update docs according to recently refactor and events (#366)

* update docs according to recently refactor and events

* update docs according to recently refactor and events

* update docs according to recently refactor and events

* minor fix according to yilun's comment

README.md

@@ -37,6 +37,7 @@ We welcome you to join us (via issues, PRs, [Slack](https://join.slack.com/t/dat
 ----
 
 ## News
+- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-07-24] "Tianchi Better Synth Data Synthesis Competition for Multimodal Large Models" — Our 4th data-centric LLM competition has kicked off! Please visit the competition's [official website](https://tianchi.aliyun.com/competition/entrance/532251) for more information.
 - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-07-17] We utilized the Data-Juicer [Sandbox Laboratory Suite](https://github.com/modelscope/data-juicer/blob/main/docs/Sandbox.md) to systematically optimize data and models through an co-development workflow between data and models, achieving a new top spot on the [VBench](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard) text-to-video leaderboard. The related achievements have been compiled and published in a [paper](http://arxiv.org/abs/2407.11784), and the model has been released on the [ModelScope](https://modelscope.cn/models/Data-Juicer/Data-Juicer-T2V) and [HuggingFace](https://huggingface.co/datajuicer/Data-Juicer-T2V) platforms.
 - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-07-12] Our *awesome list of MLLM-Data* has evolved into a systemic [survey](https://arxiv.org/abs/2407.08583) from model-data co-development perspective. Welcome to [explore](docs/awesome_llm_data.md) and contribute!
 - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-06-01] ModelScope-Sora "Data Directors" creative sprint—Our third data-centric LLM competition has kicked off! Please visit the competition's [official website](https://tianchi.aliyun.com/competition/entrance/532219) for more information.
@@ -96,8 +97,8 @@ Table of Contents
   visualization, and multidimensional automatic evaluation, so that you can better understand and improve your data and models.
   ![Data-in-the-loop](https://img.alicdn.com/imgextra/i2/O1CN017U7Zz31Y7XtCJ5GOz_!!6000000003012-0-tps-3640-1567.jpg)
 
-- **Enhanced Efficiency**: Providing efficient and parallel data processing pipelines (Aliyun-PAI\Ray\Slurm\CUDA\OP Fusion)
-  requiring less memory and CPU usage, optimized for maximum productivity.
+- **Towards production environment **: Providing efficient and parallel data processing pipelines (Aliyun-PAI\Ray\Slurm\CUDA\OP Fusion)
+  requiring less memory and CPU usage, optimized with automatic fault-toleration.
   ![sys-perf](https://img.alicdn.com/imgextra/i4/O1CN01Sk0q2U1hdRxbnQXFg_!!6000000004300-0-tps-2438-709.jpg)
 
 - **Comprehensive Data Processing Recipes**: Offering tens of [pre-built data
@@ -154,7 +155,7 @@ Table of Contents
 
 ## Installation
 
-### From Source
+### From Source 
 
 - Run the following commands to install the latest basic `data_juicer` version in
   editable mode:
@@ -229,6 +230,15 @@ You can install FFmpeg using package managers(e.g. sudo apt install ffmpeg on De
 
 Check if your environment path is set correctly by running the ffmpeg command from the terminal.
 
+
+<br><hr>
+<div style="text-align: right;">
+
+[🔼 back to index](#documentation-index-)
+
+</div>
+
+
 ## Quick Start
 
 
@@ -259,6 +269,20 @@ export DATA_JUICER_MODELS_CACHE="/path/to/another/directory/models"
 export DATA_JUICER_ASSETS_CACHE="/path/to/another/directory/assets"
 ```
 
+#### Flexible Programming Interface
+We provide various simple interfaces for users to choose from as follows. 
+```python
+#... init op & dataset ...
+
+# Chain call style, support single operator or operator list
+dataset = dataset.process(op)
+dataset = dataset.process([op1, op2])
+# Functional programming style for quick integration or script prototype iteration
+dataset = op(dataset)
+dataset = op.run(dataset)
+```
+
+
 ### Distributed Data Processing
 
 We have now implemented multi-machine distributed data processing based on [RAY](https://www.ray.io/). The corresponding demos can be run using the following commands:
@@ -376,6 +400,14 @@ docker run -dit \  # run the container in the background
 docker exec -it <container_id> bash
 ```
 
+
+<br><hr>
+<div style="text-align: right;">
+
+[🔼 back to index](#documentation-index-)
+
+</div>
+
 ## Data Recipes
 - [Recipes for data process in BLOOM](configs/reproduced_bloom/README.md)
 - [Recipes for data process in RedPajama](configs/redpajama/README.md)
@@ -417,3 +449,24 @@ If you find our work useful for your research or development, please kindly cite
   year={2024}
 }
 ```
+
+<details>
+<summary> More related papers from Data-Juicer Team:
+</summary>>
+
+- [Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development](https://arxiv.org/abs/2407.11784)
+
+- [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://arxiv.org/abs/2407.08583)
+
+- [Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining](https://arxiv.org/abs/2402.11505)
+
+</details>
+
+
+
+<br><hr>
+<div style="text-align: right;">
+
+[🔼 back to index](#documentation-index-)
+
+</div>

README_ZH.md

@@ -31,6 +31,7 @@ Data-Juicer正在积极更新和维护中，我们将定期强化和新增更多
 ----
 
 ## 新消息
+- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-07-24] “天池 Better Synth 多模态大模型数据合成赛”——第四届Data-Juicer大模型数据挑战赛已经正式启动！立即访问[竞赛官网](https://tianchi.aliyun.com/competition/entrance/532251)，了解赛事详情。
 - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png)[2024-07-17] 我们利用Data-Juicer[沙盒实验室套件](https://github.com/modelscope/data-juicer/blob/main/docs/Sandbox-ZH.md)，通过数据与模型间的系统性研发工作流，调优数据和模型，在[VBench](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard)文生视频排行榜取得了新的榜首。相关成果已经整理发表在[论文](http://arxiv.org/abs/2407.11784)中，并且模型已在[ModelScope](https://modelscope.cn/models/Data-Juicer/Data-Juicer-T2V)和[HuggingFace](https://huggingface.co/datajuicer/Data-Juicer-T2V)平台发布。
 - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png)[2024-07-12] 我们的MLLM-Data精选列表已经演化为一个模型-数据协同开发的角度系统性[综述](https://arxiv.org/abs/2407.08583)。欢迎[浏览](docs/awesome_llm_data.md)或参与贡献!
 - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-06-01] ModelScope-Sora“数据导演”创意竞速——第三届Data-Juicer大模型数据挑战赛已经正式启动！立即访问[竞赛官网](https://tianchi.aliyun.com/competition/entrance/532219)，了解赛事详情。
@@ -82,7 +83,7 @@ Data-Juicer正在积极更新和维护中，我们将定期强化和新增更多
 
 * **数据反馈回路 & 沙盒实验室**：支持一站式数据-模型协同开发，通过[沙盒实验室](docs/Sandbox-ZH.md)快速迭代，基于数据和模型反馈回路、可视化和多维度自动评估等功能，使您更了解和改进您的数据和模型。  ![Data-in-the-loop](https://img.alicdn.com/imgextra/i2/O1CN017U7Zz31Y7XtCJ5GOz_!!6000000003012-0-tps-3640-1567.jpg)
 
-* **效率增强**：提供高效并行化的数据处理流水线（Aliyun-PAI\Ray\Slurm\CUDA\算子融合），减少内存占用和CPU开销，提高生产力。  ![sys-perf](https://img.alicdn.com/imgextra/i4/O1CN01Sk0q2U1hdRxbnQXFg_!!6000000004300-0-tps-2438-709.jpg)
+* **面向生产环境**：提供高效并行化的数据处理流水线（Aliyun-PAI\Ray\Slurm\CUDA\算子融合），减少内存占用和CPU开销，支持自动化处理容错。  ![sys-perf](https://img.alicdn.com/imgextra/i4/O1CN01Sk0q2U1hdRxbnQXFg_!!6000000004300-0-tps-2438-709.jpg)
 
 * **全面的数据处理菜谱**：为pre-training、fine-tuning、中英文等场景提供数十种[预构建的数据处理菜谱](configs/data_juicer_recipes/README_ZH.md)。 在LLaMA、LLaVA等模型上有效验证。 ![exp_llama](https://img.alicdn.com/imgextra/i2/O1CN019WtUPP1uhebnDlPR8_!!6000000006069-2-tps-2530-1005.png)
 
@@ -235,6 +236,19 @@ export DATA_JUICER_MODELS_CACHE="/path/to/another/directory/models"
 export DATA_JUICER_ASSETS_CACHE="/path/to/another/directory/assets"
 ```
 
+#### 灵活的编程接口
+我们提供了各种层次的简单编程接口，以供用户选择：
+```python
+# ... init op & dataset ...
+
+# 链式调用风格，支持单算子或算子列表
+dataset = dataset.process(op)
+dataset = dataset.process([op1, op2])
+# 函数式编程风格，方便快速集成或脚本原型迭代
+dataset = op(dataset)
+dataset = op.run(dataset)
+```
+
 ### 分布式数据处理
 
 Data-Juicer 现在基于[RAY](https://www.ray.io/)实现了多机分布式数据处理。
@@ -278,6 +292,9 @@ dj-analyze --config configs/demo/analyzer.yaml
 streamlit run app.py
 ```
 
+
+
+
 ### 构建配置文件
 
 * 配置文件包含一系列全局参数和用于数据处理的算子列表。您需要设置:
@@ -380,8 +397,6 @@ Data-Juicer 被各种 LLM产品和研究工作使用，包括来自阿里云-通
 Data-Juicer 感谢并参考了社区开源项目：
 [Huggingface-Datasets](https://github.com/huggingface/datasets), [Bloom](https://huggingface.co/bigscience/bloom), [RedPajama](https://github.com/togethercomputer/RedPajama-Data/tree/rp_v1), [Pile](https://huggingface.co/datasets/EleutherAI/pile), [Alpaca-Cot](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT), [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [DeepSpeed](https://www.deepspeed.ai/), [Arrow](https://github.com/apache/arrow), [Ray](https://github.com/ray-project/ray), [Beam](https://github.com/apache/beam),  [LM-Harness](https://github.com/EleutherAI/lm-evaluation-harness), [HELM](https://github.com/stanford-crfm/helm), ....
 
-
-
 ## 参考文献
 如果您发现我们的工作对您的研发有帮助，请引用以下[论文](https://arxiv.org/abs/2309.02033) 。
 
@@ -392,4 +407,24 @@ Data-Juicer 感谢并参考了社区开源项目：
   booktitle={International Conference on Management of Data},
   year={2024}
 }
-```
+```
+<details>
+<summary>更多Data-Juicer团队相关论文:
+</summary>>
+
+- [Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development](https://arxiv.org/abs/2407.11784)
+
+- [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://arxiv.org/abs/2407.08583)
+
+- [Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining](https://arxiv.org/abs/2402.11505)
+
+</details>
+
+
+
+<br><hr>
+<div style="text-align: right;">
+
+[🔼 back to index](#documentation-index-a-namedocuments)
+
+</div>

docs/DJ_SORA.md

@@ -38,7 +38,8 @@ This project is being actively updated and maintained. We eagerly invite you to
    - [✅] Ray based multi-machine distributed running
    - [✅] Aliyun PAI-DLC & Slurm based multi-machine distributed running
 - [✅] Distributed scheduling optimization (OP-aware, automated load balancing) --> Aliyun PAI-DLC
-- [ ] [WIP] Distributed storage optimization
+- [WIP] Low precision acceleration support for video related operators. (git tags: dj_op, dj_efficiency)
+- [WIP] SOTA model enhancement of existing video related operators. (git tags: dj_op, dj_sota_models)
 
 ## Basic Operators (video spatio-temporal dimension)
 - Towards Data Quality
@@ -90,20 +91,24 @@ This project is being actively updated and maintained. We eagerly invite you to
   - [✅] **Youku-mPLUG-CN**: 36TB video-caption data: `{<caption, video_id>}`
   - [✅] **InternVid**: 234M data sample: `{<caption, youtube_id, start/end_time>}`
   - [✅] **MSR-VTT**: 10K video-caption data: `{<caption, video_id>}`
-  - [ ] [WIP] ModelScope's datasets integration
-  - [ ] VideoInstruct-100K, Panda70M, ......
+  - [✅] ModelScope's datasets integration
+  - [✅] VideoInstruct-100K, Panda70M, ......
 - [ ] Large-scale high-quality DJ-SORA dataset
   - [✅] (Data sandbox) Building and optimizing multimodal data recipes with DJ-video operators (which are also being continuously extended and improved).
-  - [ ] [WIP] Continuous expansion of data sources: open-datasets, Youku, web, ...
-  - [ ] [WIP] Large-scale analysis, cleaning, and generation of high-quality multimodal datasets based on DJ recipes (OpenVideos, ...)
-  - [ ] [WIP] Large-scale generation of 3DPatch datasets based on DJ recipes.
+  - [✅] Continuous expansion of data sources: open-datasets, Youku, web, ...
+  - [ ] Large-scale analysis, cleaning, and generation of high-quality multimodal datasets based on DJ recipes (OpenVideos, ...)
+    - [WIP] broad scenarios, high-dynamic
   - ...
 
 ## DJ-SORA Data Validation and Model Training
-  - [ ] [WIP] (DJ-Bench101) Exploring and refining the collaborative development of multimodal data and model, establishing benchmarks and insights.
+  - [ ] Exploring and refining the collaborative development of multimodal data and model, establishing benchmarks and insights. [paper](https://arxiv.org/abs/2407.11784)
   - [ ] [WIP] Integration of SORA-like model training pipelines
     - [EasyAnimate](https://github.com/aigc-apps/EasyAnimate)
+    - [✅] [T2V](https://t2v-turbo.github.io/)
+    - [✅] [V-Bench](https://vchitect.github.io/VBench-project/)
     - ...
   - [✅] (Model-Data sandbox) With relatively small models and the DJ-SORA dataset, exploring low-cost, transferable, and instructive data-model co-design, configurations and checkpoints. 
   - [ ] [WIP] Training SORA-like models with DJ-SORA data on  larger scales and in more scenarios to improve model performance.
+    - [✅] Data-Juicer-T2v, [V-Bench Top1 model](https://huggingface.co/datajuicer/Data-Juicer-T2V)
+    - ...
   - ...

docs/DJ_SORA_ZH.md

@@ -38,7 +38,8 @@ DJ-SORA将基于Data-Juicer(包含上百个专用的视频、图像、音频、
   - [✅] Ray多机分布式  
   - [✅] 基于阿里云PAI-DLC和Slurm的多机分布式 
 - [✅] 分布式调度优化（OP-aware、自动化负载均衡）-->  Aliyun PAI-DLC
-- [ ] [WIP] 分布式存储优化  
+- [WIP] 视频相关算子的低精度加速支持, git tags: dj_op, dj_efficiency
+- [WIP] 现有视频相关算子的SOTA模型增强, git tags: dj_op, dj_sota_models
 
 ## 基础算子（视频时空维度）
 - 面向数据质量
@@ -94,22 +95,25 @@ DJ-SORA将基于Data-Juicer(包含上百个专用的视频、图像、音频、
   - [✅] **Youku-mPLUG-CN**: 36TB video-caption data：`{<caption, video_id>}`
   - [✅] **InternVid**: 234M data sample:`{<caption, youtube_id, start/end_time>}`
   - [✅] **MSR-VTT**: 10K video-caption data：`{<caption, video_id>}`
-  - [ ] [WIP] ModelScope数据集集成
-  - [ ] VideoInstruct-100K, Panda70M, ......
+  - [✅] ModelScope数据集集成
+  - [✅] VideoInstruct-100K, Panda70M, ......
 - [ ] 大规模高质量DJ-SORA数据集
   - [✅] (Data sandbox) 基于DJ-video算子构建和优化多模态数据菜谱 (算子同期持续完善)
-  - [ ] [WIP] 数据源持续扩充：open-datasets, youku, web， ...
-  - [ ] [WIP] 基于DJ菜谱规模化分析、清洗、生成高质量多模态数据集 (OpenVideo, ...)
-  - [ ] [WIP] 基于DJ菜谱形成大规模3DPatch数仓
+  - [✅] 数据源持续扩充：open-datasets, youku, web， ...
+  - [ ] 基于DJ菜谱规模化分析、清洗、生成高质量多模态数据集
+    -  [WIP] 多场景、高动态 
   - ...
 
 ## DJ-SORA数据验证及模型训练
-  - [ ] [WIP] (DJ-Bench101) 探索及完善多模态数据和模型的协同开发，形成benchmark和insights
-  - [ ] [WIP] 类SORA模型训练pipeline集成
-    - [EasyAnimate](https://github.com/aigc-apps/EasyAnimate)
+  - [✅]  探索及完善多模态数据和模型的协同开发，形成benchmark和insights: [paper](https://arxiv.org/abs/2407.11784)
+  - [] [WIP] 类SORA模型训练pipeline集成
+    - [✅] [EasyAnimate](https://github.com/aigc-apps/EasyAnimate)
+    - [✅] [T2V](https://t2v-turbo.github.io/)
+    - [✅] [V-Bench](https://vchitect.github.io/VBench-project/)
     - ...
   - [✅] (Model-Data sandbox) 在相对小的模型和DJ-SORA数据集上，探索形成低开销、可迁移、有指导性的data-model co-design、配置及检查点
   - [ ] [WIP] 更大规模、更多场景使用DJ-SORA数据训练类SORA模型，提高模型性能
-  - ...
+    - [✅] Data-Juicer-T2v, [V-Bench Top1 model](https://huggingface.co/datajuicer/Data-Juicer-T2V)
+    - ...
 
 

docs/DeveloperGuide.md

@@ -11,7 +11,7 @@
 ## Coding Style
 
 We define our styles in `.pre-commit-config.yaml`. Before committing,
-please install `pre-commit` tool to check and modify accordingly:
+please install `pre-commit` tool to automatically check and modify accordingly:
 
 ```shell
 # ===========install pre-commit tool===========
@@ -104,20 +104,22 @@ class StatsKeys(object):
                 return False
     ```
 
-    - If Hugging Face models are used within an operator, you might want to leverage GPU acceleration. To achieve this, declare `self._accelerator = 'cuda'` in the constructor, and ensure that `compute_stats` and `process` methods accept an additional positional argument `rank`.
+    - If Hugging Face models are used within an operator, you might want to leverage GPU acceleration. To achieve this, declare `_accelerator = 'cuda'` in the constructor, and ensure that `compute_stats` and `process` methods accept an additional positional argument `rank`.
 
     ```python
     # ... (same as above)
 
     @OPERATORS.register_module('text_length_filter')
     class TextLengthFilter(Filter):
+
+        _accelerator = 'cuda'
+
         def __init__(self,
                     min_len: PositiveInt = 10,
                     max_len: PositiveInt = sys.maxsize,
                     *args,
                     **kwargs):
             # ... (same as above)
-            self._accelerator = 'cuda'
 
         def compute_stats(self, sample, rank=None):
             # ... (same as above)

docs/DeveloperGuide_ZH.md

@@ -10,7 +10,7 @@
 
 ## 编码规范
 
-我们将编码规范定义在 `.pre-commit-config.yaml` 中。在向仓库贡献代码之前，请使用 `pre-commit` 工具对代码进行规范化。
+我们将编码规范定义在 `.pre-commit-config.yaml` 中。在向仓库贡献代码之前，请使用 `pre-commit` 工具对代码进行自动规范化。
 
 ```shell
 # ===========install pre-commit tool===========
@@ -99,20 +99,22 @@ class StatsKeys(object):
                 return False
     ```
 
-    - 如果在算子中使用了 Hugging Face 模型，您可能希望利用 GPU 加速。为了实现这一点，请在构造函数中声明 `self._accelerator = 'cuda'`，并确保 `compute_stats` 和 `process` 方法接受一个额外的位置参数 `rank`。
+    - 如果在算子中使用了 Hugging Face 模型，您可能希望利用 GPU 加速。为了实现这一点，请在构造函数中声明 `_accelerator = 'cuda'`，并确保 `compute_stats` 和 `process` 方法接受一个额外的位置参数 `rank`。
 
     ```python
     # ... (same as above)
 
     @OPERATORS.register_module('text_length_filter')
     class TextLengthFilter(Filter):
+
+        _accelerator = 'cuda'
+
         def __init__(self,
                     min_len: PositiveInt = 10,
                     max_len: PositiveInt = sys.maxsize,
                     *args,
                     **kwargs):
             # ... (same as above)
-            self._accelerator = 'cuda'
 
         def compute_stats(self, sample, rank=None):
             # ... (same as above)