AI开发工具与平台生态系统

import pandas as pd
from sklearn.preprocessing import StandardScaler

# 数据加载和预处理
def preprocess_data(file_path):
    # 读取数据
    df = pd.read_csv(file_path)
    
    # 数据清洗
    df = df.dropna()  # 删除缺失值
    df = df.drop_duplicates()  # 删除重复值
    
    # 特征工程
    scaler = StandardScaler()
    numerical_cols = df.select_dtypes(include=['number']).columns
    df[numerical_cols] = scaler.fit_transform(df[numerical_cols])
    
    return df

# 使用示例
processed_data = preprocess_data('dataset.csv')

import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel

class TextClassifier(nn.Module):
    def __init__(self, model_name, num_classes):
        super().__init__()
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)
        self.classifier = nn.Linear(self.model.config.hidden_size, num_classes)
        
    def forward(self, input_ids, attention_mask):
        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.last_hidden_state[:, 0]  # CLS token
        logits = self.classifier(pooled_output)
        return logits

# 模型实例化
model = TextClassifier('bert-base-uncased', num_classes=2)

import mlflow
import mlflow.pytorch

# MLflow实验跟踪
def train_with_tracking(model, train_loader, val_loader, epochs):
    with mlflow.start_run():
        # 记录参数
        mlflow.log_param("epochs", epochs)
        mlflow.log_param("learning_rate", 0.001)
        
        for epoch in range(epochs):
            # 训练循环
            train_loss = train_epoch(model, train_loader)
            val_acc = validate(model, val_loader)
            
            # 记录指标
            mlflow.log_metric("train_loss", train_loss, step=epoch)
            mlflow.log_metric("val_accuracy", val_acc, step=epoch)
        
        # 记录模型
        mlflow.pytorch.log_model(model, "model")

import horovod.torch as hvd

# 初始化Horovod
hvd.init()

# 设置GPU
torch.cuda.set_device(hvd.local_rank())

# 模型和优化器
model = MyModel()
optimizer = torch.optim.Adam(model.parameters())

# 分布式优化器
optimizer = hvd.DistributedOptimizer(optimizer)

# 广播参数
hvd.broadcast_parameters(model.state_dict(), root_rank=0)

import deepspeed

# DeepSpeed配置
ds_config = {
    "train_batch_size": 32,
    "gradient_clipping": 1.0,
    "fp16": {
        "enabled": True
    },
    "zero_optimization": {
        "stage": 2
    }
}

# 初始化DeepSpeed
model, optimizer, _, _ = deepspeed.initialize(
    model=model,
    optimizer=optimizer,
    config=ds_config
)

import optuna

def objective(trial):
    # 定义搜索空间
    lr = trial.suggest_float('lr', 1e-5, 1e-1, log=True)
    batch_size = trial.suggest_categorical('batch_size', [16, 32, 64])
    hidden_size = trial.suggest_int('hidden_size', 64, 512)
    
    # 训练模型
    model = SimpleModel(hidden_size)
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    
    # 评估模型
    accuracy = train_and_evaluate(model, optimizer, batch_size)
    return accuracy

# 优化
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)

# 创建模型档案
torch-model-archiver --model-name my_model \
    --version 1.0 \
    --model-file model.py \
    --serialized-file model.pth \
    --handler handler.py

# 启动服务
torchserve --start --model-store model_store --models my_model=my_model.mar

# TensorFlow Serving配置
model_config_file = """
model_config_list: {
  config: {
    name: "my_model",
    base_path: "/path/to/models",
    model_platform: "tensorflow"
  }
}
"""

# 启动服务
# tensorflow_model_server --model_config_file=/path/to/config

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["python", "app.py"]

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-model-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-model
  template:
    metadata:
      labels:
        app: ai-model
    spec:
      containers:
      - name: model-container
        image: ai-model:latest
        ports:
        - containerPort: 8000
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"

# 初始化DVC
dvc init

# 添加数据文件
dvc add data/training_dataset.csv

# 推送数据到远程存储
dvc push

# 拉取数据
dvc pull

from kedro.pipeline import Pipeline, node
from kedro.runner import SequentialRunner

def load_data(filepath):
    return pd.read_csv(filepath)

def process_data(data):
    # 数据处理逻辑
    return processed_data

def train_model(data):
    # 模型训练逻辑
    return model

# 构建流水线
pipeline = Pipeline([
    node(load_data, "raw_data", "processed_data", name="load_raw_data"),
    node(process_data, "processed_data", "clean_data", name="process_data"),
    node(train_model, "clean_data", "model", name="train_model")
])

# 运行流水线
runner = SequentialRunner()
runner.run(pipeline)

name: ML Pipeline
on: [push, pull_request]

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.9
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
    - name: Run training
      run: |
        python train.py
    - name: Run tests
      run: |
        pytest tests/
    - name: Upload model
      uses: actions/upload-artifact@v2
      with:
        name: trained-model
        path: models/

import sagemaker
from sagemaker.huggingface import HuggingFace

# SageMaker训练
huggingface_estimator = HuggingFace(
    entry_point='train.py',
    source_dir='src',
    role=sagemaker.get_execution_role(),
    transformers_version='4.12',
    pytorch_version='1.9',
    py_version='py38',
    instance_type='ml.p3.2xlarge',
    instance_count=1,
    hyperparameters={
        'epochs': 3,
        'train_batch_size': 16,
        'model_name': 'bert-base-uncased'
    }
)

# 启动训练
huggingface_estimator.fit({'train': 's3://my-bucket/train/'})

import cv2
import numpy as np

# 图像预处理
def preprocess_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.resize(img, (224, 224))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = img.astype(np.float32) / 255.0
    return img

# 目标检测
def detect_objects(model, image):
    predictions = model.predict(image)
    return predictions

from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2 import model_zoo

cfg = get_cfg()
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)

# 预测
outputs = predictor(image)

import spacy

# 加载模型
nlp = spacy.load("en_core_web_sm")

# 文本处理
def process_text(text):
    doc = nlp(text)
    
    # 命名实体识别
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    
    # 词性标注
    pos_tags = [(token.text, token.pos_) for token in doc]
    
    # 依存句法分析
    dependencies = [(token.text, token.dep_, token.head.text) for token in doc]
    
    return {
        'entities': entities,
        'pos_tags': pos_tags,
        'dependencies': dependencies
    }

from fbprophet import Prophet

# 创建模型
model = Prophet(
    growth='linear',
    seasonality_mode='multiplicative',
    yearly_seasonality=True,
    weekly_seasonality=True
)

# 训练模型
model.fit(df)  # df需要有'ds'和'y'列

# 预测
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

import tensorrt as trt
import numpy as np

def optimize_with_tensorrt(engine_path):
    # 创建TensorRT运行时
    runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
    
    # 加载优化后的引擎
    with open(engine_path, 'rb') as f:
        engine = runtime.deserialize_cuda_engine(f.read())
    
    return engine

import onnx
import onnxruntime as ort

# 转换模型到ONNX
torch.onnx.export(
    model,
    dummy_input,
    "model.onnx",
    export_params=True,
    opset_version=11
)

# 使用ONNX Runtime推理
session = ort.InferenceSession("model.onnx")
output = session.run(None, {'input': input_data})

import torch.quantization as quant

# 准备量化
model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
model_prepared = torch.quantization.prepare(model, inplace=False)

# 校准
with torch.no_grad():
    for data, target in calibration_loader:
        model_prepared(data)

# 转换为量化模型
model_quantized = torch.quantization.convert(model_prepared, inplace=False)

from prometheus_client import Counter, Histogram, start_http_server

# 定义指标
request_counter = Counter('model_requests_total', 'Total model requests')
prediction_histogram = Histogram('prediction_time_seconds', 'Prediction time')

def predict_with_monitoring(model, input_data):
    request_counter.inc()
    
    with prediction_histogram.time():
        result = model.predict(input_data)
    
    return result

# 启动监控服务器
start_http_server(8000)

from alibi_detect.cd import MMDDrift

# 训练时数据分布
reference_data = X_train

# 创建漂移检测器
cd = MMDDrift(
    x_ref=reference_data,
    p_val=0.05,
    backend='pytorch'
)

# 检测漂移
preds = cd.predict(X_current)
if preds['data']['is_drift']:
    print("检测到数据漂移！")