Platform For AI:Train and deploy a PyTorch model

python -m pip install "alipai>=0.4.0"

python -m pai.toolkit.config

#!/bin/sh
set -e

url_prefix="https://ossci-datasets.s3.amazonaws.com/mnist/"
# Alternative mirror if the download is slow:
# url_prefix="http://yann.lecun.com/exdb/mnist/"

mkdir -p data/MNIST/raw/

wget -nv ${url_prefix}train-images-idx3-ubyte.gz -P data/MNIST/raw/
wget -nv ${url_prefix}train-labels-idx1-ubyte.gz -P data/MNIST/raw/
wget -nv ${url_prefix}t10k-images-idx3-ubyte.gz  -P data/MNIST/raw/
wget -nv ${url_prefix}t10k-labels-idx1-ubyte.gz  -P data/MNIST/raw/

- dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
- dataset2 = datasets.MNIST("../data", train=False, transform=transform)

+ # Read the mounted data path from the environment variable.
+ data_path = os.environ.get("PAI_INPUT_TRAIN_DATA", "../data")
+ dataset1 = datasets.MNIST(data_path, train=True, download=True, transform=transform)
+ dataset2 = datasets.MNIST(data_path, train=False, transform=transform)

- if args.save_model:
-     torch.save(model.state_dict(), "mnist_cnn.pt")

+ # Save the model.
+ save_model(model)

+ def save_model(model):
+     """Convert the model to TorchScript and save it to the output path."""
+     output_model_path = os.environ.get("PAI_OUTPUT_MODEL")
+     os.makedirs(output_model_path, exist_ok=True)
+
+     m = torch.jit.script(model)
+     m.save(os.path.join(output_model_path, "mnist_cnn.pt"))

# source: https://github.com/pytorch/examples/blob/main/mnist/main.py
from __future__ import print_function

import argparse
import os

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch,
                    batch_idx * len(data),
                    len(train_loader.dataset),
                    100.0 * batch_idx / len(train_loader),
                    loss.item(),
                )
            )
            if args.dry_run:
                break

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(
                output, target, reduction="sum"
            ).item()  # sum up batch loss
            pred = output.argmax(
                dim=1, keepdim=True
            )  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print(
        "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
            test_loss,
            correct,
            len(test_loader.dataset),
            100.0 * correct / len(test_loader.dataset),
        )
    )

def main():
    # Training settings
    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
    parser.add_argument(
        "--batch-size",
        type=int,
        default=64,
        metavar="N",
        help="input batch size for training (default: 64)",
    )
    parser.add_argument(
        "--test-batch-size",
        type=int,
        default=1000,
        metavar="N",
        help="input batch size for testing (default: 1000)",
    )
    parser.add_argument(
        "--epochs",
        type=int,
        default=14,
        metavar="N",
        help="number of epochs to train (default: 14)",
    )
    parser.add_argument(
        "--lr",
        type=float,
        default=1.0,
        metavar="LR",
        help="learning rate (default: 1.0)",
    )
    parser.add_argument(
        "--gamma",
        type=float,
        default=0.7,
        metavar="M",
        help="Learning rate step gamma (default: 0.7)",
    )
    parser.add_argument(
        "--no-cuda", action="store_true", default=False, help="disables CUDA training"
    )
    parser.add_argument(
        "--dry-run",
        action="store_true",
        default=False,
        help="quickly check a single pass",
    )
    parser.add_argument(
        "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
    )
    parser.add_argument(
        "--log-interval",
        type=int,
        default=10,
        metavar="N",
        help="how many batches to wait before logging training status",
    )
    parser.add_argument(
        "--save-model",
        action="store_true",
        default=False,
        help="For Saving the current Model",
    )
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    train_kwargs = {"batch_size": args.batch_size}
    test_kwargs = {"batch_size": args.test_batch_size}
    if use_cuda:
        cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
        train_kwargs.update(cuda_kwargs)
        test_kwargs.update(cuda_kwargs)

    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
    )

    data_path = os.environ.get("PAI_INPUT_TRAIN_DATA", "../data")
    dataset1 = datasets.MNIST(data_path, train=True, download=True, transform=transform)
    dataset2 = datasets.MNIST(data_path, train=False, transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)

    model = Net().to(device)
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)
        scheduler.step()

    # Save the model.
    save_model(model)

def save_model(model):
    """Convert the model to TorchScript and save it to the specified path."""
    output_model_path = os.environ.get("PAI_OUTPUT_MODEL")
    os.makedirs(output_model_path, exist_ok=True)

    m = torch.jit.script(model)
    m.save(os.path.join(output_model_path, "mnist_cnn.pt"))

if __name__ == "__main__":
    main()

|-- train_src/           # Training script directory (uploaded to OSS before the job starts)
    |-- requirements.txt # Optional: third-party dependencies
    '-- train.py         # Training script

from pai.estimator import Estimator
from pai.image import retrieve

# Get a PAI-managed PyTorch 1.8 GPU image.
image_uri = retrieve(
    "PyTorch", framework_version="1.8PAI", accelerator_type="GPU"
).image_uri
print(image_uri)

est = Estimator(
    # Startup command; working directory is /ml/usercode.
    command="python train.py $PAI_USER_ARGS",
    # Local training script directory to upload.
    source_dir="./train_src/",
    image_uri=image_uri,
    # Instance type: 4 vCPU, 15 GB RAM, 1x NVIDIA T4.
    instance_type="ecs.gn6i-c4g1.xlarge",
    # Hyperparameters are passed to the script as CLI flags via $PAI_USER_ARGS.
    hyperparameters={
        "epochs": 5,
        "batch-size": 64 * 4,
        "lr": 0.5,
    },
    # Extract the loss metric from training logs.
    metric_definitions=[
        {
            "Name": "loss",
            "Regex": r".*loss=([-+]?[0-9]*.?[0-9]+(?:[eE][-+]?[0-9]+)?).*",
        },
    ],
    base_job_name="pytorch_mnist",
)

# If you uploaded with ossutil, set data_uri explicitly:
# data_uri = "oss://<YourOssBucket>/mnist/data/"

est.fit(
    inputs={
        "train_data": data_uri,
    }
)

# OSS path of the trained model.
print("Training job output model path:")
print(est.model_data())

Implantar com um processador integrado

O PAI fornece um processador PyTorch integrado que carrega e serve modelos TorchScript. Use este método quando o modelo não exigir requisitos personalizados de serviço.

Implantar o serviço

from pai.model import Model, InferenceSpec
from pai.predictor import Predictor
from pai.common.utils import random_str

m = Model(
    model_data=est.model_data(),
    # Use the built-in PyTorch processor.
    inference_spec=InferenceSpec(processor="pytorch_cpu_1.10"),
)

p: Predictor = m.deploy(
    service_name="tutorial_pt_mnist_proc_{}".format(random_str(6)),
    instance_type="ecs.c6.xlarge",
)

print(p.service_name)
print(p.service_status)

A implantação geralmente leva vários minutos. O SDK bloqueia a execução até que o serviço esteja pronto.

O método Model.deploy retorna um objeto Predictor. Use Predictor.predict para enviar solicitações e obter previsões.

Execute inferência

import numpy as np

# Input shape: (batch_size, channels, height, width) as float32.
dummy_input = np.random.rand(2, 1, 28, 28).astype(np.float32)

res = p.predict(dummy_input)
print(res)
print(np.argmax(res, 1))

Exclua o serviço

p.delete_service()

Implantar com uma imagem personalizada

Adote esta abordagem quando o modelo exigir pré-processamento ou pós-processamento personalizado, ou tiver dependências não cobertas pelo processador integrado.

Etapa 1: Escrever o código do serviço de inferência

Crie o arquivo infer_src/run.py com uma aplicação Flask que carrega o modelo e lida com requisições HTTP:

import json
from flask import Flask, request
from PIL import Image
import os
import torch
import torchvision.transforms as transforms
import numpy as np
import io

app = Flask(__name__)
# The model is loaded from this path by default.
MODEL_PATH = "/eas/workspace/model/"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torch.jit.load(os.path.join(MODEL_PATH, "mnist_cnn.pt"), map_location=device).to(device)
transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
)

@app.route("/", methods=["POST"])
def predict():
    # Preprocess the image.
    im = Image.open(io.BytesIO(request.data))
    input_tensor = transform(im).to(device)
    input_tensor.unsqueeze_(0)
    # Run inference.
    output_tensor = model(input_tensor)
    pred_res = output_tensor.detach().cpu().numpy()[0]
    return json.dumps(pred_res.tolist())

if __name__ == '__main__':
    app.run(host="0.0.0.0", port=int(os.environ.get("LISTENING_PORT", 8000)))

Estrutura de diretórios esperada:

|-- infer_src/           # Inference service code directory (uploaded to OSS before deployment)
    |-- requirements.txt # Optional: third-party dependencies
    '-- run.py           # Inference server script

Etapa 2: Construir o InferenceSpec

A função container_serving_spec agrupa o código local com uma imagem PyTorch gerenciada pelo PAI. O diretório source_dir é carregado no OSS e montado em /ml/usercode dentro do contêiner.

from pai.model import InferenceSpec, container_serving_spec
from pai.image import retrieve, ImageScope

torch_image_uri = retrieve(
    "PyTorch", framework_version="latest", image_scope=ImageScope.INFERENCE
).image_uri

inf_spec = container_serving_spec(
    command="python run.py",
    source_dir="./infer_src/",
    image_uri=torch_image_uri,
    requirements=["flask==2.0.0", "Werkzeug==2.2.2", "pillow", "torchvision"],
)
print(inf_spec.to_dict())

Etapa 3: Implantar o serviço

from pai.model import Model
from pai.common.utils import random_str

m = Model(
    model_data=est.model_data(),
    inference_spec=inf_spec,
)

predictor = m.deploy(
    service_name="torch_mnist_script_container_{}".format(random_str(6)),
    instance_type="ecs.c6.xlarge",
)

A implantação geralmente leva vários minutos. O SDK bloqueia a execução até que o serviço esteja pronto.

Etapa 4: Execute inferência

Prepare uma imagem de teste do MNIST:

import base64
from PIL import Image
import io

# raw_data is an MNIST image of the digit 9.
raw_data = base64.b64decode(b"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")

im = Image.open(io.BytesIO(raw_data))

Envie a imagem para o serviço de inferência. O serviço aceita bytes brutos da imagem no corpo da requisição HTTP:

from pai.predictor import RawResponse
import numpy as np

resp: RawResponse = predictor.raw_predict(data=raw_data)
print(resp.json())
print(np.argmax(resp.json()))

Etapa 5: Exclua o serviço

predictor.delete_service()