reloadbrain / starwhale

an MLOps platform

Home Page:https://starwhale.ai

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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.

    • πŸ‘» 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.
    • πŸ‡ 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.

    • πŸ₯• 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.
    • πŸ₯‘ 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.
    • πŸ›₯️ Model Evaluation: PipelineHandler and some report decorators can give you complete, helpful, and user-friendly evaluation reports with only a few lines of codes.
    • πŸ›« 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

Use your own python env

Core Job Workflow

  • 🍰 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
  • β˜• 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.

  • πŸ– 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)

πŸ‘ 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

Create Job Workflow

  • 🍰 STEP1: Install minikube and helm

  • 🍡 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
  • β˜• 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

      Console Artifacts

    3. Create and view an evaluation job

      Show create job screenshot

      Console Create Job

πŸ‘ Congratulations! You have completed the evaluation process for a model.

Documentation, Community, and Support

Contributing

πŸŒΌπŸ‘PRs are always welcomed πŸ‘πŸΊ. See Contribution to Starwhale for more details.

License

Starwhale is licensed under the Apache License 2.0.

About

an MLOps platform

https://starwhale.ai

License:Apache License 2.0


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