TAO Converter [Deprecated]

This resource has been deprecated in favor of the nvidia-tao-deploy package in TAO. For further instruction on how to get this package, refer to the TAO documentation.

The models trained in TAO Toolkit are deployed to NVIDIA's Inference SDK's such as DeepStream, Riva etc\nvia TensorRT. NVIDIA TensorRT is an SDK for high-performance deep learning inference. TensorRT provides APIs and parsers\nto import trained models from all major deep learning frameworks. It then generates optimized runtime engines\ndeployable in the datacenter as well as in automotive and embedded environments. To understand TensorRT and it's\ncapabilities better, refer to the official TensorRT documentation. While the conversational AI models trained using TAO Toolkit can be consumed via TensorRT only via Riva,\nthe computer vision models trained by TAO Toolkit can be consumed by TensorRT, via the tao-converter tool. The\nTAO Converter parses the exported .etlt model file, and generates an optimized TensorRT engine. These engines\ncan be generated to support inference at low precision, such as FP16 or INT8.

While most of the TAO models support direct integration of the .etlt files to DeepStream 6.1, DeepStream can also\nconsume the optimized engine generated by the tao-converter.

The TensorRT engines generated by this tao-converter are specific to the GPU that it was generated on. So,\nbased on the platform that the model is being deployed to, you will need to download the specific version of\nthe tao-converter and generate the engine there.

The TAO models from TAO 3.0-22.05 have been verified to integrate with TensorRT version 7.0, 7.1, 7.2, 8.0, 8.2 and 8.4.

TensorRT Open Source Software

Even though TensorRT contains optimized implementations for several common operations used in Deep Neural Networks(DNNs),\nwith Deep Learning being such a quickly evolving discipline, TensorRT provides users a method to bring in new operations\nvia to the model graph via custom TensorRT Plugins. Several samples of these custom plug-ins are hosted on\nGitHub under the repository called TensorRT OSS.

Instructions to build and install TensorRT OSS can be found in this repository.

The TAO applications that require TensorRT OSS are:

FasterRCNN
SSD
DSSD
YOLOv3
YOLOv4
YOLOv4-tiny
RetinaNet
MaskRCNN
EfficientDet
PointPillars

Installing the TAO Converter

The TAO Converter is distributed as a separate binary for x86 and Jetson platforms. The following table lists the links where you can download the tao-converter.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

TensorRT	Platform
7.2	x86
7.1	x86
8.0	x86
8.2	x86
8.4	x86
8.0	aarch64
8.2	aarch64
8.4	aarch64

Before installing the tao-converter, install the TensorRT OSS library by following the instructions\nhere.

Downloading the converter

For an x86 platform with discrete GPUs, the default TAO package includes the tao-converter\nbuilt for TensorRT 8.2.5.1 with CUDA 11.4 and CUDNN 8.2. However, for any other version of TensorRT,\nyou may download using the command below:

--dest /path/to/download/directory\n\">

ngc registry resource download-version nvidia/tao/tao-converter:<latest_version> --dest /path/to/download/directory\n

Installing on an x86 platform

Once the tao-converter is downloaded, follow the instructions below to generate a TensorRT engine.

\n
Unzip the zip file on the target machine.
\n
\n
Install the OpenSSL package using the command:
\n
```
sudo apt-get install libssl-dev\n
```
\n

Export the following environment variables:

export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”\nexport TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”\n

\n
Run the tao-converter using the sample command below and generate the engine.
\n
\n
Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS\non x86 section above or in this\nGitHub repo`.
\n

\n
Make sure to follow the output node names as mentioned in Exporting the Model\nsection of the respective model in the TAO Toolkit documentation
\n

Installing on an jetson platform

Once the tao-converter is downloaded, follow the instructions below to generate a TensorRT engine.

\n
Unzip the zip file on the target machine.
\n
\n
Install the OpenSSL package using the command:
\n
```
sudo apt-get install libssl-dev\n
```
\n

Export the following environment variables:

export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”\nexport TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”\n

\n
Run the tao-converter using the sample command below and generate the engine.
\n
\n
Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS\non aarch64 section above or in this\nGitHub repo`.
\n

\n
Due to changes in the TensorRT API between versions 8.x.x and 7.2.x,\nthe deployable models generated using the export task in TAO Toolkit 3.0-21.11+\ncan only be deployed in DeepStream version 6.1. Inorder to deploy the models compatible\nwith DeepStream 5.1 from the table above with DeepStream 5.1, you will need to run the\ncorresponding tao <model> export task using the TAO Toolkit 3.0-21.08 package to re-generate\na deployable model and calibration cache file that is compatible with TensorRT 7.2.
\n
Similarly, if you have a model trained with TAO Toolkit 3.0-21.08 package and want to deploy\nto DeepStream 6.1, please regenerate the deployable model.etlt and int8 calibration\nfile using the corresponding tao <model> export task in TAO Toolkit 3.0-21.11+
\n
TAO Toolkit 3.0-22.05 was built with TensorRT 8.2.5.1.
\n\n
TAO Toolkit -> DeepStream version interoperability
\n
To install the 3.0-21.08 or 3.0-21.11 package, please instantiate a new virtual environment by following the\ninstructions in the :ref:Quick Start Guide <installing_tao_toolkit> and run the following commands
\n
\n
=0.1.20 # for 3.0-21.11\npip3 install nvidia-tao>=0.1.23 # for 3.0-22.05\n\">
pip3 install nvidia-pyindex\npip3 install nvidia-tao==0.1.19    # for 3.0-21.08\npip3 install nvidia-tao>=0.1.20    # for 3.0-21.11\npip3 install nvidia-tao>=0.1.23    # for 3.0-22.05\n
\n
\n

Running the TAO converter

\n -d \n -o \n [-c ]\n [-e ]\n [-b ]\n [-m ]\n [-t ]\n [-w ]\n [-i ]\n [-p ]\n [-s]\n [-u ]\n input_file\n\">

    tao-converter [-h] -k <encryption_key>\n                       -d <input_dimensions>\n                       -o <comma separated output nodes>\n                       [-c <path to calibration cache file>]\n                       [-e <path to output engine>]\n                       [-b <calibration batch size>]\n                       [-m <maximum batch size of the TRT engine>]\n                       [-t <engine datatype>]\n                       [-w <maximum workspace size of the TRT Engine>]\n                       [-i <input dimension ordering>]\n                       [-p <optimization_profiles>]\n                       [-s]\n                       [-u <DLA_core>]\n                       input_file\n

Required Arguments

input_file: Path to the .etlt model exported using tao <model> export.
-k: The key used to encode the .tlt model when doing the training.
-d: Comma-separated list of input dimensions that should match the dimensions used for\ntao <model> export.
-o: Comma-separated list of output blob names that should match the output configuration\nused for tao <model> export.

Optional Arguments

-e: Path to save the engine to. (default: ./saved.engine)
-t: Desired engine data type, generates calibration cache if in INT8 mode. The default\nvalue is fp32. The options are {fp32, fp16, int8}.
-w: Maximum workspace size for the TensorRT engine. The default value is 1073741824(1<<30).
-i: Input dimension ordering, all other TAO commands use NCHW. The default value is\nnchw. The options are {nchw, nhwc, nc}.
-p: Optimization profiles for .etlt models with dynamic shape. Comma separated\nlist of optimization profile shapes in the format <input_name>,<min_shape>,<opt_shape>,<max_shape>,\nwhere each shape has the format: <n>x<c>x<h>x<w>. Can be specified multiple times if there are\nmultiple input tensors for the model. This is only useful for new models introduced in TAO Toolkit 3.21.08.\nThis parameter is not required for models that are already existed in TAO Toolkit 2.0.
-s: TensorRT strict type constraints. A Boolean to apply TensorRT strict type constraints\nwhen building the TensorRT engine.
-u: Use DLA core. Specifying DLA core index when building the TensorRT engine on Jetson devices.

INT8 Mode Arguments

-c: Path to calibration cache file, only used in INT8 mode. The default value is\n./cal.bin.
-b: Batch size used during the export step for INT8 calibration cache generation.\n(default: 8).
-m: Maximum batch size for TensorRT engine.(default: 16). If meet with out-of-memory\nissue, decrease the batch size accordingly. This parameter is not required for .etlt\nmodels generated with dynamic shape. (This is only possible for new models introduced in TAO Toolkit 3.21.08.)

The usage for each TAO Computer Vision is explained in the respective models chapter.

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For further instruction on how to get this package, refer to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/text/tao_deploy/tao_deploy_overview.html).**\n\nThe models trained in TAO Toolkit are deployed to NVIDIA's Inference SDK's such as DeepStream, Riva etc\nvia TensorRT. NVIDIA TensorRT is an SDK for high-performance deep learning inference. TensorRT provides APIs and parsers\nto import trained models from all major deep learning frameworks. It then generates optimized runtime engines\ndeployable in the datacenter as well as in automotive and embedded environments. To understand TensorRT and it's\ncapabilities better, refer to the official [TensorRT documentation](https://developer.nvidia.com/tensorrt). While the conversational AI models trained using TAO Toolkit can be consumed via TensorRT only via Riva,\nthe computer vision models trained by TAO Toolkit can be consumed by TensorRT, via the `tao-converter` tool. The\n**TAO Converter** parses the exported `.etlt` model file, and generates an optimized TensorRT engine. These engines\ncan be generated to support inference at low precision, such as `FP16` or `INT8`.\n\nWhile most of the TAO models support direct integration of the .etlt files to DeepStream 6.1, DeepStream can also\nconsume the optimized engine generated by the `tao-converter`.\n\nThe TensorRT engines generated by this `tao-converter` are specific to the GPU that it was generated on. So,\nbased on the platform that the model is being deployed to, you will need to download the specific version of\nthe `tao-converter` and generate the engine there.\n\nThe TAO models from TAO 3.0-22.05 have been verified to integrate with TensorRT version 7.0, 7.1, 7.2, 8.0, 8.2 and 8.4.\n\n## TensorRT Open Source Software\n\nEven though TensorRT contains optimized implementations for several common operations used in Deep Neural Networks(DNNs),\nwith Deep Learning being such a quickly evolving discipline, TensorRT provides users a method to bring in new operations\nvia to the model graph via custom `TensorRT Plugins`. Several samples of these custom plug-ins are hosted on\nGitHub under the repository called [TensorRT OSS](https://github.com/NVIDIA/TensorRT).\n\nInstructions to build and install TensorRT OSS can be found in this [repository](https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/tree/master/TRT-OSS).\n\nThe TAO applications that require TensorRT OSS are:\n\n* FasterRCNN\n* SSD\n* DSSD\n* YOLOv3\n* YOLOv4\n* YOLOv4-tiny\n* RetinaNet\n* MaskRCNN\n* EfficientDet\n* PointPillars\n\n## Installing the TAO Converter \n\nThe TAO Converter is distributed as a separate binary for x86 and Jetson platforms. The following table lists the links where you can download the `tao-converter`.\n\n| **TensorRT** | **Platform** |\n| :-- | :-- |\n| 7.2 | x86 |\n| 7.1 | x86 |\n| 8.0 | x86 |\n| 8.2 | x86 |\n| 8.4 | x86 |\n| 8.0 | aarch64 |\n| 8.2 | aarch64 |\n| 8.4 | aarch64 |\n\nBefore installing the `tao-converter`, install the TensorRT OSS library by following the instructions\n[here](https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/tree/master/TRT-OSS).\n\n### Downloading the converter \n\nFor an x86 platform with discrete GPUs, the default TAO package includes the `tao-converter`\nbuilt for TensorRT 8.2.5.1 with CUDA 11.4 and CUDNN 8.2. However, for any other version of TensorRT,\nyou may download using the command below:\n\n```sh\nngc registry resource download-version nvidia/tao/tao-converter: --dest /path/to/download/directory\n```\n\n### Installing on an x86 platform \n\nOnce the `tao-converter` is downloaded, follow the instructions below to generate a TensorRT engine.\n\n1. Unzip the zip file on the target machine.\n2. Install the OpenSSL package using the command:\n\n ```sh\n sudo apt-get install libssl-dev\n ```\n\n3. Export the following environment variables:\n\n ```sh\n export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”\n export TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”\n ```\n\n4. Run the `tao-converter` using the sample command below and generate the engine.\n\n5. Instructions to build TensorRT OSS on Jetson can be found in the [TensorRT OSS\n on x86](#instructions-for-jetson-with-OSS) section above or in this\n [GitHub repo](https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps)`.\n\n> Make sure to follow the output node names as mentioned in `Exporting the Model`\n> section of the respective model in the [TAO Toolkit documentation](placeholder)\n\n### Installing on an jetson platform\n\nOnce the `tao-converter` is downloaded, follow the instructions below to generate a TensorRT engine.\n\n1. Unzip the zip file on the target machine.\n2. Install the OpenSSL package using the command:\n\n ```sh\n sudo apt-get install libssl-dev\n ```\n\n3. Export the following environment variables:\n\n ```sh\n export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”\n export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”\n ```\n\n4. Run the `tao-converter` using the sample command below and generate the engine.\n\n5. Instructions to build TensorRT OSS on Jetson can be found in the [TensorRT OSS\n on aarch64](#instructions-for-jetson-with-OSS) section above or in this\n [GitHub repo](https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps)`.\n\n> Due to changes in the TensorRT API between versions `8.x.x` and `7.2.x`,\n> the deployable models generated using the `export` task in TAO Toolkit 3.0-21.11+\n> can only be deployed in DeepStream version 6.1. Inorder to deploy the models compatible\n> with DeepStream 5.1 from the table above with DeepStream 5.1, you will need to run the \n> corresponding `tao export` task using the TAO Toolkit 3.0-21.08 package to re-generate\n> a deployable model and calibration cache file that is compatible with TensorRT 7.2.\n> \n> Similarly, if you have a model trained with TAO Toolkit 3.0-21.08 package and want to deploy\n> to DeepStream 6.1, please regenerate the deployable `model.etlt` and int8 calibration\n> file using the corresponding `tao export` task in TAO Toolkit 3.0-21.11+\n> \n> TAO Toolkit 3.0-22.05 was built with TensorRT 8.2.5.1.\n> \n> \n> TAO Toolkit -> DeepStream version interoperability\n> \n> To install the 3.0-21.08 or 3.0-21.11 package, please instantiate a new virtual environment by following the\n> instructions in the :ref:`Quick Start Guide ` and run the following commands\n> \n> \n>> ```sh\n>> pip3 install nvidia-pyindex\n>> pip3 install nvidia-tao==0.1.19 # for 3.0-21.08\n>> pip3 install nvidia-tao>=0.1.20 # for 3.0-21.11\n>> pip3 install nvidia-tao>=0.1.23 # for 3.0-22.05\n>> ```\n\n## Running the TAO converter\n\n```sh\n tao-converter [-h] -k \n -d \n -o \n [-c ]\n [-e ]\n [-b ]\n [-m ]\n [-t ]\n [-w ]\n [-i ]\n [-p ]\n [-s]\n [-u ]\n input_file\n```\n\n### Required Arguments\n\n* `input_file`: Path to the `.etlt` model exported using `tao export`.\n* `-k`: The key used to encode the `.tlt` model when doing the training.\n* `-d`: Comma-separated list of input dimensions that should match the dimensions used for\n `tao export`.\n* `-o`: Comma-separated list of output blob names that should match the output configuration\n used for `tao export`.\n\n### Optional Arguments\n\n* `-e`: Path to save the engine to. (default: `./saved.engine`)\n* `-t`: Desired engine data type, generates calibration cache if in INT8 mode. The default\n value is `fp32`. The options are {`fp32`, `fp16`, `int8`}.\n* `-w`: Maximum workspace size for the TensorRT engine. The default value is `1073741824(1<<30)`.\n* `-i`: Input dimension ordering, all other TAO commands use NCHW. The default value is\n `nchw`. The options are {`nchw`, `nhwc`, `nc`}.\n* `-p`: Optimization profiles for `.etlt` models with dynamic shape. Comma separated\n list of optimization profile shapes in the format `,,,`,\n where each shape has the format: `xxx`. Can be specified multiple times if there are\n multiple input tensors for the model. This is only useful for new models introduced in TAO Toolkit 3.21.08.\n This parameter is not required for models that are already existed in TAO Toolkit 2.0.\n* `-s`: TensorRT strict type constraints. A Boolean to apply TensorRT strict type constraints\n when building the TensorRT engine.\n* `-u`: Use DLA core. Specifying DLA core index when building the TensorRT engine on Jetson devices.\n\n### INT8 Mode Arguments\n\n* `-c`: Path to calibration cache file, only used in INT8 mode. The default value is\n `./cal.bin`.\n* `-b`: Batch size used during the export step for INT8 calibration cache generation.\n (default: `8`).\n* `-m`: Maximum batch size for TensorRT engine.(default: `16`). If meet with out-of-memory\n issue, decrease the batch size accordingly. This parameter is not required for `.etlt`\n models generated with dynamic shape. (This is only possible for new models introduced in TAO Toolkit 3.21.08.)\n\nThe usage for each TAO Computer Vision is explained in the respective models chapter.","displayName","TAO Converter",["D",1724451786317],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/tao-converter","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",453438,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_113":1462,"_115":1463},"ngcApps",[1415],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1397":1398,"_1399":1400,"_1401":1402,"_1403":1404,"_1405":290,"_141":314,"_1406":1407,"_1395":55,"_1408":92,"_1409":92,"_1410":1411,"_1412":92,"_1413":1414},"amplitude",{"_1393":1394,"_1395":55,"_987":-7,"_1396":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1387":1388,"_1389":1390,"_1391":1392},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1262":183,"_1263":1264,"_1265":1266,"_1267":137},"announcements",[],"consentState",{"_353":1252,"_1253":92,"_1254":31,"_1255":31,"_1256":1257},"consentConfig",{"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354},"showContactAdmin",1782605322995,[],[],[328,329,330,331,332,333],{"_45":59,"_47":70,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":68,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":66,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":60,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":56,"_47":57,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":334,"_54":55},5,"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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537],[376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392],{"_393":499,"_246":499,"_168":183,"_367":500},{"_393":494,"_246":494,"_168":183,"_367":495},{"_393":490,"_246":490,"_168":183,"_367":491},{"_393":485,"_246":485,"_168":183,"_367":486},{"_393":480,"_246":480,"_168":183,"_367":481},{"_393":468,"_246":468,"_168":183,"_367":469},{"_393":456,"_246":456,"_168":183,"_367":457},{"_393":451,"_246":451,"_168":183,"_367":452},{"_393":440,"_246":440,"_168":183,"_367":441},{"_393":435,"_246":435,"_168":183,"_367":436},{"_393":430,"_246":430,"_168":183,"_367":431},{"_393":425,"_246":425,"_168":183,"_367":426},{"_393":420,"_246":420,"_168":183,"_367":421},{"_393":415,"_246":415,"_168":183,"_367":416},{"_393":410,"_246":410,"_168":183,"_367":411},{"_393":403,"_246":403,"_168":183,"_367":404},{"_393":394,"_246":394,"_168":183,"_367":395},"hostName","amazon-adsystem.com",[396,397],{"_79":402,"_399":394,"_400":31,"_168":401},{"_79":398,"_399":394,"_400":31,"_168":401},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[405,406],{"_79":409,"_399":403,"_400":31,"_168":408},{"_79":407,"_399":403,"_400":31,"_168":408},"IDE","This domain is owned by Doubleclick (Google). 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