Prasyarat

Gambar Docker Amazon ECR URI. Anda dapat memilih salah satu yang memenuhi kebutuhan Anda dari daftar ini.

File skrip titik masuk:

1. Untuk PyTorch dan MXNet model:

   Jika Anda melatih model Anda menggunakan SageMaker AI, skrip pelatihan harus mengimplementasikan fungsi yang dijelaskan di bawah ini. Skrip pelatihan berfungsi sebagai skrip titik masuk selama inferensi. Dalam contoh yang dirinci dalam Pelatihan MNIST, Kompilasi dan Penerapan dengan MXNet Modul dan SageMaker Neo, skrip pelatihan (mnist.py) mengimplementasikan fungsi yang diperlukan.

   Jika Anda tidak melatih model Anda menggunakan SageMaker AI, Anda perlu menyediakan file entry point script (inference.py) yang dapat digunakan pada saat inferensi. Berdasarkan kerangka kerja— MXNet atau PyTorch —lokasi skrip inferensi harus sesuai dengan Struktur Direktori Model SDK SageMaker Python untuk atau Struktur Direktori Model untuk MxNet. PyTorch

   Saat menggunakan gambar Neo Inference Optimized Container dengan PyTorchdan MXNetpada tipe instance CPU dan GPU, skrip inferensi harus mengimplementasikan fungsi-fungsi berikut:

   • model_fn: Memuat model. (Opsional)

   • input_fn: Mengkonversi payload permintaan masuk ke array numpy.

   • predict_fn: Melakukan prediksi.

   • output_fn: Mengkonversi output prediksi ke payload respon.

   • Atau, Anda dapat menentukan transform_fn untuk menggabungkaninput_fn,predict_fn, danoutput_fn.

   Berikut ini adalah contoh inference.py skrip dalam direktori bernama code (code/inference.py) untuk PyTorch dan MXNet (Gluon dan Modul). Contoh pertama memuat model dan kemudian menyajikannya pada data gambar pada GPU:

   MXNet Module

   import numpy as np
   import json
   import mxnet as mx
   import neomx  # noqa: F401
   from collections import namedtuple
   
   Batch = namedtuple('Batch', ['data'])
   
   # Change the context to mx.cpu() if deploying to a CPU endpoint
   ctx = mx.gpu()
   
   def model_fn(model_dir):
       # The compiled model artifacts are saved with the prefix 'compiled'
       sym, arg_params, aux_params = mx.model.load_checkpoint('compiled', 0)
       mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
       exe = mod.bind(for_training=False,
                      data_shapes=[('data', (1,3,224,224))],
                      label_shapes=mod._label_shapes)
       mod.set_params(arg_params, aux_params, allow_missing=True)
       
       # Run warm-up inference on empty data during model load (required for GPU)
       data = mx.nd.empty((1,3,224,224), ctx=ctx)
       mod.forward(Batch([data]))
       return mod
   
   
   def transform_fn(mod, image, input_content_type, output_content_type):
       # pre-processing
       decoded = mx.image.imdecode(image)
       resized = mx.image.resize_short(decoded, 224)
       cropped, crop_info = mx.image.center_crop(resized, (224, 224))
       normalized = mx.image.color_normalize(cropped.astype(np.float32) / 255,
                                     mean=mx.nd.array([0.485, 0.456, 0.406]),
                                     std=mx.nd.array([0.229, 0.224, 0.225]))
       transposed = normalized.transpose((2, 0, 1))
       batchified = transposed.expand_dims(axis=0)
       casted = batchified.astype(dtype='float32')
       processed_input = casted.as_in_context(ctx)
   
       # prediction/inference
       mod.forward(Batch([processed_input]))
   
       # post-processing
       prob = mod.get_outputs()[0].asnumpy().tolist()
       prob_json = json.dumps(prob)
       return prob_json, output_content_type

   MXNet Gluon

   import numpy as np
   import json
   import mxnet as mx
   import neomx  # noqa: F401
   
   # Change the context to mx.cpu() if deploying to a CPU endpoint
   ctx = mx.gpu()
   
   def model_fn(model_dir):
       # The compiled model artifacts are saved with the prefix 'compiled'
       block = mx.gluon.nn.SymbolBlock.imports('compiled-symbol.json',['data'],'compiled-0000.params', ctx=ctx)
       
       # Hybridize the model & pass required options for Neo: static_alloc=True & static_shape=True
       block.hybridize(static_alloc=True, static_shape=True)
       
       # Run warm-up inference on empty data during model load (required for GPU)
       data = mx.nd.empty((1,3,224,224), ctx=ctx)
       warm_up = block(data)
       return block
   
   
   def input_fn(image, input_content_type):
       # pre-processing
       decoded = mx.image.imdecode(image)
       resized = mx.image.resize_short(decoded, 224)
       cropped, crop_info = mx.image.center_crop(resized, (224, 224))
       normalized = mx.image.color_normalize(cropped.astype(np.float32) / 255,
                                     mean=mx.nd.array([0.485, 0.456, 0.406]),
                                     std=mx.nd.array([0.229, 0.224, 0.225]))
       transposed = normalized.transpose((2, 0, 1))
       batchified = transposed.expand_dims(axis=0)
       casted = batchified.astype(dtype='float32')
       processed_input = casted.as_in_context(ctx)
       return processed_input
   
   
   def predict_fn(processed_input_data, block):
       # prediction/inference
       prediction = block(processed_input_data)
       return prediction
   
   def output_fn(prediction, output_content_type):
       # post-processing
       prob = prediction.asnumpy().tolist()
       prob_json = json.dumps(prob)
       return prob_json, output_content_type

   PyTorch 1.4 and Older

   import os
   import torch
   import torch.nn.parallel
   import torch.optim
   import torch.utils.data
   import torch.utils.data.distributed
   import torchvision.transforms as transforms
   from PIL import Image
   import io
   import json
   import pickle
   
   
   def model_fn(model_dir):
       """Load the model and return it.
       Providing this function is optional.
       There is a default model_fn available which will load the model
       compiled using SageMaker Neo. You can override it here.
   
       Keyword arguments:
       model_dir -- the directory path where the model artifacts are present
       """
   
       # The compiled model is saved as "compiled.pt"
       model_path = os.path.join(model_dir, 'compiled.pt')
       with torch.neo.config(model_dir=model_dir, neo_runtime=True):
           model = torch.jit.load(model_path)
           device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
           model = model.to(device)
   
       # We recommend that you run warm-up inference during model load
       sample_input_path = os.path.join(model_dir, 'sample_input.pkl')
       with open(sample_input_path, 'rb') as input_file:
           model_input = pickle.load(input_file)
       if torch.is_tensor(model_input):
           model_input = model_input.to(device)
           model(model_input)
       elif isinstance(model_input, tuple):
           model_input = (inp.to(device) for inp in model_input if torch.is_tensor(inp))
           model(*model_input)
       else:
           print("Only supports a torch tensor or a tuple of torch tensors")
           return model
   
   
   def transform_fn(model, request_body, request_content_type,
                    response_content_type):
       """Run prediction and return the output.
       The function
       1. Pre-processes the input request
       2. Runs prediction
       3. Post-processes the prediction output.
       """
       # preprocess
       decoded = Image.open(io.BytesIO(request_body))
       preprocess = transforms.Compose([
           transforms.Resize(256),
           transforms.CenterCrop(224),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[
                   0.485, 0.456, 0.406], std=[
                   0.229, 0.224, 0.225]),
       ])
       normalized = preprocess(decoded)
       batchified = normalized.unsqueeze(0)
       # predict
       device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
       batchified = batchified.to(device)
       output = model.forward(batchified)
   
       return json.dumps(output.cpu().numpy().tolist()), response_content_type

   PyTorch 1.5 and Newer

   import os
   import torch
   import torch.nn.parallel
   import torch.optim
   import torch.utils.data
   import torch.utils.data.distributed
   import torchvision.transforms as transforms
   from PIL import Image
   import io
   import json
   import pickle
   
   
   def model_fn(model_dir):
       """Load the model and return it.
       Providing this function is optional.
       There is a default_model_fn available, which will load the model
       compiled using SageMaker Neo. You can override the default here.
       The model_fn only needs to be defined if your model needs extra
       steps to load, and can otherwise be left undefined.
   
       Keyword arguments:
       model_dir -- the directory path where the model artifacts are present
       """
   
       # The compiled model is saved as "model.pt"
       model_path = os.path.join(model_dir, 'model.pt')
       device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
       model = torch.jit.load(model_path, map_location=device)
       model = model.to(device)
   
       return model
   
   
   def transform_fn(model, request_body, request_content_type,
                       response_content_type):
       """Run prediction and return the output.
       The function
       1. Pre-processes the input request
       2. Runs prediction
       3. Post-processes the prediction output.
       """
       # preprocess
       decoded = Image.open(io.BytesIO(request_body))
       preprocess = transforms.Compose([
                                   transforms.Resize(256),
                                   transforms.CenterCrop(224),
                                   transforms.ToTensor(),
                                   transforms.Normalize(
                                       mean=[
                                           0.485, 0.456, 0.406], std=[
                                           0.229, 0.224, 0.225]),
                                       ])
       normalized = preprocess(decoded)
       batchified = normalized.unsqueeze(0)
       
       # predict
       device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
       batchified = batchified.to(device)
       output = model.forward(batchified)
       return json.dumps(output.cpu().numpy().tolist()), response_content_type                                                
                                               

2. Untuk instance inf1 atau onnx, xgboost, gambar kontainer keras

   Untuk semua gambar kontainer yang dioptimalkan Neo Inference lainnya, atau jenis instance inferentia, skrip titik masuk harus mengimplementasikan fungsi berikut untuk Neo Deep Learning Runtime:

   • neo_preprocess: Mengkonversi payload permintaan masuk ke array numpy.

   • neo_postprocess: Mengonversi output prediksi dari Neo Deep Learning Runtime menjadi badan respons.

     catatan

     Dua fungsi sebelumnya tidak menggunakan salah satu fungsi, MXNet, PyTorch atau. TensorFlow

   Untuk contoh cara menggunakan fungsi-fungsi ini, lihat Notebook Contoh Kompilasi Model Neo.

3. Untuk TensorFlow model

   Jika model Anda memerlukan logika pra-dan pasca-pemrosesan khusus sebelum data dikirim ke model, maka Anda harus menentukan inference.py file skrip titik masuk yang dapat digunakan pada saat inferensi. Script harus mengimplementasikan baik sepasang input_handler dan output_handler fungsi atau fungsi handler tunggal.

   catatan

   Perhatikan bahwa jika fungsi handler diimplementasikan, input_handler dan output_handler diabaikan.

   Berikut ini adalah contoh kode inference.py skrip yang dapat Anda kumpulkan dengan model kompilasi untuk melakukan pra-dan sesudah pemrosesan khusus pada model klasifikasi gambar. Klien SageMaker AI mengirimkan file gambar sebagai tipe application/x-image konten ke input_handler fungsi, di mana ia dikonversi ke JSON. File gambar yang dikonversi kemudian dikirim ke Tensorflow Model Server (TFX) menggunakan REST API.

   import json
   import numpy as np
   import json
   import io
   from PIL import Image
   
   def input_handler(data, context):
       """ Pre-process request input before it is sent to TensorFlow Serving REST API
       
       Args:
       data (obj): the request data, in format of dict or string
       context (Context): an object containing request and configuration details
       
       Returns:
       (dict): a JSON-serializable dict that contains request body and headers
       """
       f = data.read()
       f = io.BytesIO(f)
       image = Image.open(f).convert('RGB')
       batch_size = 1
       image = np.asarray(image.resize((512, 512)))
       image = np.concatenate([image[np.newaxis, :, :]] * batch_size)
       body = json.dumps({"signature_name": "serving_default", "instances": image.tolist()})
       return body
   
   def output_handler(data, context):
       """Post-process TensorFlow Serving output before it is returned to the client.
       
       Args:
       data (obj): the TensorFlow serving response
       context (Context): an object containing request and configuration details
       
       Returns:
       (bytes, string): data to return to client, response content type
       """
       if data.status_code != 200:
           raise ValueError(data.content.decode('utf-8'))
   
       response_content_type = context.accept_header
       prediction = data.content
       return prediction, response_content_type

   Jika tidak ada pra-atau pasca-pemrosesan khusus, klien SageMaker AI mengonversi gambar file ke JSON dengan cara yang sama sebelum mengirimnya ke titik akhir AI. SageMaker

   Untuk informasi selengkapnya, lihat Deploying to TensorFlow Serving Endpoints di Python SageMaker SDK.

URI bucket Amazon S3 yang berisi artefak model yang dikompilasi.