*[2022/11] [Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper](https://medium.com/@yangyou_berkeley/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper-85e970fe207b)
*[2022/10] [Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000](https://medium.com/@yangyou_berkeley/use-a-laptop-to-analyze-90-of-proteins-with-a-single-gpu-inference-sequence-exceeding-10-000-4c8f0a389cd)
*[2022/10] [Embedding Training With 1% GPU Memory and 100 Times Less Budget for Super-Large Recommendation Model](https://medium.com/@yangyou_berkeley/embedding-training-with-1-gpu-memory-and-10-times-less-budget-an-open-source-solution-for-6b4c3aba07a8)
*[2022/09] [HPC-AI Tech Completes $6 Million Seed and Angel Round Fundraising](https://medium.com/@hpcaitech/hpc-ai-tech-completes-6-million-seed-and-angel-round-fundraising-led-by-bluerun-ventures-in-the-892468cc2b02)
*[2022/07] [Colossal-AI Seamlessly Accelerates Large Models at Low Costs with Hugging Face](https://medium.com/@yangyou_berkeley/colossal-ai-seamlessly-accelerates-large-models-at-low-costs-with-hugging-face-4d1a887e500d)
- 2x faster training, or 50% longer sequence length
### PaLM
-[PaLM-colossalai](https://github.com/hpcaitech/PaLM-colossalai): Scalable implementation of Google's Pathways Language Model ([PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html)).
-[Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because public pretrained model weights.
- 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt)[[Online Serving]](https://service.colossalai.org/opt)
Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details.
### Recommendation System Models
-[Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), utilize software cache to train larger embedding tables with a smaller GPU memory budget.
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-[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)
-[FastFold](https://github.com/hpcaitech/FastFold): accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.
If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
```shell
NO_CUDA_EXT=1 pip install .
```
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## Use Docker
### Pull from DockerHub
You can directly pull the docker image from our [DockerHub page](https://hub.docker.com/r/hpcaitech/colossalai). The image is automatically uploaded upon release.
### Build On Your Own
Run the following command to build a docker image from Dockerfile provided.
> Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing `docker build`. More details can be found [here](https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime).
> We recommend you install Colossal-AI from our [project page](https://www.colossalai.org) directly.
```bash
cd ColossalAI
docker build -t colossalai ./docker
```
Run the following command to start the docker container in interactive mode.
```bash
docker run -ti--gpus all --rm--ipc=host colossalai bash
```
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## Community
Join the Colossal-AI community on [Forum](https://github.com/hpcaitech/ColossalAI/discussions),
and [WeChat](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png"qrcode") to share your suggestions, feedback, and questions with our engineering team.
## Contributing
If you wish to contribute to this project, please follow the guideline in [Contributing](./CONTRIBUTING.md).
Thanks so much to all of our amazing contributors!
