reloadbrain/starwhale

What is Starwhale

Starwhale is an MLOps platform. It provides Instance, Project, Runtime, Model, and Dataset.

Instance: Each installation of Starwhale is called an instance.
- 👻 Standalone Instance: The simplest form that requires only the Starwhale Client(swcli). swcli is written by pure python3.
- 🎍 On-Premises Instance: Cloud form, we call it private cloud instance. Kubernetes and BareMetal both meet the basic environmental requirements.
- ☁️ Cloud Hosted Instance: Cloud form, we call it public cloud instance. Starwhale team maintains the web service.
Starwhale tries to keep concepts consistent across different types of instances. In this way, people can easily exchange data and migrate between them.
Project: The basic unit for organizing different resources.
ML Basic Elements: The Machine Learning/Deep Learning running environments or artifacts. Starwhale empowers the ML/DL essential elements with packaging, versioning, reproducibility, and shareability.
- 🐌 Runtime: Software dependencies description to "run" a model, which includes python libraries, native libraries, native binaries, etc.
- 🐇 Model: The standard model format used in model delivery.
- 🐫 Dataset: A unified description of how the data and labels are stored and organized. Starwhale datasets can be loaded efficiently.
Running Fundamentals: Starwhale uses Job, Step, and Task to execute ML/DL actions like model training， evaluation, and serving. Starwhale's Controller-Agents structure scales out easily.
- 🥕 Job: A set of programs to do specific work. Each job consists of one or more steps.
- 🌵 Step: Represents distinct stages of the work. Each step consists of one or more tasks.
- 🥑 Task: Operation entity. Tasks are in some specific steps.
Scenarios: Starwhale provides the best practice and out-of-the-box for different ML/DL scenarios.
- 🚝 Model Training(WIP): Use Starwhale Python SDK to record experiment meta, metric, log, and artifact.
- 🛥️ Model Evaluation: PipelineHandler and some report decorators can give you complete, helpful, and user-friendly evaluation reports with only a few lines of codes.
- 🛫 Model Serving(TBD): Starwhale Model can be deployed as a web service or stream service in production with deployment capability, observability, and scalability. Data scientists do not need to write ML/DL irrelevant codes.

MNIST Quick Tour for the standalone instance

Use Notebook

You can try it in Google Colab
Or run example/mnist/notebook.ipynb locally using vscode or jupyterlab

Use your own python env

🍰 STEP1: Installing Starwhale
```
python3 -m pip install starwhale
```
🍵 STEP2: Downloading the MNIST example

To save time in the example downloading, we skip git-lfs and other commits info.
```
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/star-whale/starwhale.git --depth 1
cd starwhale
```
☕ STEP3: Building a runtime
When you first build runtime, creating an isolated python environment and downloading python dependencies will take a lot of time. The command execution time is related to the network environment of the machine and the number of packages in the runtime.yaml. Using the befitting pypi mirror and cache config in the ~/.pip/pip.conf file is a recommended practice.

For users in the mainland of China, the following conf file is an option:
```
[global]
cache-dir = ~/.cache/pip
index-url = https://mirrors.aliyun.com/pypi/simple/
extra-index-url = https://pypi.doubanio.com/simple
```
```
swcli runtime build example/runtime/pytorch
swcli runtime list
swcli runtime info pytorch/version/latest
```

🍞 STEP4: Building a model

Download pre-trained model file:

cd example/mnist
make download-model
# For users in the mainland of China, please add `CN=1` environment for make command:
# CN=1 make download-model
cd -

[Code Example]Write some code with Starwhale Python SDK. Complete code is here.

import typing as t
import torch
from starwhale import Image, PipelineHandler, multi_classification

class MNISTInference(PipelineHandler):
        def __init__(self) -> None:
            super().__init__()
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            self.model = self._load_model(self.device)

        def ppl(self, data: t.Dict[str, t.Any], **kw: t.Any) -> t.Tuple[float, t.List[float]]:
            data_tensor = self._pre(data["img"])
            output = self.model(data_tensor)
            return self._post(output)

        @multi_classification(
            confusion_matrix_normalize="all",
            show_hamming_loss=True,
            show_cohen_kappa_score=True,
            show_roc_auc=True,
            all_labels=[i for i in range(0, 10)],
        )
        def cmp(
            self, ppl_result
        ) -> t.Tuple[t.List[int], t.List[int], t.List[t.List[float]]]:
            result, label, pr = [], [], []
            for _data in ppl_result:
                label.append(_data["input"]["label"])
                result.append(_data["output"][0])
                pr.append(_data["output"][1])
            return label, result, pr

    def _pre(self, input:bytes):
        """write some mnist preprocessing code"""

    def _post(self, input:bytes):
        """write some mnist post-processing code"""

    def _load_model():
        """load your pre trained model"""

[Code Example]Define model.yaml.

name: mnist
run:
    handler: mnist.evaluator:MNISTInference

Run one command to build the model.

swcli model build example/mnist --runtime pytorch/version/latest
swcli model list
swcli model info mnist/version/latest

🍺 STEP5: Building a dataset

Download MNIST RAW data files.

cd example/mnist
make download-data
# For users in the mainland of China, please add `CN=1` environment for make command:
# CN=1 make download-data
cd -

[Code Example]Write some code with Starwhale Python SDK. Full code is here.

import struct
from pathlib import Path
from starwhale import GrayscaleImage

def iter_swds_bin_item():
    root_dir = Path(__file__).parent.parent / "data"

    with (root_dir / "t10k-images-idx3-ubyte").open("rb") as data_file, (
        root_dir / "t10k-labels-idx1-ubyte"
    ).open("rb") as label_file:
        _, data_number, height, width = struct.unpack(">IIII", data_file.read(16))
        _, label_number = struct.unpack(">II", label_file.read(8))
        print(
            f">data({data_file.name}) split data:{data_number}, label:{label_number} group"
        )
        image_size = height * width

        for i in range(0, min(data_number, label_number)):
            _data = data_file.read(image_size)
            _label = struct.unpack(">B", label_file.read(1))[0]
            yield {
                "img": GrayscaleImage(
                    _data,
                    display_name=f"{i}",
                    shape=(height, width, 1),
                ),
                "label": _label,
            }

Run one command to build the dataset.

swcli dataset build example/mnist --handler mnist.dataset:iter_swds_bin_item --runtime pytorch/version/latest
swcli dataset info mnist/version/latest
swcli dataset head mnist/version/latest

Starwhale also supports build dataset with pure python sdk. You can try it in Google Colab.

🍖 STEP6: Running an evaluation job

swcli eval run --model mnist/version/latest --dataset mnist/version/latest --runtime pytorch/version/latest
swcli eval list
swcli eval info $(swcli eval list | grep mnist | grep success | awk '{print $1}' | head -n 1)

👏 Now, you have completed the fundamental steps for Starwhale standalone. Let's go ahead and finish the tutorial on the on-premises instance.

MNIST Quick Tour for on-premises instance

🍰 STEP1: Install minikube and helm
- Minikube 1.25+
- Helm 3.2.0+
🍵 STEP2: Start minikube
```
minikube start
```
For users in the mainland of China, please add some external parameters. The following command was well tested; you may also try another kubernetes version.
```
minikube start --image-mirror-country=cn --kubernetes-version=1.25.3
```
If there is no kubectl bin in your machine, you may use minikube kubectl or alias kubectl="minikube kubectl --" alias command.

🍵 STEP3: Installing Starwhale

helm repo add starwhale https://star-whale.github.io/charts
helm repo update
helm pull starwhale/starwhale --untar --untardir ./charts

helm upgrade --install starwhale ./charts/starwhale -n starwhale --create-namespace -f ./charts/starwhale/values.minikube.global.yaml

For users in the mainland of China, use values.minikube.global.yaml.

helm upgrade --install starwhale ./charts/starwhale -n starwhale --create-namespace -f ./charts/starwhale/values.minikube.cn.yaml

After the installation is successful, the following prompt message appears:

Release "starwhale" has been upgraded. Happy Helming!
NAME: starwhale
LAST DEPLOYED: Tue Feb 14 16:25:03 2023
NAMESPACE: starwhale
STATUS: deployed
REVISION: 14
NOTES:
******************************************
Chart Name: starwhale
Chart Version: 0.1.0
App Version: latest
Starwhale Image:
    - server: ghcr.io/star-whale/server:latest
Runtime default Image:
- runtime image: ghcr.io/star-whale/starwhale:latest

******************************************
Web Visit:

Port Forward Visit:
- starwhale controller:
    - run: kubectl port-forward --namespace starwhale svc/controller 8082:8082
    - visit: http://localhost:8082
- minio admin:
    - run: kubectl port-forward --namespace starwhale svc/minio 9001:9001
    - visit: http://localhost:9001
- mysql:
    - run: kubectl port-forward --namespace starwhale svc/mysql 3306:3306
    - visit: mysql -h 127.0.0.1 -P 3306 -ustarwhale -pstarwhale

******************************************
Login Info:
- starwhale: u:starwhale, p:abcd1234
- minio admin: u:minioadmin, p:minioadmin

*_* Enjoy to use Starwhale Platform. *_*

Then keep checking the minikube service status until all deployments are running.

kubectl get deployments -n starwhale

NAME	READY	UP-TO-DATE	AVAILABLE	AGE
controller	1/1	1	1	5m
minio	1/1	1	1	5m
mysql	1/1	1	1	5m

Make the Starwhale controller accessible locally with the following command:

kubectl port-forward --namespace starwhale svc/controller 8082:8082

☕ STEP4: Upload the artifacts to the cloud instance
pre-prepared artifacts Before starting this tutorial, the following three artifacts should already exist on your machine：
- a starwhale model named mnist
- a starwhale dataset named mnist
- a starwhale runtime named pytorch
The above three artifacts are what we built on our machine using starwhale.
1. Use swcli to operate the remote server First, log in to the server:
```
swcli instance login --username starwhale --password abcd1234 --alias dev http://localhost:8082
```
2. Start copying the model, dataset, and runtime that we constructed earlier:
```
swcli model copy mnist/version/latest dev/project/starwhale
swcli dataset copy mnist/version/latest dev/project/starwhale
swcli runtime copy pytorch/version/latest dev/project/starwhale
```
🍞 STEP5: Use the web UI to run an evaluation
1. Log in Starwhale instance: let's use the username(starwhale) and password(abcd1234) to open the server web UI(http://localhost:8082/).
2. Then, we will see the project named 'project_for_mnist' that we created earlier with swcli. Click the project name, you will see the model, runtime, and dataset uploaded in the previous step.
  
  Show the uploaded artifacts screenshots
3. Create and view an evaluation job
  
  Show create job screenshot

👏 Congratulations! You have completed the evaluation process for a model.

Documentation, Community, and Support

Visit Starwhale HomePage.
More information in the official documentation.
For general questions and support, join the Slack.
For bug reports and feature requests, please use Github Issue.
To get community updates, follow @starwhaleai on Twitter.
For Starwhale artifacts, please visit:
- Python Package on Pypi.
- Helm Charts on Artifacthub.
- Docker Images on Docker Hub, Github Packages and Starwhale Registry.
Additionally, you can always find us at developer@starwhale.ai.

Contributing

🌼👏PRs are always welcomed 👍🍺. See Contribution to Starwhale for more details.

License

Starwhale is licensed under the Apache License 2.0.

reloadbrain / starwhale