diff --git a/README-zh-Hans.md b/README-zh-Hans.md index 57cf90586..ec9014deb 100644 --- a/README-zh-Hans.md +++ b/README-zh-Hans.md @@ -38,12 +38,12 @@
  • 并行训练样例展示
    • @@ -59,6 +59,7 @@
  • @@ -102,6 +103,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的 - 1维, [2维](https://arxiv.org/abs/2104.05343), [2.5维](https://arxiv.org/abs/2105.14500), [3维](https://arxiv.org/abs/2105.14450) 张量并行 - [序列并行](https://arxiv.org/abs/2105.13120) - [零冗余优化器 (ZeRO)](https://arxiv.org/abs/1910.02054) + - [自动并行](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt/auto_parallel_with_gpt) - 异构内存管理 - [PatrickStar](https://arxiv.org/abs/2108.05818) - 使用友好 @@ -113,12 +115,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的

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    ## 并行训练样例展示 -### ViT -

    - -

    -- 14倍批大小和5倍训练速度（张量并行=64） ### GPT-3

    @@ -153,6 +150,12 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的 请访问我们的 [文档](https://www.colossalai.org/) 和 [例程](https://github.com/hpcaitech/ColossalAI-Examples) 以了解详情。 +### ViT +

    + +

    + +- 14倍批大小和5倍训练速度（张量并行=64） ### 推荐系统模型 - [Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), 使用软件Cache实现Embeddings，用更少GPU显存训练更大的模型。 @@ -199,23 +202,38 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的 - [OPT推理服务](https://service.colossalai.org/opt): 无需注册，免费体验1750亿参数OPT在线推理服务 +

    + +

    + +- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): 降低1750亿参数BLOOM模型部署推理成本超10倍

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    ## Colossal-AI 成功案例 ### AIGC -加速AIGC(AI内容生成)模型，如[Stable Diffusion](https://github.com/CompVis/stable-diffusion) +加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion) +

    - +

    -- [Colossal-AI优化Stable Diffusion](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 6.5倍训练加速和预训练成本降低, 微调硬件成本下降约7倍(从RTX3090/4090到RTX3050/2070) +- [训练](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 减少5.6倍显存消耗，硬件成本最高降低46倍(从A100到RTX3060)

    - +

    +- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/hotfix/doc/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调 + +

    + +

    + +- [推理](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): GPU推理显存消耗降低2.5倍 + +

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    ### 生物医药 diff --git a/README.md b/README.md index 36d5c2e82..c58ad5e5c 100644 --- a/README.md +++ b/README.md @@ -38,12 +38,12 @@
  • Parallel Training Demo
    • @@ -59,6 +59,7 @@
  • @@ -104,6 +105,7 @@ distributed training and inference in a few lines. - 1D, [2D](https://arxiv.org/abs/2104.05343), [2.5D](https://arxiv.org/abs/2105.14500), [3D](https://arxiv.org/abs/2105.14450) Tensor Parallelism - [Sequence Parallelism](https://arxiv.org/abs/2105.13120) - [Zero Redundancy Optimizer (ZeRO)](https://arxiv.org/abs/1910.02054) + - [Auto-Parallelism](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt/auto_parallel_with_gpt) - Heterogeneous Memory Management - [PatrickStar](https://arxiv.org/abs/2108.05818) @@ -119,12 +121,6 @@ distributed training and inference in a few lines.

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    ## Parallel Training Demo -### ViT -

    - -

    - -- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64 ### GPT-3

    @@ -158,6 +154,13 @@ distributed training and inference in a few lines. Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details. +### ViT +

    + +

    + +- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64 + ### Recommendation System Models - [Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), utilize software cache to train larger embedding tables with a smaller GPU memory budget. @@ -202,22 +205,37 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt - [OPT Serving](https://service.colossalai.org/opt): Try 175-billion-parameter OPT online services for free, without any registration whatsoever. +

    + +

    + +- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 175-billion-parameter BLOOM by more than 10 times. +

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    ## Colossal-AI in the Real World ### AIGC -Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion](https://github.com/CompVis/stable-diffusion) +Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).

    - +

    -- [Stable Diffusion with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 6.5x faster training and pretraining cost saving, the hardware cost of fine-tuning can be almost 7X cheaper (from RTX3090/4090 to RTX3050/2070) +- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).

    - +

    +- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/hotfix/doc/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject. + +

    + +

    + +- [Inference](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce inference GPU memory consumption by 2.5x. + +

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    ### Biomedicine