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docs(tensorflow): apply flowmark formatter
Collapses pipe-aligned tables to compact form and re-wraps prose at 150 columns to satisfy the flowmark pre-commit hook.
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‎docs/tensorflow/changelog/index.md‎

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Changelog for the Amazon Linux 2023-based TensorFlow SageMaker training images (`2.21-gpu-py312-cu129-amzn2023-sagemaker` and
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`2.21-cpu-py312-amzn2023-sagemaker`).
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* * *
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## TensorFlow 2.21 — 2026-07-10
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‎docs/tensorflow/deployment/sagemaker.md‎

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# Amazon SageMaker AI Deployment
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Use the TensorFlow DLC for training jobs launched via the SageMaker Python SDK or `boto3`. The images bundle the `sagemaker-tensorflow-training`
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toolkit, OpenMPI for multi-node coordination, and SageMaker-specific Python libraries (`mlflow`, `smclarify`, `pandas`, `seaborn`, `s3fs`,
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etc.).
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toolkit, OpenMPI for multi-node coordination, and SageMaker-specific Python libraries (`mlflow`, `smclarify`, `pandas`, `seaborn`, `s3fs`, etc.).
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The TensorFlow 2.21 DLC is a **SageMaker-only** release — there is no EC2 or EKS variant of this image. Point your estimators, processors, and
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training jobs at the SageMaker image URIs shown below.
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SageMaker provisions EFA on the instance types that support it (e.g., `ml.p5.48xlarge`, `ml.p4d.24xlarge`). The GPU image ships with EFA 1.49.0 and
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OpenMPI 4.1.8 pre-installed — no extra plumbing in your training script.
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Multi-node peer discovery is handled by the `sagemaker_tensorflow_container` training toolkit, which parses
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`/opt/ml/input/config/resourceconfig.json` at container start to enumerate hosts, current host rank, and network interface. Peers are addressed
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by the SageMaker-assigned hostnames (e.g., `algo-1`, `algo-2`) that resolve inside the training cluster — your script does not need to know about
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this.
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Multi-node peer discovery is handled by the `sagemaker_tensorflow_container` training toolkit, which parses `/opt/ml/input/config/resourceconfig.json`
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at container start to enumerate hosts, current host rank, and network interface. Peers are addressed by the SageMaker-assigned hostnames (e.g.,
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`algo-1`, `algo-2`) that resolve inside the training cluster — your script does not need to know about this.
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## Container Layout
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| Path | Purpose |
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| ------------------------------------ | ---------------------------------------------------------------------------------------------- |
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| `/opt/ml/input/data/<channel_name>/` | Training data SageMaker mounts from S3 (one subdir per channel) |
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| `/opt/ml/model/` | Write your final model artifacts here — SageMaker uploads to `OutputDataConfig.s3_output_path` |
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| `/opt/ml/output/` | Auxiliary outputs (logs, checkpoints) |
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| `/opt/ml/code/` | Your training source dir (populated from the SDK's `source_dir`) |
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| `/opt/venv/` | Python venv with TensorFlow + DLC libraries |
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| Path | Purpose |
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| --- | --- |
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| `/opt/ml/input/data/<channel_name>/` | Training data SageMaker mounts from S3 (one subdir per channel) |
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| `/opt/ml/model/` | Write your final model artifacts here — SageMaker uploads to `OutputDataConfig.s3_output_path` |
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| `/opt/ml/output/` | Auxiliary outputs (logs, checkpoints) |
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| `/opt/ml/code/` | Your training source dir (populated from the SDK's `source_dir`) |
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| `/opt/venv/` | Python venv with TensorFlow + DLC libraries |
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## Notes
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- The image sets `SAGEMAKER_TRAINING_MODULE=sagemaker_tensorflow_container.training:main` — this is the entry-point the SageMaker
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training toolkit invokes at container start, which in turn launches your `entry_point` script.
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- The image sets `SAGEMAKER_TRAINING_MODULE=sagemaker_tensorflow_container.training:main` — this is the entry-point the SageMaker training toolkit
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invokes at container start, which in turn launches your `entry_point` script.
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- For a baseline driver/AMI compatible with these CUDA 12.9 images, request the latest SageMaker training AMI when launching jobs.

‎docs/tensorflow/index.md‎

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## Images
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| Platform | Variant | Image |
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| --------------- | ------- | ----------------------------------------------------------------------------------------------------- |
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| {{ sagemaker }} | GPU | `public.ecr.aws/deep-learning-containers/tensorflow-training:2.21-gpu-py312-cu129-amzn2023-sagemaker` |
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| {{ sagemaker }} | CPU | `public.ecr.aws/deep-learning-containers/tensorflow-training:2.21-cpu-py312-amzn2023-sagemaker` |
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| Platform | Variant | Image |
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| --- | --- | --- |
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| {{ sagemaker }} | GPU | `public.ecr.aws/deep-learning-containers/tensorflow-training:2.21-gpu-py312-cu129-amzn2023-sagemaker` |
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| {{ sagemaker }} | CPU | `public.ecr.aws/deep-learning-containers/tensorflow-training:2.21-cpu-py312-amzn2023-sagemaker` |
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All images are also available on the [ECR Public Gallery](https://gallery.ecr.aws/deep-learning-containers/tensorflow-training). For private ECR URIs,
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see [Image Access](../get_started/index.md).
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- **CUDA 12.9.1** with **cuDNN 9.5.1.17** (`nvidia-cudnn-cu12`) and **NCCL 2.30.7** (`nvidia-nccl-cu12`) for multi-GPU collectives
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- **[EFA](https://aws.amazon.com/hpc/efa/) 1.49.0** with **OpenMPI 4.1.8** for low-latency multi-node communication on EFA-capable instances
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- **[MPI for Python](https://mpi4py.readthedocs.io/) (`mpi4py`)** for multi-process Python coordination
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- **[SageMaker training toolkits](https://github.com/aws/sagemaker-tensorflow-training-toolkit)** — `sagemaker-tensorflow-training`, `sagemaker-training`
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- **[SageMaker training toolkits](https://github.com/aws/sagemaker-tensorflow-training-toolkit)** — `sagemaker-tensorflow-training`,
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`sagemaker-training`
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- **[MLflow](https://mlflow.org/) 3.9+**, **`sagemaker>=3.4.0`**, **`smclarify`**, and **`sagemaker-experiments` 0.1.45**
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- **Data & ML tooling** — `tensorflow-io` 0.37, `tensorflow-datasets`, `pandas`, `scikit-learn`, `scipy`, `numpy` (2.1+), `Pillow`, `h5py`,
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`opencv-python`, `numba`, `plotly`, `seaborn`, `shap`, `bokeh`, `imageio`, `cloudpickle`
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- **Reproducible** — pinned via `pyproject.toml` + `uv.lock` for every image variant
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- **Security-patched** — continuously maintained with security patches from {{ aws }} on an Amazon Linux 2023 base
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TensorFlow 2.21 is the first TensorFlow DLC on Amazon Linux 2023 and Python 3.12. Prior TensorFlow DLCs shipped on Ubuntu with earlier Python versions;
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this release moves the base OS and interpreter forward alongside the framework bump.
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TensorFlow 2.21 is the first TensorFlow DLC on Amazon Linux 2023 and Python 3.12. Prior TensorFlow DLCs shipped on Ubuntu with earlier Python
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versions; this release moves the base OS and interpreter forward alongside the framework bump.

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