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Support matrix updates for 22.06 #435

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Jul 7, 2022
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Support matrix updates for 22.06 #435

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21c5de6
Updates from containers
nvidia-merlin-bot Jul 6, 2022
9b985b5
Updates to containers for 22.06
mikemckiernan Jul 6, 2022
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205 changes: 104 additions & 101 deletions docs/data.json
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Original file line number Diff line number Diff line change
@@ -1,119 +1,42 @@
{
"nvcr.io/nvidia/merlin/merlin-tensorflow": {
"22.05": {
"base_container": "Triton version 22.03",
"compressedSize": "5.01 GB",
"cublas": "11.8.1.74",
"cuda": "11.6.1.005",
"cudf": "22.2.0",
"cudnn": "8.3.3.40+cuda11.5",
"cufft": "10.7.1.112",
"curand": "10.2.9.55",
"cusolver": "11.3.3.112",
"cusparse": "11.7.2.112",
"cutensor": "1.5.0.1",
"dgx_system": "* DGX-1\n* DGX-2\n* DGX A100\n* DGX Station",
"gpu_model": "* `NVIDIA Ampere GPU Architecture <https://www.nvidia.com/en-us/geforce/turing>`_\n* `Turing <https://www.nvidia.com/en-us/geforce/turing/>`_\n* `Volta <https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/>`_\n* `Pascal <https://www.nvidia.com/en-us/data-center/pascal-gpu-architecture/>`_",
"hugectr": "Not applicable",
"hugectr2onnx": "Not applicable",
"merlin.core": "0.3.0",
"merlin.models": "0.4.0",
"merlin.systems": "0.2.0",
"nvidia_driver": "NVIDIA Driver version 465.19.01\nor later is required. However,\nif you're running on Data Center\nGPUs (formerly Tesla) such as T4,\nyou can use any of the following\nNVIDIA Driver versions:\n\n* 418.40 (or later R418)\n* 440.33 (or later R440)\n* 450.51 (or later R450)\n* 460.27 (or later R460)\n\n**Note**: The CUDA Driver\nCompatibility Package does not\nsupport all drivers.",
"nvidia_pytorch": "Not applicable",
"nvidia_tensorflow": "Not applicable",
"nvtabular": "1.1.1",
"openmpi": "4.1.2rc4",
"os": "Ubuntu 20.04.4 LTS",
"python_major": "3",
"pytorch": "Not applicable",
"release": "22.05",
"rmm": "21.12.0",
"size": "11.05 GB",
"sm": "Not applicable",
"sparse_operation_kit": "Not applicable",
"tensorrt": "8.2.3.0+cuda11.4.2.006",
"tf": "Not applicable",
"transformers4rec": "0.1.8",
"triton": "2.20.0"
}
},
"nvcr.io/nvidia/merlin/merlin-pytorch": {
"22.05": {
"base_container": "Triton version 22.04",
"compressedSize": "6.63 GB",
"cublas": "11.9.3.115",
"cuda": "11.6.2.010",
"cudf": "22.2.0",
"cudnn": "8.4.0.27",
"cufft": "10.7.2.124",
"curand": "10.2.9.124",
"cusolver": "11.3.4.124",
"cusparse": "11.7.2.124",
"cutensor": "1.5.0.3",
"dgx_system": "* DGX-1\n* DGX-2\n* DGX A100\n* DGX Station",
"gpu_model": "* `NVIDIA Ampere GPU Architecture <https://www.nvidia.com/en-us/geforce/turing>`_\n* `Turing <https://www.nvidia.com/en-us/geforce/turing/>`_\n* `Volta <https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/>`_\n* `Pascal <https://www.nvidia.com/en-us/data-center/pascal-gpu-architecture/>`_",
"hugectr": "Not applicable",
"hugectr2onnx": "Not applicable",
"merlin.core": "0.3.0",
"merlin.models": "0.4.0",
"merlin.systems": "0.2.0",
"nvidia_driver": "NVIDIA Driver version 465.19.01\nor later is required. However,\nif you're running on Data Center\nGPUs (formerly Tesla) such as T4,\nyou can use any of the following\nNVIDIA Driver versions:\n\n* 418.40 (or later R418)\n* 440.33 (or later R440)\n* 450.51 (or later R450)\n* 460.27 (or later R460)\n\n**Note**: The CUDA Driver\nCompatibility Package does not\nsupport all drivers.",
"nvidia_pytorch": "Not applicable",
"nvidia_tensorflow": "Not applicable",
"nvtabular": "1.1.1",
"openmpi": "4.1.2rc4",
"os": "Ubuntu 20.04.4 LTS",
"python_major": "3",
"pytorch": "1.11.0+cu113",
"release": "22.05",
"rmm": "21.12.0",
"size": "14.37 GB",
"sm": "Not applicable",
"sparse_operation_kit": "Not applicable",
"tensorrt": "8.2.4.2+cuda11.4.2.006",
"tf": "Not applicable",
"transformers4rec": "0.1.8",
"triton": "2.21.0"
}
},
"nvcr.io/nvidia/merlin/merlin-hugectr": {
"22.05": {
"base_container": "Triton version 22.03",
"compressedSize": "5.54 GB",
"cublas": "11.8.1.74",
"cuda": "11.6.1.005",
"cudf": "22.2.0",
"cudnn": "8.3.3.40+cuda11.5",
"cufft": "10.7.1.112",
"curand": "10.2.9.55",
"cusolver": "11.3.3.112",
"cusparse": "11.7.2.112",
"cutensor": "1.5.0.1",
"22.06": {
"base_container": "Triton version 22.05",
"compressedSize": "6.57 GB",
"cublas": "11.10.1.25",
"cuda": "11.7.0.022",
"cudf": "22.4.0",
"cudnn": "8.4.0.27+cuda11.6",
"cufft": "10.7.2.50",
"curand": "10.2.10.50",
"cusolver": "11.3.5.50",
"cusparse": "11.7.3.50",
"cutensor": "1.5.0.3",
"dgx_system": "* DGX-1\n* DGX-2\n* DGX A100\n* DGX Station",
"gpu_model": "* `NVIDIA Ampere GPU Architecture <https://www.nvidia.com/en-us/geforce/turing>`_\n* `Turing <https://www.nvidia.com/en-us/geforce/turing/>`_\n* `Volta <https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/>`_\n* `Pascal <https://www.nvidia.com/en-us/data-center/pascal-gpu-architecture/>`_",
"hugectr": "Not applicable",
"hugectr": "3.7.0",
"hugectr2onnx": "Not applicable",
"merlin.core": "0.3.0",
"merlin.models": "0.4.0",
"merlin.systems": "0.2.0",
"merlin.core": "0.4.0",
"merlin.models": "0.5.0+7.g886cf6de",
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I'm surprised these aren't plain old 0.5.0 and 0.3.0, but I'm not saying anything is wrong here. I tend to believe these probably are the actual versions included in the containers, but maybe there were additional changes after the version tags to get the containers working?

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Surprises in the data are bad. I can't speak to how the versions are determined other than indicating that they come from pip as in python -m pip show "merlin.core". I need to find a more traceable way to get this data. Thanks.

"merlin.systems": "0.3.0+1.g712b04d",
"nvidia_driver": "NVIDIA Driver version 465.19.01\nor later is required. However,\nif you're running on Data Center\nGPUs (formerly Tesla) such as T4,\nyou can use any of the following\nNVIDIA Driver versions:\n\n* 418.40 (or later R418)\n* 440.33 (or later R440)\n* 450.51 (or later R450)\n* 460.27 (or later R460)\n\n**Note**: The CUDA Driver\nCompatibility Package does not\nsupport all drivers.",
"nvidia_pytorch": "Not applicable",
"nvidia_tensorflow": "Not applicable",
"nvtabular": "1.1.1",
"nvtabular": "1.2.2",
"openmpi": "4.1.2rc4",
"os": "Ubuntu 20.04.4 LTS",
"python_major": "3",
"pytorch": "Not applicable",
"release": "22.05",
"release": "22.06",
"rmm": "21.12.0",
"size": "12.04 GB",
"sm": "Not applicable",
"size": "13.95 GB",
"sm": "60, 61, 70, 75, 80",
"sparse_operation_kit": "Not applicable",
"tensorrt": "8.2.3.0+cuda11.4.2.006",
"tensorrt": "8.2.5.1+cuda11.4.2.006",
"tf": "Not applicable",
"transformers4rec": "0.1.8",
"triton": "2.20.0"
"timestamp_utc": "2022-07-06T00:33:44.548581",
"transformers4rec": "0.1.10",
"triton": "2.22.0"
}
},
"nvcr.io/nvidia/merlin/merlin-inference": {
Expand Down Expand Up @@ -414,6 +337,46 @@
"triton": "2.20.0"
}
},
"nvcr.io/nvidia/merlin/merlin-pytorch": {
"22.06": {
"base_container": "Triton version 22.05",
"compressedSize": "6.68 GB",
"cublas": "11.10.1.25",
"cuda": "11.7.0.022",
"cudf": "22.4.0",
"cudnn": "8.4.0.27+cuda11.6",
"cufft": "10.7.2.50",
"curand": "10.2.10.50",
"cusolver": "11.3.5.50",
"cusparse": "11.7.3.50",
"cutensor": "1.5.0.3",
"dgx_system": "* DGX-1\n* DGX-2\n* DGX A100\n* DGX Station",
"gpu_model": "* `NVIDIA Ampere GPU Architecture <https://www.nvidia.com/en-us/geforce/turing>`_\n* `Turing <https://www.nvidia.com/en-us/geforce/turing/>`_\n* `Volta <https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/>`_\n* `Pascal <https://www.nvidia.com/en-us/data-center/pascal-gpu-architecture/>`_",
"hugectr": "Not applicable",
"hugectr2onnx": "Not applicable",
"merlin.core": "0.4.0",
"merlin.models": "0.5.0+7.g886cf6de",
"merlin.systems": "0.3.0+1.g712b04d",
"nvidia_driver": "NVIDIA Driver version 465.19.01\nor later is required. However,\nif you're running on Data Center\nGPUs (formerly Tesla) such as T4,\nyou can use any of the following\nNVIDIA Driver versions:\n\n* 418.40 (or later R418)\n* 440.33 (or later R440)\n* 450.51 (or later R450)\n* 460.27 (or later R460)\n\n**Note**: The CUDA Driver\nCompatibility Package does not\nsupport all drivers.",
"nvidia_pytorch": "Not applicable",
"nvidia_tensorflow": "Not applicable",
"nvtabular": "1.2.2",
"openmpi": "4.1.2rc4",
"os": "Ubuntu 20.04.4 LTS",
"python_major": "3",
"pytorch": "1.11.0",
"release": "22.06",
"rmm": "21.12.0",
"size": "14.81 GB",
"sm": "Not applicable",
"sparse_operation_kit": "Not applicable",
"tensorrt": "8.2.5.1+cuda11.4.2.006",
"tf": "Not applicable",
"timestamp_utc": "2022-07-06T00:34:34.793178",
"transformers4rec": "0.1.10",
"triton": "2.22.0"
}
},
"nvcr.io/nvidia/merlin/merlin-pytorch-inference": {
"22.03": {
"base_container": "Triton version 22.02",
Expand Down Expand Up @@ -825,6 +788,46 @@
"triton": "Not applicable"
}
},
"nvcr.io/nvidia/merlin/merlin-tensorflow": {
"22.06": {
"base_container": "Triton version 22.05",
"compressedSize": "6.43 GB",
"cublas": "11.10.1.25",
"cuda": "11.7.0.022",
"cudf": "22.4.0",
"cudnn": "8.4.0.27+cuda11.6",
"cufft": "10.7.2.50",
"curand": "10.2.10.50",
"cusolver": "11.3.5.50",
"cusparse": "11.7.3.50",
"cutensor": "1.5.0.3",
"dgx_system": "* DGX-1\n* DGX-2\n* DGX A100\n* DGX Station",
"gpu_model": "* `NVIDIA Ampere GPU Architecture <https://www.nvidia.com/en-us/geforce/turing>`_\n* `Turing <https://www.nvidia.com/en-us/geforce/turing/>`_\n* `Volta <https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/>`_\n* `Pascal <https://www.nvidia.com/en-us/data-center/pascal-gpu-architecture/>`_",
"hugectr": "Not applicable",
"hugectr2onnx": "Not applicable",
"merlin.core": "0.4.0",
"merlin.models": "0.5.0+7.g886cf6de",
"merlin.systems": "0.3.0+1.g712b04d",
"nvidia_driver": "NVIDIA Driver version 465.19.01\nor later is required. However,\nif you're running on Data Center\nGPUs (formerly Tesla) such as T4,\nyou can use any of the following\nNVIDIA Driver versions:\n\n* 418.40 (or later R418)\n* 440.33 (or later R440)\n* 450.51 (or later R450)\n* 460.27 (or later R460)\n\n**Note**: The CUDA Driver\nCompatibility Package does not\nsupport all drivers.",
"nvidia_pytorch": "Not applicable",
"nvidia_tensorflow": "Not applicable",
"nvtabular": "1.2.2",
"openmpi": "4.1.2rc4",
"os": "Ubuntu 20.04.4 LTS",
"python_major": "3",
"pytorch": "Not applicable",
"release": "22.06",
"rmm": "21.12.0",
"size": "13.66 GB",
"sm": "Not applicable",
"sparse_operation_kit": "1.1.3",
"tensorrt": "8.2.5.1+cuda11.4.2.006",
"tf": "Not applicable",
"timestamp_utc": "2022-07-06T00:35:26.028456",
"transformers4rec": "0.1.10",
"triton": "2.22.0"
}
},
"nvcr.io/nvidia/merlin/merlin-tensorflow-inference": {
"22.03": {
"base_container": "Triton version 22.02",
Expand Down
3 changes: 1 addition & 2 deletions docs/source/containers.rst
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Expand Up @@ -7,10 +7,9 @@ Access the catalog of containers at http://ngc.nvidia.com/catalog/containers.
The following table identifies the container names, catalog URL, and key Merlin components.

.. list-table::
:widths: 25 50 25
:header-rows: 1

* - Container
* - Container Name
- NGC Catalog URL
- Key Merlin Components
* - merlin-hugectr
Expand Down
3 changes: 1 addition & 2 deletions docs/source/support_matrix/support_matrix_merlin_tensorflow.rst
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@@ -1,8 +1,7 @@
Merlin TensorFlow Inference Support Matrix
Merlin TensorFlow Support Matrix
==========================================

This container enables you to train and deploy NVTabular workflows and TensorFlow
models to the Triton Inference Server for production.

.. include:: ../generated/nvcr.io-nvidia-merlin-merlin-tensorflow.rst