Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
https://github.com/star-whale/starwhale

an MLOps/LLMOps platform
https://github.com/star-whale/starwhale
ai cloud-native dataset datastore fine-tuning infra kubernetes llm llmops mlops model-evaluation runtime
Last synced: 4 months ago
JSON representation
an MLOps/LLMOps platform
Host: GitHub
URL: https://github.com/star-whale/starwhale
Owner: star-whale
License: apache-2.0
Created: 2021-12-30T14:26:20.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-02-05T07:30:03.000Z (5 months ago)
Last Synced: 2024-03-06T04:44:01.985Z (4 months ago)
Topics: ai, cloud-native, dataset, datastore, fine-tuning, infra, kubernetes, llm, llmops, mlops, model-evaluation, runtime
Language: Java
Homepage: https://starwhale.ai
Size: 70.6 MB
Stars: 184
Watchers: 6
Forks: 30
Open Issues: 118
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: code-of-conduct.md
Lists

awesome-mlops - Starwhale - An MLOps/LLMOps platform for model building, evaluation, and fine-tuning. (Model Testing & Validation)
awesome-llmops - Starwhale - tuning. | ![GitHub Badge](https://img.shields.io/github/stars/star-whale/starwhale.svg?style=flat-square) | (Large Scale Deployment / ML Platforms)
README

        


    

    An MLOps/LLMOps Platform


🚀 ️☁️ [Starwhale Cloud](https://cloud.starwhale.cn) is now open to the public, try it! 🎉🍻







    





    





    





    





    





    





    





    





    







    


        English |

        中文

    


## What is Starwhale

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development liftcycle, enabling teams to optimize their workflows around key areas like model building, evaluation, release and fine-tuning.

![products](https://starwhale-examples.oss-cn-beijing.aliyuncs.com/docs/products.png)

Starwhale meets diverse deployment needs with three flexible configurations:

- 🐥 **Standalone** - Deployed in a local development environment, managed by the `swcli` command-line tool, meeting development and debugging needs.

- 🦅 **Server** - Deployed in a private data center, relying on a Kubernetes cluster, providing centralized, web-based, and secure services.

- 🦉 **Cloud** - Hosted on a public cloud, with the access address . The Starwhale team is responsible for maintenance, and no installation is required. You can start using it after registering an account.

As its core, Starwhale abstracts **Model**, **Runtime** and **Dataset** as first-class citizens - providing the fundamentals for streamlined operations. Starwhale further delivers tailored capabilities for common workflow scenarios including:

- 🔥 **Models Evaluation** - Implement robust, production-scale evaluations with minimal coding through the Python SDK.

- 🌟 **Live Demo** - Interactively assess model performance through user-friendly web interfaces.

- 🌊 **LLM Fine-tuning** - End-to-end toolchain from efficient fine-tuning to comparative benchmarking and publishing.

Starwhale is also an open source platform, using the [Apache-2.0 license](https://github.com/star-whale/starwhale/blob/main/LICENSE). The Starwhale framework is designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

![framework](https://starwhale-examples.oss-cn-beijing.aliyuncs.com/docs/framework.png)

## Key Concepts

### 🐘 Starwhale Dataset

Starwhale Dataset offers efficient data storage, loading, and visualization capabilities, making it a dedicated data management tool tailored for the field of machine learning and deep learning

![dataset overview](https://starwhale-examples.oss-cn-beijing.aliyuncs.com/docs/dataset-overview.svg)

```python

import torch

from starwhale import dataset, Image

# build dataset for starwhale cloud instance

with dataset("https://cloud.starwhale.cn/project/starwhale:public/dataset/test-image", create="empty") as ds:

    for i in range(100):

        ds.append({"image": Image(f"{i}.png"), "label": i})

    ds.commit()

# load dataset

ds = dataset("https://cloud.starwhale.cn/project/starwhale:public/dataset/test-image")

print(len(ds))

print(ds[0].features.image.to_pil())

print(ds[0].features.label)

torch_ds = ds.to_pytorch()

torch_loader = torch.utils.data.DataLoader(torch_ds, batch_size=5)

print(next(iter(torch_loader)))

```

### 🐇 Starwhale Model

Starwhale Model is a standard format for packaging machine learning models that can be used for various purposes, like model fine-tuning, model evaluation, and online serving. A Starwhale Model contains the model file, inference codes, configuration files, and any other files required to run the model.

![overview](https://starwhale-examples.oss-cn-beijing.aliyuncs.com/docs/model-overview.svg)

```bash

# model build

swcli model build . --module mnist.evaluate --runtime pytorch/version/v1 --name mnist

# model copy from standalone to cloud

swcli model cp mnist https://cloud.starwhale.cn/project/starwhale:public

# model run

swcli model run --uri mnist --runtime pytorch --dataset mnist

swcli model run --workdir . --module mnist.evaluator --handler mnist.evaluator:MNISTInference.cmp

```

### 🐌 Starwhale Runtime

Starwhale Runtime aims to provide a reproducible and sharable running environment for python programs. You can easily share your working environment with your teammates or outsiders, and vice versa. Furthermore, you can run your programs on Starwhale Server or Starwhale Cloud without bothering with the dependencies.

![overview](https://starwhale-examples.oss-cn-beijing.aliyuncs.com/docs/runtime-overview.svg)

```bash

# build from runtime.yaml, conda env, docker image or shell

swcli runtime build --yaml runtime.yaml

swcli runtime build --conda pytorch --name pytorch-runtime --cuda 11.4

swcli runtime build --docker pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime

swcli runtime build --shell --name pytorch-runtime

# runtime activate

swcli runtime activate pytorch

# integrated with model and dataset

swcli model run --uri test --runtime pytorch

swcli model build . --runtime pytorch

swcli dataset build --runtime pytorch

```

### 🐄 Starwhale Evaluation

Starwhale Evaluation enables users to evaluate sophisticated, production-ready distributed models by writing just a few lines of code with Starwhale Python SDK.

```python

import typing as t

import gradio

from starwhale import evaluation

from starwhale.api.service import api

def model_generate(image):

    ...

    return predict_value, probability_matrix

@evaluation.predict(

    resources={"nvidia.com/gpu": 1},

    replicas=4,

)

def predict_image(data: dict, external: dict) -> None:

    return model_generate(data["image"])

@evaluation.evaluate(use_predict_auto_log=True, needs=[predict_image])

def evaluate_results(predict_result_iter: t.Iterator):

    for _data in predict_result_iter:

        ...

    evaluation.log_summary({"accuracy": 0.95, "benchmark": "test"})

@api(gradio.File(), gradio.Label())

def predict_view(file: t.Any) -> t.Any:

    with open(file.name, "rb") as f:

        data = Image(f.read(), shape=(28, 28, 1))

    _, prob = predict_image({"image": data})

    return {i: p for i, p in enumerate(prob)}

```

### 🦍 Starwhale Fine-tuning

Starwhale Fine-tuning provides a full workflow for Large Language Model(LLM) tuning, including batch model evaluation, live demo and model release capabilities. Starwhale Fine-tuning Python SDK is very simple.

```python

import typing as t

from starwhale import finetune, Dataset

from transformers import Trainer

@finetune(

    resources={"nvidia.com/gpu":4, "memory": "32G"},

    require_train_datasets=True,

    require_validation_datasets=True,

    model_modules=["evaluation", "finetune"],

)

def lora_finetune(train_datasets: t.List[Dataset], val_datasets: t.List[Dataset]) -> None:

    # init model and tokenizer

    trainer = Trainer(

        model=model, tokenizer=tokenizer,

        train_dataset=train_datasets[0].to_pytorch(), # convert Starwhale Dataset into Pytorch Dataset

        eval_dataset=val_datasets[0].to_pytorch())

    trainer.train()

    trainer.save_state()

    trainer.save_model()

    # save weights, then Starwhale SDK will package them into Starwhale Model

```

## Installation

### 🍉 Starwhale Standalone

Requirements: Python 3.7~3.11 in the Linux or macOS os.

```bash

python3 -m pip install starwhale

```

### 🥭 Starwhale Server

Starwhale Server is delivered as a Docker image, which can be run with Docker directly or deployed to a Kubernetes cluster. For the laptop environment, using `swcli server start` command is a appropriate choice that depends on Docker and Docker-Compose.

```bash

swcli server start

```

## Quick Tour

We use [MNIST](https://paperswithcode.com/dataset/mnist) as the hello world example to show the basic Starwhale Model workflow.

### 🪅 MNIST Evaluation in Starwhale Standalone

- Use your own Python environment, follow the [Standalone quickstart doc](https://starwhale.cn/docs/en/next/getting-started/standalone/).

- Use Google Colab environment, follow the [Jupyter notebook example](https://colab.research.google.com/github/star-whale/starwhale/blob/main/example/notebooks/quickstart-standalone.ipynb).

### 🪆 MNIST Evaluation in Starwhale Server

- Run it in the your private Starwhale Server instance, please read [Server installation(minikube)](https://starwhale.cn/docs/en/next/server/installation/minikube) and [Server quickstart](https://starwhale.cn/docs/en/next/getting-started/server) docs.

- Run it in the [Starwhale Cloud](https://cloud.starwhale.cn), please read [Cloud quickstart doc](https://starwhale.cn/docs/en/next/getting-started/cloud).

## Examples

- 🔥 Helloworld: [Cloud](https://cloud.starwhale.cn/projects/15/evaluations), [Code](https://github.com/star-whale/starwhale/tree/main/example/helloworld).

- 🚀 LLM:

  - 🐊 OpenSource LLMs Leaderboard: [Evaluation](https://cloud.starwhale.cn/projects/349/evaluations), [Code](https://github.com/star-whale/starwhale/tree/main/example/llm-leaderboard)

  - 🐢 Llama2: [Run llama2 chat in five minutes](https://starwhale.cn/docs/en/blog/run-llama2-chat-in-five-minutes/), [Code](https://github.com/star-whale/starwhale/tree/main/example/LLM/llama2)

  - 🦎 Stable Diffusion: [Cloud Demo](https://cloud.starwhale.cn/projects/374/models), [Code](https://github.com/star-whale/stable-diffusion-webui)

  - 🦙 LLAMA [evaluation and fine-tune](https://github.com/star-whale/starwhale/tree/main/example/LLM/llama)

  - 🎹 Text-to-Music: [Cloud Demo](https://cloud.starwhale.cn/projects/400/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/LLM/musicgen)

  - 🍏 Code Generation: [Cloud Demo](https://cloud.starwhale.cn/projects/404/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/code-generation/code-llama)

- 🌋 Fine-tuning:

  - 🐏 Baichuan2: [Cloud Demo](https://cloud.starwhale.cn/projects/401/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/llm-finetune/models/baichuan2)

  - 🐫 ChatGLM3: [Cloud Demo](https://cloud.starwhale.cn/projects/401/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/llm-finetune/models/chatglm3)

  - 🦏 Stable Diffusion: [Cloud Demo](https://cloud.starwhale.cn/projects/374/spaces/3/fine-tune-runs), [Code](https://github.com/star-whale/starwhale/tree/main/example/stable-diffusion/txt2img-ft)

- 🦦 Image Classification:

  - 🐻‍❄️ MNIST: [Cloud Demo](https://cloud.starwhale.cn/projects/392/evaluations), [Code](https://github.com/star-whale/starwhale/tree/main/example/mnist).

  - 🦫 [CIFAR10](https://github.com/star-whale/starwhale/tree/main/example/cifar10)

  - 🦓 Vision Transformer(ViT): [Cloud Demo](https://cloud.starwhale.cn/projects/399/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/image-classification)

- 🐃 Image Segmentation:

  - Segment Anything(SAM): [Cloud Demo](https://cloud.starwhale.cn/projects/398/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/image-segmentation)

- 🐦 Object Detection:

  - 🦊 YOLO: [Cloud Demo](https://cloud.starwhale.cn/projects/397/overview), [Code](https://github.com/star-whale/starwhale/tree/main/example/object-detection)

  - 🐯 [Pedestrian Detection](https://github.com/star-whale/starwhale/tree/main/example/PennFudanPed)

- 📽️ Video Recognition: [UCF101](https://github.com/star-whale/starwhale/tree/main/example/ucf101)

- 🦋 Machine Translation: [Neural machine translation](https://github.com/star-whale/starwhale/tree/main/example/nmt)

- 🐜 Text Classification: [AG News](https://github.com/star-whale/starwhale/tree/main/example/text_cls_AG_NEWS)

- 🎙️ Speech Recognition: [Speech Command](https://github.com/star-whale/starwhale/tree/main/example/speech_command)

## Documentation, Community, and Support

- Visit [Starwhale HomePage](https://starwhale.ai).

- More information in the [official documentation](https://doc.starwhale.ai).

- For general questions and support, join the [Slack](https://starwhale.slack.com/).

- For bug reports and feature requests, please use [Github Issue](https://github.com/star-whale/starwhale/issues).

- To get community updates, follow [@starwhaleai](https://twitter.com/starwhaleai) on Twitter.

- For Starwhale artifacts, please visit:

  - Python Package on [Pypi](https://pypi.org/project/starwhale/).

  - Helm Charts on [Artifacthub](https://artifacthub.io/packages/helm/starwhale/starwhale).

  - Docker Images on [Docker Hub](https://hub.docker.com/u/starwhaleai), [Github Packages](https://github.com/orgs/star-whale/packages) and [Starwhale Registry](https://docker-registry.starwhale.cn/).

- Additionally, you can always find us at *[email protected]*.

## Contributing

🌼👏**PRs are always welcomed** 👍🍺. See [Contribution to Starwhale](https://doc.starwhale.ai/community/contribute) for more details.

## License

Starwhale is licensed under the [Apache License 2.0](https://github.com/star-whale/starwhale/blob/main/LICENSE).