starwhale 0.3.6

An MLOps Platform for Model Evaluation

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(TBD): 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

  git clone https://github.com/star-whale/starwhale.git
  

  If git-lfs has not been previously installed in the local environment(the command is git lfs install), you need to download the trained model file.

  wget https://media.githubusercontent.com/media/star-whale/starwhale/main/example/mnist/models/mnist_cnn.pt -O example/mnist/models/mnist_cnn.pt
  

• ☕ STEP3: Building a runtime

  cd example/runtime/pytorch
  swcli runtime build .
  swcli runtime list
  swcli runtime info pytorch/version/latest
  

• 🍞 STEP4: Building a model

  • Enter example/mnist directory:

  cd ../../mnist
  

  • Write some code with Starwhale Python SDK. Complete code is here.

  import typing as t
  import torch
  from starwhale import Image, PipelineHandler, PPLResultIterator, 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, img: Image, **kw: t.Any) -> t.Tuple[t.List[int], t.List[float]]:
           data_tensor = self._pre(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: PPLResultIterator
       ) -> t.Tuple[t.List[int], t.List[int], t.List[t.List[float]]]:
           result, label, pr = [], [], []
           for _data in ppl_result:
               label.append(_data["annotations"]["label"])
               result.extend(_data["result"][0])
               pr.extend(_data["result"][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"""
  

  • Define model.yaml.

  name: mnist
  model:
    - models/mnist_cnn.pt
  config:
    - config/hyperparam.json
  run:
    handler: mnist.evaluator:MNISTInference
  

  • Run one command to build the model.

   swcli model build .
   swcli model info mnist/version/latest
  

• 🍺 STEP5: Building a dataset

  • Download MNIST RAW data files.

   mkdir -p data && cd data
   wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
   wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
   gzip -d *.gz
   cd ..
   ls -lah data/*
  

  • Write some code with Starwhale Python SDK. Full code is here.

   import struct
   import typing as t
   from pathlib import Path
   from starwhale import BuildExecutor
  
   class DatasetProcessExecutor(SWDSBinBuildExecutor):
       def iter_item(self) -> t.Generator[t.Tuple[t.Any, t.Any], None, None]:
           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 GrayscaleImage(
                       _data,
                       display_name=f"{i}",
                       shape=(height, width, 1),
                   ), {"label": _label}
  

  • Define dataset.yaml.

   name: mnist
   handler: mnist.dataset:DatasetProcessExecutor
   attr:
     alignment_size: 1k
     volume_size: 4M
     data_mime_type: "x/grayscale"
  

  • Run one command to build the dataset.

   swcli dataset build .
   swcli dataset info 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 -vvv eval run --model mnist/version/latest --dataset mnist/version/latest --runtime pytorch/version/latest
   swcli eval list
   swcli eval info ${version}
  

👏 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 these startup parameters：--image-mirror-country=cn --image-repository=registry.cn-hangzhou.aliyuncs.com/google_containers. 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 install --devel my-starwhale starwhale/starwhale -n starwhale --create-namespace --set minikube.enabled=true
  

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

  NAME: my-starwhale
  LAST DEPLOYED: Thu Jun 23 14:48:02 2022
  NAMESPACE: starwhale
  STATUS: deployed
  REVISION: 1
  NOTES:
  ******************************************
  Chart Name: starwhale
  Chart Version: 0.3.0
  App Version: 0.3.0
  ...
  
  Port Forward Visit:
  - starwhale controller:
      - run: kubectl port-forward --namespace starwhale svc/my-starwhale-controller 8082:8082
      - visit: http://localhost:8082
  - minio admin:
      - run: kubectl port-forward --namespace starwhale svc/my-starwhale-minio 9001:9001
      - visit: http://localhost:9001
  - mysql:
      - run: kubectl port-forward --namespace starwhale svc/my-starwhale-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 using Starwhale. *_*
  

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

  kubectl get pods -n starwhale
  

  NAME	READY	STATUS	RESTARTS	AGE
  my-starwhale-controller-7d864558bc-vxvb8	1/1	Running	0	1m
  my-starwhale-minio-7d45db75f6-7wq9b	1/1	Running	0	2m
  my-starwhale-mysql-0	1/1	Running	0	2m

  Make the Starwhale controller accessible locally with the following command:

  kubectl port-forward --namespace starwhale svc/my-starwhale-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 and ghcr.io.

• Additionally, you can always find us at [e-mail].

Contributing

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

License

Starwhale is licensed under the Apache License 2.0.