Hardened PyTorch 2.12 on Ubuntu 24.04

This is a repackaged open source software product wherein additional charges apply for a hardened, AWS-optimized image with verifiable provenance evidence and maintenance. PyTorch 2.12 with CUDA 13.0 runs on an Ubuntu 24.04 LTS base tuned for Amazon EC2, with a documented set of security settings applied on top and a complete provenance evidence pack including a Software Bill of Materials (SBOM).

PyTorch 2.12 Deployment:

1. A Matched GPU Stack: Driver, CUDA runtime, cuDNN, NCCL and the framework build are pinned to a combination known to work together, so a first login goes straight to training or serving instead of debugging a version mismatch.

2. Isolated Virtual Environment: The whole stack lives in a self-contained Python 3.13 virtual environment at /opt/pytorch, leaving the system Python clean and untouched.

3. Notebook and Serving Ready: JupyterLab, TorchServe and tritonclient are preinstalled, and Docker with the NVIDIA Container Toolkit plus the EFA and NCCL OFI libraries cover containerized and multi-node work.

Comprehensive Evidence Pack:

Every AMI carries an 8-file provenance evidence pack under /opt/optimization-report/:

• Tuning_Parameters.txt: Every tuning change, shown against its base-image value, with the reasoning and how to reverse it.
• Security_Parameters.txt: All 31 security settings in the same format; the 10 already at the wanted value are marked unchanged rather than presented as improvements.
• package_changes.txt: Packages upgraded, installed and removed versus the base image.
• sbom.spdx.json: Software Bill of Materials in SPDX format, generated from the dpkg database.
• cve_scan.txt / cve_scan_full.txt.gz: Vulnerability scan grouped by whether an upstream fix exists, with the kernel pinning explained.
• key_files.sha256: SHA-256 checksums of all 7 files this image changed, so the delivered state can be verified in one command.
• README.txt: Index of artifact contents and operational guidance.

Security & Attack Surface:

1. Kernel Network Hardening: 19 network-related kernel parameters are fixed in /etc/sysctl.d/99-security.conf: ICMP redirects are neither accepted nor sent, source-routed packets are rejected, and the rest are pinned at safe values so they cannot drift.

2. Unused Kernel Modules Disabled: 12 rarely used filesystem and network protocol modules are blocked from loading. No NVIDIA, EFA or container module is affected.

3. SSH Login Window Tightened: LoginGraceTime is reduced from 120 to 60 seconds. Root login policy, authentication retries and forwarding are left as the base image ships them.

4. No Outstanding Upstream Fixes: Every package outside the kernel pin was upgraded to the newest version its distribution offers, including updates Ubuntu was still phasing in. The scan reports zero findings with a fix available and not installed.

GPU Stack and Framework Left Intact:

1. Nothing Touches the Stack: The NVIDIA driver, the CUDA runtime and the PyTorch virtual environment are not modified. After the tuning is applied and the instance reboots, nvidia-smi reports the same driver, torch.cuda.is_available() returns true, a GPU tensor operation runs, and the kernel command line is byte for byte identical to the base image - all verified on a GPU instance.

2. Version Pinning Respected: The kernel is held at a fixed version because the driver is built against it. That pinning predates this release and is documented in the evidence pack rather than silently overridden.

AWS Network & Kernel Tuning:

1. Network Stack: TCP BBR congestion control with fair queueing, increased connection backlogs, raised socket buffer ceilings, and MTU probing enabled. Ceilings are only ever raised, never lowered.

2. System Tuning: tuned daemon active with an aws-optimized profile whose bootloader section is deliberately empty, so the network interface keeps its original name after a reboot.

3. Reliability: The systemd journal is capped at 200 MB so logs cannot fill the root filesystem.

Operational Tools:

1. Preinstalled Tools: AWS SSM Agent active, CloudWatch Agent installed (disabled by default).

Maintenance:

1. Maintenance Specifications: Rebuilt and updated bi-weekly to monthly incorporating upstream Ubuntu 24.04 security updates, with artifact documentation refreshed per release.

About PyTorch

The most widely used open-source deep learning framework, here built for CUDA 13.0 and paired with torchvision, torchaudio and Triton, plus cuDNN and NCCL for accelerated and multi-GPU work. It underpins training and inference pipelines across research and production.

About Ubuntu 24.04 LTS

The most widely deployed Linux distribution in the cloud, built on the Debian foundation, with a five-year maintenance window for LTS releases and broad compatibility across AI and container tooling.