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Model Training - helixml/helix

Model Training in Helix Project

The Helix project uses KubeDL for model training and serving. Here are the possible options for model training in Helix with examples:

  1. Distributed Training: KubeDL supports distributed training, which allows training large models on multiple GPUs or machines. This can significantly reduce the time required for training. Here's an example of distributed training using KubeDL:
apiVersion: "kubedl.io/v1alpha1"
kind: "TrainingJob"
metadata:
  name: "example-distributed-training"
spec:
  trainingTaskImage: "kubedl/tensorflow:2.3.0"
  replicas: 2
  template:
    metadata:
      labels:
        app: "example-distributed-training"
    spec:
      containers:
      - name: "main"
        image: "kubedl/tensorflow:2.3.0"
        command:
        - "/bin/bash"
        - "-c"
        - |
          # Your training script here
          distributed_training.sh

Source: https://kubedl.io/docs/video

  1. Fine-tuning Models: Morphling, a model tuning library, can be used to fine-tune pre-trained models. This can be useful when you have a smaller dataset or when you want to adapt a pre-trained model to a new task. Here's an example of fine-tuning a model using Morphling:
from morphling import Pipeline

pipeline = Pipeline.load("saving_directory")

# Modify the pipeline for fine-tuning
# ...

pipeline.train()

Source: https://kubedl.io/docs/video

  1. Health Checks: Helix uses Kubernetes for deployment, which includes health checks for model training jobs. This ensures that the training jobs are running as expected and can help to identify and recover from failures. Here's an example of adding health checks to a Kubernetes deployment:
apiVersion: apps/v1
kind: Deployment
metadata:
  name: example-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: example
  template:
    metadata:
      labels:
        app: example
    spec:
      containers:
      - name: example
        image: example-image
        ports:
        - containerPort: 80
        readinessProbe:
          httpGet:
            path: /healthz
            port: 80
          initialDelaySeconds: 5
          periodSeconds: 5

Source: https://kubebyexample.com/learning-paths/developing-nodejs/add-health-checks

  1. Go Mocking Framework: For unit testing and mocking dependencies in Go, the Go Mocking Framework can be used. This can be useful when testing model training code that has dependencies on external services or resources. Here's an example of using the Go Mocking Framework:
import (
  "github.com/golang/mock/gomock"
  "github.com/stretchr/testify/assert"
  "testing"
)

func TestModelTraining(t *testing.T) {
  ctrl := gomock.NewController(t)
  defer ctrl.Finish()

  mockService := NewMockService(ctrl)
  mockService.EXPECT().DoSomething().Return(nil)

  service := NewService(mockService)
  err := service.TrainModel()

  assert.NoError(t, err)
}

Source: https://github.com/golang/mock

  1. Go CMP: Go CMP is a code linter and formatter that can be used to ensure that the model training code follows a consistent style and format. This can help to improve the readability and maintainability of the code. Here's an example of using Go CMP:
go get golang.org/x/tools/cmd/goimports

# Run goimports on all Go files in the current directory
find . -name '*.go' -exec goimports -w {} \;

Source: https://github.com/golang/go/wiki/CodeReviewComments#goimports