ContainersNVIDIADeepSeek-R1NVIDIALlama-3.1-Nemotron-70B-InstructNVIDIAPyTorch
CollectionsNVIDIAOmniverse Kit (FB)NVIDIADeepStream SDKNVIDIAOmniverse Kit App Streaming
ModelsNVIDIAStyleGAN3 pretrained modelsNVIDIAPeopleNetNVIDIATrafficCamNet
ResourcesNVIDIARiva Skills Quick Start
Helm ChartsNVIDIAGPU OperatorNVIDIANVIDIA NIM Operator
NVIDIA
NVIDIA
Merlin PyTorch
Container
NVIDIA
NVIDIA
Merlin PyTorch

The Merlin PyTorch container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch, and serve the trained model on Triton Inference Server.

LayerLabelCreated
63499356641cbef06d00fa196d3c77aefec3d1999c6dbe5908dcec8c06f8ceffRUN
|4
01/15/2024 3:29 AM UTC
5d1a53aef1a1a84759e88311287ddd10c24f80ce02584da6ef53761220e5a2c6RUN
HUGECTR_DEV_MODE=false HUGECTR_VER=v23.12.00 _CI_JOB_TOKEN= _HUGECTR_REPO=github.com/NVIDIA-Merlin/HugeCTR.git ln -s /opt/tritonserver/backends/pytorch/* /usr/local/lib/
01/15/2024 3:29 AM UTC
7c0cf780a37b4005fb2f42d3e85f51d7a429371c6365e7b9603535c0dc0e23b7RUN
HUGECTR_DEV_MODE=false HUGECTR_VER=v23.12.00 _CI_JOB_TOKEN= _HUGECTR_REPO=github.com/NVIDIA-Merlin/HugeCTR.git if [ "$HUGECTR_DEV_MODE" == "false" ]; then export HUGECTR_HOME=/usr/local/hugectr &&
  rm -rf ${HUGECTR_HOME}/lib/libgmock* ${HUGECTR_HOME}/lib/pkgconfig/gmock* ${HUGECTR_HOME}/include/gmock &&
  rm -rf ${HUGECTR_HOME}/lib/libgtest* ${HUGECTR_HOME}/lib/pkgconfig/gtest* ${HUGECTR_HOME}/include/gtest &&
  git clone --branch ${HUGECTR_VER} --depth 1 --recurse-submodules --shallow-submodules https://${_CI_JOB_TOKEN}${_HUGECTR_REPO} /hugectr &&
  pushd /hugectr/hps_torch/ &&
  pip --no-cache-dir install ninja &&
  TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 9.0" python setup.py install &&
  popd &&
  rm -rf /hugectr ; fi
01/15/2024 3:29 AM UTC
f3ef6944d8dcd5621bac76bb2282a42dece874add1385ad89ac1e6740805e7b0ARG
HUGECTR_VER=main
01/15/2024 3:27 AM UTC
d24207b959ca3f57f05c8327568d1cd72dec60f7a74ad7e24e01f1d6d5646dc4ARG
_CI_JOB_TOKEN=
01/15/2024 3:27 AM UTC
faa6cf65347445428545db032b87ab3b7f0f2bb3f99c508accb89b796ebd2c85ARG
_HUGECTR_REPO=github.com/NVIDIA-Merlin/HugeCTR.git
01/15/2024 3:27 AM UTC
fdd4b1ea6d00b5105eee40e4d40417b33f10c513b7dfa19e86f64d6936933929ARG
HUGECTR_DEV_MODE=false
01/15/2024 3:27 AM UTC
73568d5e995ef81bd952b94c4cace0efa3d6aac5ee2a85dba7396bde2fb40b25COPY
--chown=1000:1000dir:1a5ca6dfe8eada207b7ef8e793a3d89c8a45335e87a07bc147d9f535fd1a7426 in /usr/local/lib/python3.10/dist-packages/torch.egg-info/
01/15/2024 3:27 AM UTC
f476252cf1a4bd7a5ba777f77e4683d2c2229c948cb4e9d5c689e6f149165833COPY
--chown=1000:1000dir:aa26a6b9ed90d8a30bd0209a98ead8506ba8005252f79b13314c46afe5dac6aa in /usr/local/lib/python3.10/dist-packages/numpy.dist-info/
01/15/2024 3:27 AM UTC
b23bb22896113d805fc2440b898fe1d69490a54fae296e250ee5955780eb8551COPY
--chown=1000:1000dir:7b4ffac94789f98e1ca57dc46d652ac800ab9018d6f0d8fe9197c71468fe53a7 in /usr/local/lib/python3.10/dist-packages/numba.dist-info/
01/15/2024 3:27 AM UTC
...

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking "Accept All", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on "Manage Settings." By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.