Linux / arm64
Linux / amd64
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.
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 PRODUCT production branch releases every six months with a three-month overlap in between two releases.
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 24.03.
For a comprehensive collection of resources on TensorFlow, including tutorials, documentation, and examples, visit the following links:
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.
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 - May 2024 (24h1) is compatible with NVIDIA AI Enterprise Infrastructure 4.0 and NVIDIA AI Enterprise Infrastructure 4.1 and NVIDIA AI Enterprise Infrastructure 5.
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.
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.
Get access to knowledge base articles and support cases or submit a ticket.
Visit the NVIDIA AI Enterprise Documentation Hub for release documentation, deployment guides and more.
Go to the NVIDIA Licensing Portal to manage your software licenses.