follow the tutorials [available here](
 - [`peft-curation`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/peft-curation) which focuses on data curation for LLM parameter-efficient fine-tuning (PEFT) use-cases.
 - [`distributed_data_classification`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/distributed_data_classification) which focuses on using the quality and domain classifiers to help with data annotation.
 - [`single_node_tutorial`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/single_node_tutorial) which demonstrates an end-to-end data curation pipeline for curating Wikipedia data in Thai.
+- [`image-curation`](https://github.com/NVIDIA/NeMo-Curator/blob/main/tutorials/image-curation/image-curation.ipynb) which explores the scalable image curation modules.
 
 
 ### Access Python Modules
@@ -201,9 +175,9 @@ NeMo Curator also offers CLI scripts for you to use. The scripts in `nemo_curato
 
 ### Use NeMo Framework Launcher
 
-As an alternative method for interfacing with NeMo Curator, you can use the [NeMo Framework Launcher](https://github.com/NVIDIA/NeMo-Megatron-Launcher). The launcher enables you to easily configure the parameters and cluster. It can also automatically generate the SLURM batch scripts that wrap around the CLI scripts required to run your pipeline.
+As an alternative method for interfacing with NeMo Curator, you can use the [NeMo Framework Launcher](https://github.com/NVIDIA/NeMo-Megatron-Launcher). The launcher enables you to easily configure the parameters and cluster. It can also automatically generate the Slurm batch scripts that wrap around the CLI scripts required to run your pipeline.
 
-In addition, other methods are available to run NeMo Curator on SLURM. For example, refer to the example scripts in [`examples/slurm`](examples/slurm/) for information on how to run NeMo Curator on SLURM without the NeMo Framework Launcher.
+In addition, other methods are available to run NeMo Curator on Slurm. For example, refer to the example scripts in [`examples/slurm`](examples/slurm/) for information on how to run NeMo Curator on Slurm without the NeMo Framework Launcher.
 
 ## Module Ablation and Compute Performance
 
@@ -212,7 +186,7 @@ The modules within NeMo Curator were primarily designed to curate high-quality d
 The following figure shows that the use of different data curation modules implemented in NeMo Curator led to improved model zero-shot downstream task performance.
 
 <p align="center">
-  <img src="./docs/user-guide/images/zeroshot_ablations.png" alt="drawing" width="700"/>
+  <img src="./docs/user-guide/assets/zeroshot_ablations.png" alt="drawing" width="700"/>
 </p>
 
 In terms of scalability and compute performance, using the combination of RAPIDS and Dask fuzzy deduplication enabled us to deduplicate the 1.1 Trillion token Red Pajama dataset in 1.8 hours with 64 NVIDIA A100 Tensor Core GPUs.

diff --git a/docs/user-guide/api/datasets.rst b/docs/user-guide/api/datasets.rst
@@ -7,4 +7,12 @@ DocumentDataset
 -------------------
 
 .. autoclass:: nemo_curator.datasets.DocumentDataset
+    :members:
+
+
+-------------------------------
+ImageTextPairDataset
+-------------------------------
+
+.. autoclass:: nemo_curator.datasets.ImageTextPairDataset
     :members:
diff --git a/docs/user-guide/api/image/classifiers.rst b/docs/user-guide/api/image/classifiers.rst
@@ -0,0 +1,21 @@
+======================================
+Classifiers
+======================================
+
+------------------------------
+Base Class
+------------------------------
+
+.. autoclass:: nemo_curator.image.classifiers.ImageClassifier
+    :members:
+
+
+------------------------------
+Image Classifiers
+------------------------------
+
+.. autoclass:: nemo_curator.image.classifiers.AestheticClassifier
+    :members:
+
+.. autoclass:: nemo_curator.image.classifiers.NsfwClassifier
+    :members:
diff --git a/docs/user-guide/api/image/embedders.rst b/docs/user-guide/api/image/embedders.rst
@@ -0,0 +1,18 @@
+======================================
+Embedders
+======================================
+
+------------------------------
+Base Class
+------------------------------
+
+.. autoclass:: nemo_curator.image.embedders.ImageEmbedder
+    :members:
+
+
+------------------------------
+Timm
+------------------------------
+
+.. autoclass:: nemo_curator.image.embedders.TimmImageEmbedder
+    :members:
diff --git a/docs/user-guide/api/image/index.rst b/docs/user-guide/api/image/index.rst
@@ -0,0 +1,10 @@
+======================================
+Image Curation
+======================================
+
+.. toctree::
+   :maxdepth: 4
+   :titlesonly:
+
+   embedders.rst
+   classifiers.rst
diff --git a/docs/user-guide/api/index.rst b/docs/user-guide/api/index.rst
@@ -18,4 +18,5 @@ API Reference
    decontamination.rst
    services.rst
    synthetic.rst
+   image/index.rst
    misc.rst
diff --git a/docs/user-guide/images/diagram.png → docs/user-guide/assets/diagram.png b/docs/user-guide/images/diagram.png → docs/user-guide/assets/diagram.png
diff --git a/...ide/images/sorted_sequence_dataloader.png → ...ide/assets/sorted_sequence_dataloader.png b/...ide/images/sorted_sequence_dataloader.png → ...ide/assets/sorted_sequence_dataloader.png
diff --git a/.../user-guide/images/zeroshot_ablations.png → .../user-guide/assets/zeroshot_ablations.png b/.../user-guide/images/zeroshot_ablations.png → .../user-guide/assets/zeroshot_ablations.png
diff --git a/docs/user-guide/distributeddataclassification.rst b/docs/user-guide/distributeddataclassification.rst
@@ -201,7 +201,7 @@ The key feature of CrossFit used in NeMo Curator is the sorted sequence data loa
 - Groups sorted sequences into optimized batches.
 - Efficiently allocates batches to the provided GPU memories by estimating the memory footprint for each sequence length and batch size.
 
-.. image:: images/sorted_sequence_dataloader.png
+.. image:: assets/sorted_sequence_dataloader.png
    :alt: Sorted Sequence Data Loader
 
 Check out the `rapidsai/crossfit`_ repository for more information.