Add Online Explainability notebooks for SageMaker Clarify

sagemaker-clarify/index.rst

@@ -26,3 +26,12 @@ SageMaker Clarify Model Monitoring
    :maxdepth: 1
 
    ../sagemaker_model_monitor/fairness_and_explainability/SageMaker-Model-Monitor-Fairness-and-Explainability
+
+SageMaker Clarify Online Explainability
+---------------------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   online_explainability/tabular/tabular_online_explainability_with_sagemaker_clarify
+   online_explainability/natural_language_processing/nlp_online_explainability_with_sagemaker_clarify

sagemaker-clarify/online_explainability/natural_language_processing/code/inference.py

@@ -0,0 +1,57 @@
+from io import StringIO
+import numpy as np
+import os
+import pandas as pd
+import json
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from typing import Any, Dict, List
+
+
+def model_fn(model_dir: str) -> Dict[str, Any]:
+    """
+    Load the model for inference
+    """
+    model_path = os.path.join(model_dir, "model")
+
+    # Load HuggingFace tokenizer.
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+    # Load HuggingFace model from disk.
+    model = AutoModelForSequenceClassification.from_pretrained(model_path, local_files_only=True)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    model.eval()
+    model_dict = {"model": model, "tokenizer": tokenizer}
+    return model_dict
+
+
+def predict_fn(input_data: List, model: Dict) -> np.ndarray:
+    """
+    Apply model to the incoming request
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    tokenizer = model["tokenizer"]
+    huggingface_model = model["model"]
+
+    encoded_input = tokenizer(input_data, truncation=True, padding=True, max_length=128, return_tensors="pt")
+    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
+    with torch.no_grad():
+        output = huggingface_model(input_ids=encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"])
+        res = torch.nn.Softmax(dim=1)(output.logits).detach().cpu().numpy()[:, 1]
+        return res
+
+
+def input_fn(request_body: str, request_content_type: str) -> List[str]:
+    """
+    Deserialize and prepare the prediction input
+    """
+    if request_content_type == "application/json":
+        sentences = [json.loads(request_body)]
+
+    elif request_content_type == "text/csv":
+        # We have a single column with the text.
+        sentences = list(pd.read_csv(StringIO(request_body), header=None).values[:, 0].astype(str))
+    else:
+        sentences = request_body
+    return sentences

sagemaker-clarify/online_explainability/natural_language_processing/code/requirements.txt

@@ -0,0 +1,3 @@
+transformers==4.2.1
+torch==1.7.1
+pandas