JAX is a framework for high-performance numerical computing and\nmachine learning research. It includes Numpy-like\nAPIs, automatic differentiation, XLA acceleration and\nsimple primitives for scaling across GPUs.

The JAX NGC Container comes with all dependencies included, providing an\neasy place to start developing applications in areas such as NLP, Computer Vision,\nMultimodality, physics-based simulations, reinforcement learning, drug discovery, and neural rendering.

The JAX NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that\nenable and optimize GPU performance. This container may also include\nmodifications to the JAX source code in order to\nmaximize performance and compatibility. This container also includes software\nfor accelerating ETL (DALI and training (cuDNN, NCCL).

For building neural networks, the JAX NGC\nContainer includes Flax, a neural network library with support for\ncommon deep learning models, layers and optimizers. We\nalso include a container for Paxml, a framework for training LLMs such as GPT, and a container for T5x, a framework for training T5 and other Flax-based models. You can use the JAX, Paxml, or T5x containers for your deep learning workloads or install your own favorite libraries on top of them.

Prerequisites

Using the JAX NGC\nContainer requires the host system to have the following installed:

For supported versions, see the Framework Containers Support Matrix and\nthe NVIDIA Container Toolkit Documentation.

No other installation, compilation, or\ndependency management is required. It is not necessary to install the NVIDIA\nCUDA Toolkit.

Running JAX

To run a container, issue the appropriate\ncommand as explained in the Running A Container chapter in the NVIDIA Containers For Deep\nLearning Frameworks User's Guide and specify the registry, repository, and\ntags. For more information about using NGC, refer to the NGC Container User Guide.

The following command assumes you want to run the JAX container interactively, where 23.08 is the container version. For example, 23.08 for August 2023 release:

docker run --gpus all -it --rm nvcr.io/nvidia/jax:23.08-py3\n

To pull data and model descriptions from locations outside the container for use by JAX or save results to locations outside the container, mount one or more host directories as Docker data volumes.

See /workspace/README.md for information on getting started and customizing your JAX image.

If you use multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with might not be enough. To increase the shared memory size, issue one of the following commands:

--shm-size=1g\n--ulimit memlock=-1\n

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. 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.

Running JAX in multi-node, multi-GPU

One of the key features of JAX is its easy\nscaling primitives for running JAX processes across multiple accelerators. The\nJAX distributed system allows JAX processes to discover each other and share\ntopology information, perform health checks, ensure that all processes shut\ndown if any process dies, and can be used for distributed checkpointing.

For information on how to set up a cluster and\nlaunch JAX processes, please refer to JAX readthedocs. For HPC cluster environments with a Slurm or OpenMPI\nscheduler, the jax.distributed.initialize() API will automatically detect all available\nJAX processes.

What Is In This Container?

This container image contains the complete\nsource of the NVIDIA version of JAX in /opt/jax. It\nis prebuilt and installed as a system Python module. Visit JAX's readthedocs page to learn more about JAX.

The NVIDIA JAX Container\nis optimized for use with NVIDIA GPUs, and contains the following software for\nGPU acceleration:

CUDA
cuBLAS
NVIDIA cuDNN
NVIDIA NCCL (optimized for NVLink)
NVIDIA DALI

The software stack in this container has been\nvalidated for compatibility, and does not require any\nadditional installation or compilation from the end user. This container can\nhelp accelerate your deep learning workflow from end to end.

ETL

NVIDIA Data Loading Library (DALI) is designed to accelerate data loading and\npreprocessing pipelines for deep learning applications by offloading them to\nthe GPU. DALI primarily focuses on building data preprocessing pipelines for image,\nvideo, and audio data. These pipelines are typically complex and include\nmultiple stages, leading to bottlenecks when run on CPU. Use this container to get started on accelerating data loading with DALI.

Training

NVIDIA CUDA Deep Neural Network Library (cuDNN) is a GPU-accelerated library of primitives for deep neural\nnetworks. cuDNN provides highly tuned implementations\nfor standard routines such as forward and backward convolution, pooling,\nnormalization, and activation layers. The version of JAX in this container is\nprecompiled with cuDNN support, and\ndoes not require any additional configuration.

NVIDIA Collective Communications Library (NCCL)\nimplements multi-GPU and multi-node communication primitives for NVIDIA GPUs\nand Networking that take into account system and\nnetwork topology. NCCL is integrated with JAX to accelerate training on\nmulti-GPU and multi-node systems. In particular, NCCL\nprovides the default all-reduce algorithm for the Mirrored and MultiWorkerMirrored distributed training strategies.

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor 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.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

License

By pulling and using the container, you accept the terms and conditions of this End User License Agreement and Product-Specific Terms.

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It includes Numpy-like APIs, automatic differentiation, XLA acceleration and simple primitives for scaling across GPUs and supports an ecosystem of libraries.","[JAX](https://jax.readthedocs.io/) is a framework for high-performance numerical computing and\nmachine learning research. It includes Numpy-like\nAPIs, automatic differentiation, [XLA](https://github.com/openxla/openxla-nvgpu) acceleration and \nsimple primitives for scaling across GPUs. \n\nThe JAX NGC Container comes with all dependencies included, providing an\neasy place to start developing applications in areas such as NLP, Computer Vision, \nMultimodality, physics-based simulations, reinforcement learning, drug discovery, and neural rendering.\n\nThe JAX NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that\nenable and optimize GPU performance. This container may also include\nmodifications to the JAX source code in order to\nmaximize performance and compatibility. This container also includes software\nfor accelerating ETL ([DALI](https://developer.nvidia.com/dali) and training ([cuDNN](https://developer.nvidia.com/cudnn), [NCCL](https://developer.nvidia.com/nccl)). \n\nFor building neural networks, the JAX NGC\nContainer includes [Flax](https://flax.readthedocs.io/en/latest/notebooks/flax_basics.html), a neural network library with support for\ncommon deep learning models, layers and optimizers. We\nalso include a container for [Paxml](https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/pax), a framework for training LLMs such as GPT, and a container for [T5x](https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/t5x), a framework for training T5 and other Flax-based models. You can use the JAX, Paxml, or T5x containers for your deep learning workloads or install your own favorite libraries on top of them. \n\n## Prerequisites\n\nUsing the JAX NGC\nContainer requires the host system to have the following installed:\n\n* [Docker Engine](https://docs.docker.com/get-docker/)\n* [NVIDIA GPU Drivers](https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html)\n* [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker)\n\nFor supported versions, see the [Framework Containers Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) and\nthe [NVIDIA Container Toolkit Documentation](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).\n\nNo other installation, compilation, or\ndependency management is required. It is not necessary to install the NVIDIA\nCUDA Toolkit.\n\n## Running JAX\n\nTo run a container, issue the appropriate\ncommand as explained in the [Running A Container](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#runcont) chapter in the NVIDIA Containers For Deep\nLearning Frameworks User's Guide and specify the registry, repository, and\ntags. For more information about using NGC, refer to the [NGC Container User Guide](https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html).\n\nThe following command assumes you want to run the JAX container interactively, where 23.08 is the container version. For example, 23.08 for August 2023 release:\n\n```\ndocker run --gpus all -it --rm nvcr.io/nvidia/jax:23.08-py3\n```\n\nTo pull data and model descriptions from locations outside the container for use by JAX or save results to locations outside the container, mount one or more host directories as [Docker data volumes](https://docs.docker.com/engine/admin/volumes/volumes/).\n\nSee [/workspace/README.md](https://github.com/google/jax/blob/main/README.md) for information on getting started and customizing your JAX image.\n\nIf you use multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with might not be enough. To increase the shared memory size, issue one of the following commands:\n\n```\n--shm-size=1g\n--ulimit memlock=-1\n```\n\nNote: 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. 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.\n\n## Running JAX in multi-node, multi-GPU\n\nOne of the key features of JAX is its easy\nscaling primitives for running JAX processes across multiple accelerators. The\nJAX distributed system allows JAX processes to discover each other and share\ntopology information, perform health checks, ensure that all processes shut\ndown if any process dies, and can be used for distributed checkpointing.\n\nFor information on how to set up a cluster and\nlaunch JAX processes, please refer to [JAX readthedocs](https://jax.readthedocs.io/en/latest/multi_process.html). For HPC cluster environments with a Slurm or OpenMPI\nscheduler, the [jax.distributed.initialize()](https://jax.readthedocs.io/en/latest/_autosummary/jax.distributed.initialize.html#jax.distributed.initialize) API will automatically detect all available\nJAX processes.\n\n## What Is In This Container?\n\nThis container image contains the complete\nsource of the NVIDIA version of JAX in /opt/jax. It\nis prebuilt and installed as a system Python module. Visit JAX's [readthedocs](https://jax.readthedocs.io/en/latest/) page to learn more about JAX.\n\nThe NVIDIA JAX Container\nis optimized for use with NVIDIA GPUs, and contains the following software for\nGPU acceleration:\n\n* [CUDA](https://developer.nvidia.com/cuda-toolkit)\n* [cuBLAS](https://developer.nvidia.com/cublas)\n* [NVIDIA cuDNN](https://developer.nvidia.com/cudnn)\n* [NVIDIA NCCL](https://developer.nvidia.com/nccl) (optimized for [NVLink](https://www.nvidia.com/object/nvlink.html))\n* [NVIDIA DALI](https://developer.nvidia.com/dali)\n\nThe software stack in this container has been\nvalidated for compatibility, and does not require any\nadditional installation or compilation from the end user. This container can\nhelp accelerate your deep learning workflow from end to end.\n\n### ETL\n\n[NVIDIA Data Loading Library (DALI)](https://developer.nvidia.com/dali) is designed to accelerate data loading and\npreprocessing pipelines for deep learning applications by offloading them to\nthe GPU. DALI primarily focuses on building data preprocessing pipelines for image,\nvideo, and audio data. These pipelines are typically complex and include\nmultiple stages, leading to bottlenecks when run on CPU. Use this container to [get started](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html) on accelerating data loading with DALI.\n\n### Training\n\n[NVIDIA CUDA Deep Neural Network Library (cuDNN)](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for deep neural\nnetworks. cuDNN provides highly tuned implementations\nfor standard routines such as forward and backward convolution, pooling,\nnormalization, and activation layers. The version of JAX in this container is\nprecompiled with cuDNN support, and\ndoes not require any additional configuration.\n\n[NVIDIA Collective Communications Library (NCCL)](https://developer.nvidia.com/nccl)\nimplements multi-GPU and multi-node communication primitives for NVIDIA GPUs\nand Networking that take into account system and\nnetwork topology. NCCL is integrated with JAX to accelerate training on\nmulti-GPU and multi-node systems. In particular, NCCL\nprovides the default all-reduce algorithm for the Mirrored and MultiWorkerMirrored distributed training strategies.\n\n## Suggested Reading\n\nFor the latest Release Notes, see the [JAX Release Notes](https://docs.nvidia.com/deeplearning/frameworks/jax-release-notes/index.html).\n\nFor a full list of the supported software and\nspecific versions that come packaged with this framework based on the container\nimage, see the [Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).\n\nFor more information about JAX, including tutorials, documentation, and examples, see:\n\n * [JAX website](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)\n * [JAX Toolbox](https://www.bing.com/search?pglt=673&q=jax+toolbox+github&cvid=9085b307e786471d972cd2da360afa9f&aqs=edge.0.69i59j69i57j0l3j69i60l3j69i64.1464j0j1&FORM=ANNTA1&PC=U531)\n\n### JAX on Public Clouds\n * AWS\n * [SageMaker GPT2 distributed training code sample](https://github.com/aws-samples/aws-samples-for-ray/tree/main/sagemaker/jax_alpa_language_model)\n * Azure\n * [Accelerating AI applications using the JAX framework on Azure’s NDm A100 v4 Virtual Machines](https://techcommunity.microsoft.com/t5/azure-high-performance-computing/accelerating-ai-applications-using-the-jax-framework-on-azure-s/ba-p/3735314)\n * [Running T5 training benchmarks using NVIDIA JAX container on Azure - YouTube](https://www.youtube.com/watch?v=oZIV07EwSw4)\n * GCP\n * [Getting started with JAX multi-node applications with NVIDIA GPUs on Google Kubernetes Engine](https://cloud.google.com/blog/products/containers-kubernetes/machine-learning-with-jax-on-kubernetes-with-nvidia-gpus)\n * OCI\n * [Running a deep learning workload with JAX on multinode multi-GPU clusters on OCI](https://blogs.oracle.com/cloud-infrastructure/post/running-multinode-jax-clusters-on-oci-gpu-cloud)\n\n## **Security Common Vulnerabilities and Exposures (CVEs)**\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor 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.\n\n## **Ethical Considerations**\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## License\nBy pulling and using the container, you accept the terms and conditions of this [End User License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).","JAX",["D",1782490329887],[321,319,317,315,307,313,303,311,305,309,299,301,297,295,293,291,180,288,286,284,282,280,278,276,274,272,270,268,266,261,264],"/api/image/nvidia/-/containers/jax",9114717189,"hasSignedTag",[],[349,350,351,352,353],[],[341],[],{"_92":342,"_343":342,"_344":345,"_346":347},"signed images","value","filter",{"_348":347,"_343":342},"category","system","field",{"_92":366,"_343":367,"_344":368,"_346":356},{"_92":362,"_343":363,"_344":364,"_346":356},{"_92":360,"_343":360,"_344":361,"_346":356},{"_92":330,"_343":357,"_344":358,"_346":356},{"_92":354,"_343":354,"_344":355,"_346":356},"NSPECT-Q9WX-5SWR",{"_348":356,"_343":354},"label","pltfm_jax",{"_348":359,"_343":357},"platform","DLFW",{"_348":356,"_343":360},"DL","soln_dl",{"_348":365,"_343":363},"technology","AI","soln_ai",{"_348":365,"_343":367},{"_147":1539,"_149":1540},"ngcApps",[1492],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1473":1474,"_1475":1476,"_1477":1478,"_1479":1480,"_1481":380,"_179":404,"_1482":1483,"_1484":51,"_1485":127,"_1486":127,"_1487":1488,"_1489":127,"_1490":1491},"amplitude",{"_1470":1471,"_1076":-7,"_1472":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1464":1465,"_1466":1467,"_1468":1469},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1351":105,"_1352":1353,"_1354":1355,"_1356":1357},"announcements",[],"consentState",{"_442":1341,"_1342":26,"_1343":26,"_1344":127,"_1345":1346},"consentConfig",{"_424":425,"_426":427,"_428":429,"_430":431,"_432":433,"_434":435,"_436":437,"_438":439,"_440":441,"_442":443},"showContactAdmin",1783869359643,[],[],[418,419,420,421,422,423],{"_40":62,"_42":67,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":64,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734],[679,680,681,682,683],{"_482":574,"_92":574,"_91":105,"_456":705},{"_482":540,"_92":540,"_91":105,"_456":700},{"_482":693,"_92":693,"_91":105,"_456":694},{"_482":689,"_92":689,"_91":105,"_456":690},{"_482":684,"_92":684,"_91":105,"_456":685},"nvcrm.my.site.com",[686],{"_262":687,"_488":684,"_489":26,"_91":688},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[691],{"_262":692,"_488":689,"_489":26,"_91":523},"edge","github.com",[695,696],{"_262":699,"_488":693,"_489":26,"_91":698},{"_262":697,"_488":693,"_489":26,"_91":698},"logged_in","This domain is owned by Github. 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