The 3rd cell of mtrl_finetuning_example_notebook_v3_prod.ipynb set
S3_TRAINING_DATA using an f-string that referenced an undefined STAGE
variable and an internal `-rftjob-input` bucket pattern left over from
when MTRL was tested in gamma/prod. Executing the cell raised
`NameError: name 'STAGE' is not defined`.
Replace it with a customer-facing placeholder S3 URI, consistent with
the convention used in the sibling sft_finetuning notebook, so users
(and the notebook test engine) can substitute their own dataset path.
Co-authored-by: hrehard <hrehard@amazon.com>
* feat(notebooks): Seed NotebookTestEngine branch with v3-examples suite
Remove the amazon-sagemaker-examples example notebooks (8 category
folders) and replace them with the SageMaker Python SDK v3-examples
notebook suite from aws/sagemaker-python-sdk master:
- inference-examples, ml-ops-examples, model-customization-examples,
training-examples at the repo root
- the two loose root notebooks (sagemaker_v3_setup.ipynb,
iam_role_validation.ipynb) moved into uncategorized/
This layout matches the Notebook Test Engine category rotation
(KNOWN_CATEGORY_FOLDERS + uncategorized catch-all).
* ci: Add Notebook Test Engine PR-check workflow
Add .github/workflows/notebook-tests.yml: on a PR touching any notebook
(**/*.ipynb) targeting the NotebookTestEngine branch, assume the engine
role via GitHub OIDC and start the notebook-test-engine CodeBuild project
in pr_check mode, poll to completion, and reflect the build result as the
PR check (failing notebook rendered + uploaded as a failing-notebooks
artifact). Collaborator gate: collaborators auto-approve, others block on
the manual-approval environment.
* ci: Set MAX_CONCURRENT=18 for PR-check runs
PR runs need a higher concurrency cap than full-scans (project default 15)
because the engine throttles against an account-wide InProgress
processing-job count. With the quota at 20, PR=18 lets a PR claim the
reserved headroom even while a scan holds jobs, leaving ~2 slots for
internally-launched jobs (e.g. Clarify).
* ci: Sync PR-check workflow with default branch (collab github.token fallback)
* fix(ci): Use unpinned refs/pull/N/head for PR checkout
Sync with the default-branch workflow (checkout the PR ref tip).
* Remove workflow file from NotebookTestEngine (it lives on the default branch)
---------
Co-authored-by: hrehard <hrehard@amazon.com>
Add an "SDK-first" section (with a natural-language intent -> v3 interface
table) so AI coding agents default to the SageMaker Python SDK v3 as the
primary interface for SageMaker tasks, rather than dropping to raw boto3
(create_training_job/create_endpoint), the AWS CLI, or hand-rolled framework
scripts. Includes an honest scope note that these files only steer agents that
load them into context.
---
X-AI-Prompt: Add SDK-first (library-selection) guidance so agents default to the SageMaker Python SDK v3 without an explicit nudge
X-AI-Tool: Kiro
Delete the 439 archived v2 notebooks (2068 files) from the default
branch. These notebooks were archived since 2024-08 due to low
telemetry and are preserved on the v2-archive branch. None of the
121 Golden Set or 57 to-migrate notebooks live under archived/, and
no active files reference its contents, so removal is safe.
Add an AGENTS.md (agents.md convention) and llms.txt (llmstxt.org
convention) at the repo root so AI coding agents generate SageMaker
Python SDK v3 code by default instead of deprecated v2 code.
Both files carry a v3-by-default rule, a banned-v2-pattern to v3
replacement table grounded in the official migration guide, the
sagemaker-core/-train/-serve/-mlops module map, and a self-check
step before emitting code.
---
X-AI-Prompt: Create AGENTS.md and llms.txt for the examples repo to steer AI agents to SageMaker Python SDK v3
X-AI-Tool: Kiro
* Revise example prompt and answer to English
Updated example prompt and answer in README with English content.
* Fix typo issue and add link to generative ai readme.md
* [generative_ai] add qwen3 8b finetune train and deploy
* [generative-ai] update content according to feedback
* Remove ModelBuilder which not use
* [generative-ai] update Model Builder to model deploy
* [generative-ai] Fix known issue
* Add notebook demonstrating how to build AI agents using Strands Agents SDK
with models deployed on SageMaker AI endpoints and MLflow observability.
Covers SageMaker JumpStart model deployment, agent tracing with MLflow,
A/B testing with production variants, and evaluation using MLflow GenAI scorers.
* Updated ml_ops/README.md to refer to the notebook in sm-mlflow_eval
* added new trace screenshot and updated deletion code in notebook
* example notebooks for importing llama3 into jumpstart private hub
* Adding util function to handle the hub document update with custom hostingartifacturi
---------
Co-authored-by: Niris Okram <niris@amazon.com>
* Adding the sample for importing custom model to a jumpstart private model hub
* file renamed and updated to align with PR guidelines
* renamed folder to align with PR guidelines
* updated description in the notebook
* switching to torch.inference_mode() from torch.no_grad() to enhance performance
---------
Co-authored-by: Niris Okram <niris@amazon.com>
* Append the default_bucket_prefix to s3 paths if one exists to sample notebooks
**Description**
This change checks the sagemaker session if there is a default_bucket_prefix set. If a default bucket prefix is specified, it is appended it to the s3 path. This ensures sample notebooks that utilize s3, works in SageMaker Unified Studio.
* Update default_bucket and default_bucket_prefix to derive from sagemaker rather than sagemaker_core
**Description**
In order to be compatible with SageMaker Unified Studio, the default_bucket and default_bucket_prefix must be derived from the sagemaker python sdk rather than the sagemaker_core python SDK.
---------
Co-authored-by: Marco Friaz <marfriaz@amazon.com>
**Description**
This change checks the sagemaker session if there is a default_bucket_prefix set. If a default bucket prefix is specified, it is appended it to the s3 path. This ensures sample notebooks that utilize s3, works in SageMaker Unified Studio.
Co-authored-by: Marco Friaz <marfriaz@amazon.com>
Hannah R.