Clara Deploy SDK is being consolidated into Clara Holoscan SDK

More info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy

DeepStream Batch Pipeline

This asset requires the Clara Deploy SDK. Follow the instructions on the\nClara Ansible page\nto install the Clara Deploy SDK.

Overview

The DeepStream Batch pipeline is one of the reference pipelines provided with Clara Deploy SDK.

The pipeline is bundled with an organ detection model running on top of DeepStream SDK which provides a reference\napplication. It accepts a .mp4 file in H.264 format and performs the object detection finding stomach and intestines from the input video.\nThe output of the pipeline is a rendered video (bounding boxes with labels are overlayed on top of the original video) in H.264 format (output.mp4) as well as the primary detector output in a modified KITTI metadata format (.txt files).

Pipeline Definition

The DeepStream Batch pipeline is defined in the Clara Deploy pipeline definition language.\nThis pipeline consists of the following operator:

DeepStream App Operator to read/filter/write volume image using FastIO and

The following is the details of pipeline definition, with comments describing each operator's functions\nas well as input and output.

api-version: 0.4.0\nname: deepstream-batch-pipeline\nparameters:\n  DS_CONFIG: configs/config.txt\n  DS_INPUT:  # if empty, any .mp4 file in /input folder would be used.\n  DS_OUTPUT: output.mp4\n\noperators:\n- name: deepstream\n  description: DeepStream Operator\n  container:\n    image: clara/app-deepstream\n    tag: latest\n    command: [\"/workspace/launch_deepstream.sh\"]\n  variables:\n    DS_CONFIG: ${{DS_CONFIG}}\n    DS_INPUT: ${{DS_INPUT}}\n    DS_OUTPUT: ${{DS_OUTPUT}}\n  requests:\n    gpu: 1\n    memory: 8192\n  input:\n  - path: /input/\n  output:\n  - path: /output/\n

Data Input

Input requires a folder containing the following folders/files:

: folder for configuration (name depends on `${DS_CONFIG}`)\n│ └── config.txt # : a configuration file (name depends on `${DS_CONFIG}`)\n├── models # : folder for models/labels (used by the configuration file)\n│ ├── calibration.bin # calibration data needed for the model\n│ ├── labels.txt # label data\n│ ├── model.etlt # device-independent model file\n│ └── model.engine # device-specific model file\n└── test_input.mp4 # : input video file (.mp4 file in H.264 format)\n\">

.\n├── configs               # <required>: folder for configuration (name depends on `${DS_CONFIG}`)\n│   └── config.txt        # <required>: a configuration file (name depends on `${DS_CONFIG}`)\n├── models                # <required>: folder for models/labels (used by the configuration file)\n│   ├── calibration.bin   # calibration data needed for the model\n│   ├── labels.txt        # label data\n│   ├── model.etlt        # device-independent model file\n│   └── model.engine      # device-specific model file\n└── test_input.mp4        # <required>: input video file (.mp4 file in H.264 format)\n

The bundled model (app_ds_torso-model_v1.zip) includes configs and models folder so only\ninput video file (.mp4) in H.264 format is needed in the input folder.

Note that the bundled input (app_ds_torso-input_v1.zip) includes a sample input video file\nand you can use it for the test.

Data Output

With the bundled model (app_ds_torso), its output is the rendered video (bounding boxes with labels are overlayed on top of the original video) in H.264 format (output.mp4) as well as the primary detector output in a modified KITTI metadata format (.txt files).

Performance Implications

The DeepStream App Operator is a wrapper of DeepStream SDK's\nreference application and the container image used by the operator doesn't include models/configurations for the application.\nInstead, model and configuration files are also uploaded with the input video file as input payload\nwhenever triggering a job and it can cause performance degradation.

In addition to that, bundled TRT model (resnet18_detector.etlt_b4_fp16.engine) is a device-specific model and optimized from the device-independent model (resnet18_detector.etlt) on GV100 Volta GPU (32GB). If loading the device-specific model file is failed, the application would create the optimized-device-specific model from the device-independent model on runtime which can cause some delays on startup time.

To mitigate the performance degradation, please build a custom docker image from the source code by\ncopying models/configs folder into the container and updating paths/image/tag.

> Dockerfile\n# Convert '/input/configs/' to '/configs/'\nsed -i -e 's#/input/configs/#/configs/#' input/app_ds_torso/configs/config.txt\n# Convert '/input/models/' to '/models/'\nsed -i -e 's#/input/models/#/models/#' input/app_ds_torso/configs/dslhs_nvinfer_config.txt\n# Convert 'configs/config.txt' to '/configs/config.txt'\nsed -i -e 's#configs/config.txt#/configs/config.txt#' deepstream-batch-pipeline.yaml\n# Update the image used in the pipeline definition to 'clara/app_deepstream:latest'\nsed -i -e 's#image: .*#image: clara/app_deepstream#' deepstream-batch-pipeline.yaml\nsed -i -e 's#tag: .*#tag: latest#' deepstream-batch-pipeline.yaml\n# Build local image: clara/app_deepstream:latest\n./build.sh\n\n# Now you can create/trigger pipeline using 'deepstream-batch-pipeline.yaml' ...\n\n\">

clara pull pipeline clara_deepstream_batch_pipeline\ncd clara_deepstream_batch_pipeline\n# Unzip source code\nunzip source.zip\n# Unzip app_ds_torso-model_v1.zip and app_ds_torso-input_v1.zip into `input/app_ds_torso` folder\n./download_input.sh\n# Update Dockerfile to add configs/models folder to the container\necho \"COPY ./input/app_ds_torso/configs /configs\nCOPY ./input/app_ds_torso/models /models\" >> Dockerfile\n# Convert '/input/configs/' to '/configs/'\nsed -i -e 's#/input/configs/#/configs/#' input/app_ds_torso/configs/config.txt\n# Convert '/input/models/' to '/models/'\nsed -i -e 's#/input/models/#/models/#' input/app_ds_torso/configs/dslhs_nvinfer_config.txt\n# Convert 'configs/config.txt' to '/configs/config.txt'\nsed -i -e 's#configs/config.txt#/configs/config.txt#' deepstream-batch-pipeline.yaml\n# Update the image used in the pipeline definition to 'clara/app_deepstream:latest'\nsed -i -e 's#image: .*#image: clara/app_deepstream#' deepstream-batch-pipeline.yaml\nsed -i -e 's#tag: .*#tag: latest#' deepstream-batch-pipeline.yaml\n# Build local image: clara/app_deepstream:latest\n./build.sh\n\n# Now you can create/trigger pipeline using 'deepstream-batch-pipeline.yaml' ...\n\n

License

An End User License Agreement\nis included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the\nterms and conditions of these licenses.

Suggested Reading

Release Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum: (https://developer.nvidia.com/clara).

For answers to any questions you may have about this release, visit the NVIDIA Devtalk forum:\n(https://devtalk.nvidia.com/default/board/362/clara-sdk/).

","resourceVersions",[218,219,220,221,222,223,224,225,226],"resourceVersionsTotal",8,"hasMoreVersions","versionMetadata",{"_187":22,"_188":189,"_49":190,"_191":-5,"_192":193,"_194":-5,"_195":196,"_197":16,"_198":31,"_199":-5,"_200":-5,"_201":31,"_202":203,"_204":104,"_205":206,"_207":104,"_208":209,"_210":104,"_211":212,"_213":31,"_214":212,"_215":31,"_216":217},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/clara/resources/clara_deepstream_batch_pipeline/-","version","imageReference","registry","targetPath","cliDownloadCommand","ngc registry resource download-version nvidia/clara/clara_deepstream_batch_pipeline:0.6.0-2006.4","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/clara/resources/clara_deepstream_batch_pipeline/0.6.0-2006.4/version-history","accessCheckResult",{"_186":104},"hasRelatedCollections","showNimMetadataTab","context",{"_182":183,"_184":185},1793000380404,[],[],[176,177,178,179,180,181],{"_45":59,"_47":70,"_49":61,"_51":62,"_53":71,"_55":56},{"_45":59,"_47":68,"_49":61,"_51":62,"_53":69,"_55":56},{"_45":59,"_47":66,"_49":61,"_51":62,"_53":67,"_55":56},{"_45":59,"_47":60,"_49":61,"_51":62,"_53":63,"_55":56},{"_45":57,"_47":58,"_49":120,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","clara","allowed","versionId","description","# DeepStream Batch Pipeline\n\nThis asset requires the Clara Deploy SDK. Follow the instructions on the\n[Clara Bootstrap](https://ngc.nvidia.com/catalog/model-scripts/nvidia:clara:clara_bootstrap) page\nto install the Clara Deploy SDK.\n\n## Overview\n\nThe DeepStream Batch pipeline is one of the reference pipelines provided with Clara Deploy SDK.\n\nThe pipeline is bundled with an organ detection model running on top of [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) which provides a [reference\napplication](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream%2520Development%2520Guide%2Fdeepstream_app_architecture.html%23). It accepts a .mp4 file in H.264 format and performs the object detection finding `stomach` and `intestines` from the input video.\nThe output of the pipeline is a rendered video (bounding boxes with labels are overlayed on top of the original video) in H.264 format (output.mp4) as well as the primary detector output in a modified KITTI metadata format (.txt files).\n\n## Pipeline Definition\n\nThe DeepStream Batch pipeline is defined in the Clara Deploy pipeline definition language.\nThis pipeline consists of the following operator:\n- [DeepStream App Operator](/sdk/Applications/Operators/deepstream/app_deepstream/public/docs/README.md) to read/filter/write volume image using [FastIO](/sdk/CPDriver/public/docs/Overview.md) and\n\nThe following is the details of pipeline definition, with comments describing each operator's functions\nas well as input and output.\n\n```yaml\napi-version: 0.3.0\nname: deepstream-batch-pipeline\nparameters:\n DS_CONFIG: configs/config.txt\n DS_INPUT: # if empty, any .mp4 file in /input folder would be used.\n DS_OUTPUT: output.mp4\n\noperators:\n- name: deepstream\n description: DeepStream Operator\n container:\n image: clara/app_deepstream\n tag: 0.6.0-2006.4\n command: [\"/workspace/launch_deepstream.sh\"]\n variables:\n DS_CONFIG: ${{DS_CONFIG}}\n DS_INPUT: ${{DS_INPUT}}\n DS_OUTPUT: ${{DS_OUTPUT}}\n input:\n - path: /input/\n output:\n - path: /output/\n```\n\n## Data Input\n\nInput requires a folder containing the following folders/files:\n\n```bash\n.\n├── configs # : folder for configuration (name depends on `${DS_CONFIG}`)\n│ └── config.txt # : a configuration file (name depends on `${DS_CONFIG}`)\n├── models # : folder for models/labels (used by the configuration file)\n│ ├── calibration.bin # calibration data needed for the model\n│ ├── labels.txt # label data\n│ ├── model.etlt # device-independent model file\n│ └── model.engine # device-specific model file\n└── test_input.mp4 # : input video file (.mp4 file in H.264 format)\n```\n\nThe bundled model (`app_ds_torso-model_v1.zip`) includes `configs` and `models` folder so only\ninput video file (.mp4) in H.264 format is needed in the input folder.\n\nNote that the bundled input (`app_ds_torso-input_v1.zip`) includes a sample input video file\nand you can use it for the test.\n\n## Data Output\n\nWith the bundled model (`app_ds_torso`), its output is the rendered video (bounding boxes with labels are overlayed on top of the original video) in H.264 format (output.mp4) as well as the primary detector output in a modified KITTI metadata format (.txt files).\n\n## Performance Implications\n\nThe [DeepStream App Operator](/sdk/Applications/Operators/deepstream/app_deepstream/public/docs/README.md) is a wrapper of DeepStream SDK's\n[reference application](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream%2520Development%2520Guide%2Fdeepstream_app_architecture.html%23) and the container image used by the operator doesn't include models/configurations for the application.\nInstead, model and configuration files are also uploaded with the input video file as input payload\nwhenever triggering a job and it can cause performance degradation.\n\nIn addition to that, bundled TRT model (resnet18_detector.etlt_b4_fp16.engine) is a device-specific model and optimized from the device-independent model (resnet18_detector.etlt) on [GV100 Volta GPU (32GB)](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/productspage/quadro/quadro-desktop/quadro-volta-gv100-data-sheet-us-nvidia-704619-r3-web.pdf). If loading the device-specific model file is failed, the application would create the optimized-device-specific model from the device-independent model on runtime which can cause some delays on startup time.\n\nTo mitigate the performance degradation, please build a custom docker image from the source code by\ncopying models/configs folder into the container and updating paths/image/tag.\n\n```\nclara pull pipeline clara_deepstream_batch_pipeline\ncd clara_deepstream_batch_pipeline\n# Unzip source code\nunzip source.zip\n# Unzip app_ds_torso-model_v1.zip and app_ds_torso-input_v1.zip into `input/app_ds_torso` folder\n./download_input.sh\n# Update Dockerfile to add configs/models folder to the container\necho \"COPY ./input/app_ds_torso/configs /configs\nCOPY ./input/app_ds_torso/models /models\" >> Dockerfile\n# Convert '/input/configs/' to '/configs/'\nsed -i -e 's#/input/configs/#/configs/#' input/app_ds_torso/configs/config.txt\n# Convert '/input/models/' to '/models/'\nsed -i -e 's#/input/models/#/models/#' input/app_ds_torso/configs/dslhs_nvinfer_config.txt\n# Convert 'configs/config.txt' to '/configs/config.txt'\nsed -i -e 's#configs/config.txt#/configs/config.txt#' deepstream-batch-pipeline.yaml\n# Update the image used in the pipeline definition to 'clara/app_deepstream:latest'\nsed -i -e 's#image: .*#image: clara/app_deepstream#' deepstream-batch-pipeline.yaml\nsed -i -e 's#tag: .*#tag: latest#' deepstream-batch-pipeline.yaml\n# Build local image: clara/app_deepstream:latest\n./build.sh\n\n# Now you can create/trigger pipeline using 'deepstream-batch-pipeline.yaml' ...\n\n```\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license)\nis included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the\nterms and conditions of these licenses.\n\n## Suggested Reading\n\nRelease Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum: (https://developer.nvidia.com/clara).\n\nFor answers to any questions you may have about this release, visit the NVIDIA Devtalk forum:\n(https://devtalk.nvidia.com/default/board/362/clara-sdk/).","UPLOAD_COMPLETE","releaseType","createdDate",["D",1592939263052],"updatedDate","totalSizeInBytes",54543766,"totalFileCount","isSigned","digest","malwareScanStatus","isScanned","releaseNotes","# 0.6.0-2006.4\n\nInitial release.","hasReleaseNotes","setup","1. CD into a directory where pipelines will be stored, e.g.\n```bash\nmkdir -p ~/.clara/pipelines\ncd ~/.clara/pipelines\n```\n2. Use clara pull pipeline to download a pipeline definition:\n```bash\nclara pull pipeline clara_deepstream_batch_pipeline\n```\n3. Unzip test data\n```bash\ncd clara_deepstream_batch_pipeline\nunzip source.zip\n./download_input.sh\n```\n4. Install pipeline:\n```bash\nclara create pipelines -p deepstream-batch-pipeline.yaml\n```\n5. Verify that the pipeline is correctly stored\n```bash\nclara list pipelines\n```\n6. Verify that the stored pipeline is correct by examining the output of the following command:\n```bash\nclara describe pipeline \n```","hasSetup","quickStartGuide","# How to Run a Reference Pipeline using Local Input Files\n\n## Download & Install the Pipeline from NGC\n\nPlease refer to `Setup` section to install this Pipeline.\n\nNote down the pipeline ID during the pipeline installation as it would be used in a later section to trigger a job with input files.\n\n## Trigger Pipeline\n\nA pipeline can be triggered via Clara CLI.\n\nFor this created pipeline, sample input files are available within the SDK in the zipped test data file.\n\nOnce you execute `./download_input.sh` fromo the pipeline folder,\nyou will see the test data at `input/app_ds_torso` folder.\n\n1. Open a terminal and go to the directory which the pipeline is pulled.\n\n```bash\ncd /path-to-the-pipeline/\n```\n\n2. Trigger the pipeline (upload input files to Clara API Server) with the pipeline ID in the previous section.\n\n```bash\nclara create jobs -n deepstream-batch-test -p -f input/app_ds_torso/\n```\n\nWhen the above step is completed, you should be able to see the following result in the terminal (UUID of Job and Payload may be different).\n\n```bash\nJOB ID:f3170b457f5c41879951e6da8f37341e\nPAYLOAD ID:12bb398cb54844d5958d51e66e10713a\n```\n\nNote down this job ID and payload ID as they would be used in a later section to access output files.\n\nThen, execute the job:\n```bash\nclara start job -j \n```\n\n## Download and Verify the Outputs\n\nOnce the job has completed successfully, we can download output payload by using [clara download](https://docs.nvidia.com/clara/deploy/sdk/cli/public/docs/overview.html#commands) command.\n\nOutput payload files are located under `/operators/deepstream/` folder.\nWe can use the following command to download output files:\n\n```bash\nclara download :/operators/deepstream/* result # download files into result folder\n```\n\nThen, you can see the output video file using the following command:\n\n```bash\ngoogle-chrome result/output.mp4 # View the video output using Chrome which has a built-in viewer for H.264 file.\n```\n\nIf you are working with the server machine, the payload folder is directly accessible at the default payload folder `/clara/payloads//`.","hasQuickStartGuide","advanced","","hasAdvanced","performance","hasPerformance","policies",[],{"_79":242,"_81":243,"_191":-5,"_198":31,"_201":31,"_216":241,"_204":104},{"_79":240,"_81":240,"_191":-5,"_198":31,"_201":31,"_216":241,"_204":104},{"_79":238,"_81":238,"_191":-5,"_198":31,"_201":31,"_216":239,"_204":104},{"_79":236,"_81":236,"_191":-5,"_198":31,"_201":31,"_216":237,"_204":104},{"_79":234,"_81":234,"_191":-5,"_198":31,"_201":31,"_216":235,"_204":104},{"_79":232,"_81":232,"_191":-5,"_198":31,"_201":31,"_216":233,"_204":104},{"_79":22,"_81":22,"_191":-5,"_198":31,"_201":31,"_216":231,"_204":104},{"_79":229,"_81":229,"_191":-5,"_198":31,"_201":31,"_216":230,"_204":104},{"_79":227,"_81":227,"_191":-5,"_198":31,"_201":31,"_216":228,"_204":104},"0.5.0-2003.12",[],"0.5.0-2004.7",[],[],"0.7.1-2008.1",[],"0.7.2-2010.1",[],"0.7.3-2011.5",[],"0.7.4-2103.2",[],"0.8.1-2108.1",[],"-","Latest (0.8.1-2108.1)","nvidia/clara/clara_deepstream_batch_pipeline","key","nvidia/clara/resources/clara_deepstream_batch_pipeline","resourceType","resources","resourceId","clara_deepstream_batch_pipeline","shortDescription","Clara Deploy DeepStream Batch Pipeline","# Clara Deploy SDK is being consolidated into Clara Holoscan SDK\n\t\nMore info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy\n\t\n\n# DeepStream Batch Pipeline\n\nThis asset requires the Clara Deploy SDK. Follow the instructions on the\n[Clara Ansible](https://ngc.nvidia.com/catalog/model-scripts/nvidia:clara:clara_ansible) page\nto install the Clara Deploy SDK.\n\n## Overview\n\nThe DeepStream Batch pipeline is one of the reference pipelines provided with Clara Deploy SDK.\n\nThe pipeline is bundled with an organ detection model running on top of [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) which provides a [reference\napplication](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream_Development_Guide/deepstream_ref_app_deepstream_app.html). It accepts a .mp4 file in H.264 format and performs the object detection finding `stomach` and `intestines` from the input video.\nThe output of the pipeline is a rendered video (bounding boxes with labels are overlayed on top of the original video) in H.264 format (output.mp4) as well as the primary detector output in a modified KITTI metadata format (.txt files).\n\n## Pipeline Definition\n\nThe DeepStream Batch pipeline is defined in the Clara Deploy pipeline definition language.\nThis pipeline consists of the following operator:\n- [DeepStream App Operator](https://docs.nvidia.com/clara/deploy/sdk/Applications/Operators/deepstream/app_deepstream/public/docs/README.html) to read/filter/write volume image using [FastIO](https://docs.nvidia.com/clara/deploy/sdk/CPDriver/public/docs/Overview.html) and\n\nThe following is the details of pipeline definition, with comments describing each operator's functions\nas well as input and output.\n\n```yaml\napi-version: 0.4.0\nname: deepstream-batch-pipeline\nparameters:\n DS_CONFIG: configs/config.txt\n DS_INPUT: # if empty, any .mp4 file in /input folder would be used.\n DS_OUTPUT: output.mp4\n\noperators:\n- name: deepstream\n description: DeepStream Operator\n container:\n image: clara/app-deepstream\n tag: latest\n command: [\"/workspace/launch_deepstream.sh\"]\n variables:\n DS_CONFIG: ${{DS_CONFIG}}\n DS_INPUT: ${{DS_INPUT}}\n DS_OUTPUT: ${{DS_OUTPUT}}\n requests:\n gpu: 1\n memory: 8192\n input:\n - path: /input/\n output:\n - path: /output/\n```\n\n## Data Input\n\nInput requires a folder containing the following folders/files:\n\n```bash\n.\n├── configs # : folder for configuration (name depends on `${DS_CONFIG}`)\n│ └── config.txt # : a configuration file (name depends on `${DS_CONFIG}`)\n├── models # : folder for models/labels (used by the configuration file)\n│ ├── calibration.bin # calibration data needed for the model\n│ ├── labels.txt # label data\n│ ├── model.etlt # device-independent model file\n│ └── model.engine # device-specific model file\n└── test_input.mp4 # : input video file (.mp4 file in H.264 format)\n```\n\nThe bundled model (`app_ds_torso-model_v1.zip`) includes `configs` and `models` folder so only\ninput video file (.mp4) in H.264 format is needed in the input folder.\n\nNote that the bundled input (`app_ds_torso-input_v1.zip`) includes a sample input video file\nand you can use it for the test.\n\n## Data Output\n\nWith the bundled model (`app_ds_torso`), its output is the rendered video (bounding boxes with labels are overlayed on top of the original video) in H.264 format (output.mp4) as well as the primary detector output in a modified KITTI metadata format (.txt files).\n\n## Performance Implications\n\nThe [DeepStream App Operator](/sdk/Applications/Operators/deepstream/app_deepstream/public/docs/README.md) is a wrapper of DeepStream SDK's\n[reference application](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream_Development_Guide/deepstream_ref_app_deepstream_app.html) and the container image used by the operator doesn't include models/configurations for the application.\nInstead, model and configuration files are also uploaded with the input video file as input payload\nwhenever triggering a job and it can cause performance degradation.\n\nIn addition to that, bundled TRT model (resnet18_detector.etlt_b4_fp16.engine) is a device-specific model and optimized from the device-independent model (resnet18_detector.etlt) on [GV100 Volta GPU (32GB)](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/productspage/quadro/quadro-desktop/quadro-volta-gv100-data-sheet-us-nvidia-704619-r3-web.pdf). If loading the device-specific model file is failed, the application would create the optimized-device-specific model from the device-independent model on runtime which can cause some delays on startup time.\n\nTo mitigate the performance degradation, please build a custom docker image from the source code by\ncopying models/configs folder into the container and updating paths/image/tag.\n\n```\nclara pull pipeline clara_deepstream_batch_pipeline\ncd clara_deepstream_batch_pipeline\n# Unzip source code\nunzip source.zip\n# Unzip app_ds_torso-model_v1.zip and app_ds_torso-input_v1.zip into `input/app_ds_torso` folder\n./download_input.sh\n# Update Dockerfile to add configs/models folder to the container\necho \"COPY ./input/app_ds_torso/configs /configs\nCOPY ./input/app_ds_torso/models /models\" >> Dockerfile\n# Convert '/input/configs/' to '/configs/'\nsed -i -e 's#/input/configs/#/configs/#' input/app_ds_torso/configs/config.txt\n# Convert '/input/models/' to '/models/'\nsed -i -e 's#/input/models/#/models/#' input/app_ds_torso/configs/dslhs_nvinfer_config.txt\n# Convert 'configs/config.txt' to '/configs/config.txt'\nsed -i -e 's#configs/config.txt#/configs/config.txt#' deepstream-batch-pipeline.yaml\n# Update the image used in the pipeline definition to 'clara/app_deepstream:latest'\nsed -i -e 's#image: .*#image: clara/app_deepstream#' deepstream-batch-pipeline.yaml\nsed -i -e 's#tag: .*#tag: latest#' deepstream-batch-pipeline.yaml\n# Build local image: clara/app_deepstream:latest\n./build.sh\n\n# Now you can create/trigger pipeline using 'deepstream-batch-pipeline.yaml' ...\n\n```\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license)\nis included with the product. 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Name	Size	Updated	Actions
app_ds_torso-input_v1.zip	15.63 MB	June 23, 2020 UTC	curl wget Download
app_ds_torso-model_v1.zip	36.38 MB	June 23, 2020 UTC	curl wget Download
deepstream-batch-pipeline.yaml	572 B	June 23, 2020 UTC	curl wget Download
source.zip	8.92 KB	June 23, 2020 UTC	curl wget Download