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# Prasyarat
<a name="neo-deployment-hosting-services-prerequisites"></a>

**catatan**  
Ikuti petunjuk di bagian ini jika Anda mengkompilasi model menggunakan AWS SDK untuk Python (Boto3), AWS CLI, atau konsol SageMaker AI. 

Untuk membuat SageMaker Neo-compiled model, Anda memerlukan yang berikut:

1. Gambar Docker Amazon ECR URI. Anda dapat memilih salah satu yang memenuhi kebutuhan Anda dari [daftar ini](https://docs.aws.amazon.com/sagemaker/latest/dg/neo-deployment-hosting-services-container-images.html). 

1. File skrip titik masuk:

   1. **Untuk PyTorch dan model MXNet:**

      *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, Kompilasi, dan Penerapan MNIST dengan Modul MXNet dan SageMaker Neo](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker_neo_compilation_jobs/mxnet_mnist/mxnet_mnist_neo.html), skrip pelatihan () mengimplementasikan fungsi yang diperlukan. `mnist.py`

      *Jika Anda tidak melatih model Anda menggunakan SageMaker AI*, Anda perlu menyediakan file entry point script (`inference.py`) yang dapat digunakan pada saat inferensi. [https://sagemaker.readthedocs.io/en/stable/frameworks/mxnet/using_mxnet.html#model-directory-structure](https://sagemaker.readthedocs.io/en/stable/frameworks/mxnet/using_mxnet.html#model-directory-structure) 

      Saat menggunakan gambar Neo Inference Optimized Container dengan **PyTorch**dan **MXNet** pada 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 menggabungkan`input_fn`,`predict_fn`, dan`output_fn`.

      Berikut ini adalah contoh `inference.py` script dalam direktori bernama `code` (`code/inference.py`) for **PyTorch dan MxNet (Gluon dan** Module). 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
      ```

------

   1.  **Untuk instance inf1 atau onnx, xgboost, gambar kontainer keras** 

      Untuk semua gambar Inference-optimized kontainer Neo lainnya, atau jenis instance inferentia, skrip titik masuk harus mengimplementasikan fungsi-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,, atau. PyTorch TensorFlow

      Untuk contoh cara menggunakan fungsi-fungsi ini, lihat [Notebook Contoh Kompilasi Model Neo](https://docs.aws.amazon.com//sagemaker/latest/dg/neo.html#neo-sample-notebooks). 

   1. **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)](https://www.tensorflow.org/tfx/serving/api_rest) 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](https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/deploying_tensorflow_serving.html#providing-python-scripts-for-pre-pos-processing). 

1. URI bucket Amazon S3 yang berisi artefak model yang dikompilasi.