Important Notice: Production Branch October 2024 has reach its End of Life July 2025. To receive enterprise support, monthly bug fixes, and security updates for high and critical software vulnerabilities, please transition your application to Production Branch May 2025. For more information, refer to the NVIDIA AI Entperise Release Branches documentation.

What Is TensorFlow 2?

TensorFlow is an open-source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

What Is TensorFlow 2 Production Branch October 2024?

The TensorFlow 2 Production Branch, exclusively available with NVIDIA AI Enterprise, is a 9-month supported, API-stable branch that includes monthly fixes for high and critical software vulnerabilities. This branch provides a stable and secure environment for building your mission-critical AI applications. The production branch releases every six months with a three-month overlap in between two releases.

Getting started with TensorFlow 2 Production Branch

Before you start, ensure that your environment is set up by following one of the deployment guides available in the NVIDIA AI Enterprise Documentation.

For an overview of the features included in the TensorFlow 2 Production Branch, please refer to the Release Notes TensorFlow 2 24.08.

For a comprehensive collection of resources on TensorFlow, including tutorials, documentation, and examples, visit the following links:

• TensorFlow tutorials

• TensorFlow API

• TensorFlow User Guide

Additionally, if you're looking for information on Docker containers and guidance on running a container, review the Containers For Deep Learning Frameworks User Guide.

Compatible Infrastructure Software Versions

For the optimized performance, it is highly recommended to deploy the supported NVIDIA AI Enterprise Infrastructure software in conjunction with your AI software.

Production Branch - October 2024 (24h2) is compatible with NVIDIA AI Enterprise Infrastructure 4 and NVIDIA AI Enterprise Infrastructure 5.

Security Vulnerabilities in Open Source Packages

Please review the Security Scanning tab to view the latest security scan results.

For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Known Issues

There is a bug in RAPIDS whereby attempting to serialize any cudf dataframe whose column names are numpy integers will result in a TypeError similar to TypeError: can not serialize 'numpy.int64' object. A fix will be provided in the next Production Branch October 2024 (PB24h2) release. As a workaround, users should rewrite the dataframe column names by getting the underlying int/float value from the numpy type and reassigning that value as the column name.

Warning: The pickle module is not secure. Only unpickle data you trust. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never unpickle data that could have come from an untrusted source, or that could have been tampered with.

These images contain versions of 'dask' and associated libraries which may show up as being vulnerable to CVE-2024-10096. The maintainers of 'dask' reject the CVE, as described in https://github.com/dask/community/issues/415. In short, Dask is a distributed computing framework that uses unauthenticated communication between participants in the clusters it orchestrates. Follow the advice at https://distributed.dask.org/en/stable/limitations.html?highlight=host#security and only create Dask clusters within networks that you trust.

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