Deploy Nova Bedrock

1# Cell 0 [markdown]: Model Deployment — Bedrock
2
3# Cell 1: Setup
4
5# %pip install --upgrade 'sagemaker>=3.7.1,<4.0' --quiet  # NOTEBOOK_ONLY
6
7# Cell 2: Configuration
8
9import json
10import os
11
12import boto3
13
14os.environ["AWS_DEFAULT_REGION"] = "[REGION]"
15
16from pprint import pprint
17
18from sagemaker.core import Attribution, set_attribution
19from sagemaker.core.resources import TrainingJob
20from sagemaker.serve.bedrock_model_builder import BedrockModelBuilder
21
22set_attribution(Attribution.SAGEMAKER_AGENT_PLUGIN)
23
24REGION = "[REGION]"
25TRAINING_JOB_NAME = "[TRAINING_JOB_NAME]"
26ROLE_ARN = "[ROLE_ARN]"
27CUSTOM_MODEL_NAME = "[CUSTOM_MODEL_NAME]"
28
29# Cell 3: Build and Deploy to Bedrock
30
31training_job = TrainingJob.get(training_job_name=TRAINING_JOB_NAME)
32print(f"Training job status: {training_job.training_job_status}")
33
34bedrock_builder = BedrockModelBuilder(model=training_job)
35
36deployment_result = bedrock_builder.deploy(
37    role_arn=ROLE_ARN,
38    custom_model_name=CUSTOM_MODEL_NAME,
39)
40
41deployment_arn = deployment_result["customModelDeploymentArn"]
42pprint(f"Deployment Result: {deployment_result}")
43
44# Cell 4: Test Inference
45
46bedrock_runtime = boto3.client("bedrock-runtime", region_name=REGION)
47message = "What is the capital of France?"
48print(f"Model Inference Message: {message}")
49resp = bedrock_runtime.converse(
50    modelId=deployment_arn,
51    messages=[{"role": "user", "content": [{"text": message}]}],
52    inferenceConfig={"maxTokens": 100, "temperature": 0.7},
53)
54
55response_str = resp["output"]["message"]["content"][0]["text"]
56print(f"Model Response: {response_str}")
57
58# Cell 5: Save Manifest
59# Save manifest - record output of workflow step for future reference
60from pathlib import Path
61
62manifest_dir = Path("[PROJECT_DIR]") / "manifests"
63manifest_dir.mkdir(parents=True, exist_ok=True)
64manifest_path = manifest_dir / f"deploy-{CUSTOM_MODEL_NAME}.json"
65manifest_path.write_text(
66    json.dumps(
67        {
68            "custom_model_name": CUSTOM_MODEL_NAME,
69        },
70        indent=2,
71    )
72)
73print(f"Manifest saved: {manifest_path}")