Linux / amd64
Linux / arm64
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.
Before you can run an NGC deep learning framework container, your Docker environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in the Running A Container chapter in the NVIDIA Containers And Frameworks User Guide and specify the registry, repository, and tags. For more information about using NGC, refer to the NGC Container User Guide.
The method implemented in your system depends on the DGX OS version installed (for DGX systems), the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers on TITAN PCs, Quadro PCs, or vGPUs.
Select the Tags tab and locate the container image release that you want to run.
In the Pull Tag column, click the icon to copy the
docker pull command.
Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.
Run the container image.
If you have Docker 19.03 or later, a typical command to launch the container is:
docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/tensorflow:xx.xx-tfx-py3
If you have Docker 19.02 or earlier, a typical command to launch the container is:
nvidia-docker run -it --rm -v local_dir:container_dir nvcr.io/nvidia/tensorflow:xx.xx-tfx-py3
-itmeans run in interactive mode
--rmwill delete the container when finished
-vis the mounting directory
local_diris the directory or file from your host system (absolute path) that you want to access from inside your container. For example, the
local_dirin the following path is
If you are inside the container, for example,
ls /data/mnist, you will see the same files as if you issued the
ls /home/jsmith/data/mnist command from outside the container.
container_diris the target directory when you are inside your container. For example,
/data/mnistis the target directory in the example:
xx.xxis the container version. For example,
tfxis the version of TensorFlow. For example,
TensorFlow is run by importing it as a Python module:
$ python >>> import tensorflow as tf >>> print(tf.__version__) 1.15.0
You might want to pull in data and model descriptions from locations outside the container for use by TensorFlow. To accomplish this, the easiest method is to mount one or more host directories as Docker data volumes. You have pulled the latest files and run the container image.
Note: In order to share data between ranks, NCCL may require shared system memory for IPC and pinned (page-locked) system memory resources. The operating system's limits on these resources may need to be increased accordingly. Refer to your system's documentation for details. In particular, Docker containers default to limited shared and pinned memory resources. When using NCCL inside a container, it is recommended that you increase these resources by issuing:
--shm-size=1g --ulimit memlock=-1
in the command line to:
docker run --gpus all
/workspace/README.mdinside the container for information on customizing your TensorFlow image.
For the latest Release Notes, see the TensorFlow Release Notes Documentation website.
For a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the Frameworks Support Matrix.
For more information about TensorFlow, including tutorials, documentation, and examples, see:
To review known CVEs on the 21.07 image, please refer to the Known Issues section of the Product Release Notes.
By pulling and using the container, you accept the terms and conditions of this End User License Agreement.