repo_id stringlengths 21 96 | file_path stringlengths 31 155 | content stringlengths 1 92.9M | __index_level_0__ int64 0 0 |
|---|---|---|---|
rapidsai_public_repos/rapids-triton/cpp/test/triton | rapidsai_public_repos/rapids-triton/cpp/test/triton/api/model_finalize.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test/triton | rapidsai_public_repos/rapids-triton/cpp/test/triton/api/model_initialize.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test/triton | rapidsai_public_repos/rapids-triton/cpp/test/triton/api/execute.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test/triton | rapidsai_public_repos/rapids-triton/cpp/test/triton/api/instance_initialize.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test | rapidsai_public_repos/rapids-triton/cpp/test/batch/batch.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test | rapidsai_public_repos/rapids-triton/cpp/test/memory/buffer.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test | rapidsai_public_repos/rapids-triton/cpp/test/memory/types.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test | rapidsai_public_repos/rapids-triton/cpp/test/memory/resource.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test/memory | rapidsai_public_repos/rapids-triton/cpp/test/memory/detail/copy.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test/memory | rapidsai_public_repos/rapids-triton/cpp/test/memory/detail/owned_device_buffer.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test | rapidsai_public_repos/rapids-triton/cpp/test/tensor/dtype.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/cpp/test | rapidsai_public_repos/rapids-triton/cpp/test/tensor/tensor.cpp | /*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton | rapidsai_public_repos/rapids-triton/docs/usage.md | <!--
Copyright (c) 2021, NVIDIA CORPORATION.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writin... | 0 |
rapidsai_public_repos/rapids-triton/ci | rapidsai_public_repos/rapids-triton/ci/local/build.sh | #!/bin/bash
set -e
REPODIR=$(cd $(dirname $0)/../../; pwd)
EXAMPLE_TAG=rapids_triton_identity \
TEST_TAG=rapids_triton_identity_test \
$REPODIR/build.sh
if [ -z $CUDA_VISIBLE_DEVICES ]
then
docker run -v "${REPODIR}/qa/logs:/qa/logs" --gpus all --rm rapids_triton_identity_test
else
docker run -v "${REPODIR}/... | 0 |
rapidsai_public_repos/rapids-triton | rapidsai_public_repos/rapids-triton/qa/entrypoint.sh | #!/bin/bash
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton | rapidsai_public_repos/rapids-triton/qa/run_tests.sh | #!/bin/bash
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 0 |
rapidsai_public_repos/rapids-triton/qa | rapidsai_public_repos/rapids-triton/qa/L0_e2e/test_model.py | # Copyright (c) 2021, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 0 |
rapidsai_public_repos/rapids-triton/qa/L0_e2e/cpu_model_repository | rapidsai_public_repos/rapids-triton/qa/L0_e2e/cpu_model_repository/identity/config.pbtxt | backend: "rapids-identity"
max_batch_size: 32768
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
version_policy: { all { }}
instance_group [{ kind: KIND_CPU }]
parameters [ ]
dynamic_batching {
max_queue_de... | 0 |
rapidsai_public_repos/rapids-triton/qa/L0_e2e/model_repository | rapidsai_public_repos/rapids-triton/qa/L0_e2e/model_repository/identity/config.pbtxt | backend: "rapids-identity"
max_batch_size: 32768
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
version_policy: { all { }}
instance_group [{ kind: KIND_GPU }]
parameters [ ]
dynamic_batching {
max_queue_de... | 0 |
rapidsai_public_repos | rapidsai_public_repos/deeplearning/README.md | ### RAPIDS.AI Deep Learning Repo
This repository is the home of our efforts to integrate RAPIDS acceleration of dataframes on GPU into popular deep learning frameworks. The work can be broken down into three main sections:
- Dataloaders and preprocessing functionality developed to help provide connectivity between R... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2019/README.md | ## Accelerating Recommender Systems by 15x with RAPIDS (Source Code)
This content was moved to a new [competition repository](https://github.com/NVIDIA-Merlin/competitions).
| 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/04_2_Normalization.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/01_1_Exploring_DataSet.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/06_1_Intro_Dask.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/03_4_CountEncoding.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/03_3_TargetEncoding.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/03_2_Categorify.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/README.md | # RecSys2020 Tutorial: Feature Engineering for Recommender Systems
by Chris Deotte (NVidia), Benedikt Schifferer (NVidia) and Even Oldridge (NVidia)
### Content
The selection of features and proper preparation of data for deep learning or machine learning models plays a significant role in the performance of recomme... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/05_1_TimeSeries_HistoricalEvents.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/06_2_Intro_NVTabular_XGBoost.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/05_2_TimeSeries_Differences.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/00_0_Initial.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/02_1_Preprocessing.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/04_1_Binning.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/Dockerfile | ARG dev=false
FROM nvcr.io/nvidia/cuda:10.2-devel-ubuntu18.04 AS base
# install python and cudf
RUN apt-get update
RUN apt-get -y install graphviz git
ADD https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh /miniconda.sh
RUN sh /miniconda.sh -b -p /conda && /conda/bin/conda update -n base conda && /... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/04_3_GaussRank.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/03_1_CombineCategories.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/LICENSE | The MIT License (MIT)
Copyright (c) 2020, NVIDIA CORPORATION.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, me... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/04_2_Normalization.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/01_1_Exploring_DataSet.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/06_1_Intro_Dask.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/03_4_CountEncoding.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/03_3_TargetEncoding.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/03_2_Categorify.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/05_1_TimeSeries_HistoricalEvents.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/06_2_Intro_NVTabular_XGBoost.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/05_2_TimeSeries_Differences.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/00_0_Initial.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/02_1_Preprocessing.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/04_1_Binning.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/04_3_GaussRank.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/solutions/03_1_CombineCategories.ipynb | # The MIT License (MIT)
# Copyright (c) 2020, NVIDIA CORPORATION.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/images/dask-dataframe.svg | <?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!-- Created with Inkscape (http://www.inkscape.org/) -->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:cc="http://creativecommons.org/ns#"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/images/dask-array-black-text.svg | <?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!-- Created with Inkscape (http://www.inkscape.org/) -->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:cc="http://creativecommons.org/ns#"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns... | 0 |
rapidsai_public_repos/deeplearning/RecSys2020Tutorial | rapidsai_public_repos/deeplearning/RecSys2020Tutorial/data/README.md | # RecSys2020 Tutorial: Feature Engineering for Recommender Systems
by Chris Deotte (Nvidia), Benedikt Schifferer (Nvidia) and Even Oldridge (Nvidia)
### Content
The selection of features and proper preparation of data for deep learning or machine learning models plays a significant role in the performance of recomme... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/bootstrap_model.py | from mpnn_model.common import *
from torch_scatter import *
from torch_geometric.utils import scatter_
import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
# Fast ai
from fastai.tabular import *
from fastai.callbacks import SaveModelCallback
#__all__ = ['LinearBn', 'MlpBn', 'CustomTabu... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/tree.md | .
├── build_data
│ ├── __init__.py
│ ├── lib
│ │ ├── include.py
│ │ ├── __init__.py
│ │ ├── net
│ │ │ ├── __init__.py
│ │ │ ├── __pycache__
│ │ │ │ ├── __init__
│ │ │ └── rate.py
│ │ └── utility
│ │ ├── draw.py
│ │ ├── file.py
│ │ ├── __init__.py
│ ... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/README.md | Placeholder for our 33rd place solution.
| 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/train_MPNN_RNN_BOOTSTRAP.ipynb | import os
GPU_id = 2
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_id)
from fastai.basic_train import *
from fastai.callbacks import SaveModelCallback
from functools import partial
import cudf as gd
import warnings
import glob
import gzip
from torch.utils.dlpack import from_dlpack
from mpnn_model.common import *
fro... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/train_MPNN.ipynb | import os
GPU_id = 0
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_id)
from fastai.basic_train import *
from fastai.callbacks import SaveModelCallback
from functools import partial
from torch.utils.dlpack import from_dlpack
import cudf as gd
import warnings
import glob
from mpnn_model.common import *
from mpnn_mode... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/save_pretrained_single_models.ipynb | import os
GPU_id = 1
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_id)
from torch_scatter import *
from torch_geometric.utils import scatter_
import torch
import torch.nn as nn
import numbers
import torch
from torch import _utils
from fastai.torch_core import to_device
import torch.nn.functional as F
from fastai.bas... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/train_MPNN_RNN_SINGLE_TYPE.ipynb | import os
GPU_id = 0
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_id)
from mpnn_model.common import *
from mpnn_model.common_constants import *
from mpnn_model.dataset import TensorBatchDataset, BatchDataBunch, BatchDataLoader
from mpnn_model.data_collate import tensor_collate_rnn
from mpnn_model.GaussRank impor... | 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/README_old.md | Placeholder for our 33rd place solution.
| 0 |
rapidsai_public_repos/deeplearning | rapidsai_public_repos/deeplearning/champs-scalar-coupling/train_MPNN_RNN.ipynb | import os
GPU_id = 2
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_id)
from fastai.basic_train import *
from fastai.callbacks import SaveModelCallback
from functools import partial
from torch.utils.dlpack import from_dlpack
import cudf as gd
import warnings
import glob
from mpnn_model.common import *
from mpnn_mode... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/scripts/train_mpnn.py | #############################################################################################################
# #
# Run a training process for model: MPNN ... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/scripts/train_mpnn_rnn.py | #############################################################################################################
# #
# Run a training process for model: MPNN+RNN ... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/scripts/bootsrap_train_mpnn_rnn.py | #############################################################################################################
# #
# helper functions ... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/scripts/train_type.py | # Define dataset
# coupling_cols = ['atom_index_0', 'atom_index_1','coupling_type','scalar_coupling',
# 'gaussrank_coupling','fc','sd','pso','dso','id', 'path_index_0', 'path_index_1',
# 'path_index_2', 'path_index_3', 'path_btype_0', 'path_btype_1',
# 'path_btype_2',... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/parallel_process.py | from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
"""
Credit to http://danshiebler.com
"""
def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=3):
"""
A parallel version of the map function with a progress bar.
Args:
array (... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/common.py | from lib.include import *
from lib.utility.draw import *
from lib.utility.file import *
from lib.net.rate import *
#---------------------------------------------------------------------------------
COMMON_STRING ='@%s: \n' % os.path.basename(__file__)
if 1:
SEED = int(time.time()) #35202 #35202 #123 #
r... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/build_train_validation_rnn.ipynb | from build_predictions import *
from GaussRank import GaussRankMap
import pandas as pd
from create_parquet import *
from data import *
import warnings
warnings.filterwarnings("ignore") DATA_DIR='/rapids/notebooks/srabhi/champs-2019/input/'node_frame = pd.read_csv(DATA_DIR+'parquet/baseline_node_frame.csv')node_frame.... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/data.py | #
#
#
# This module aims to create molecule graphs from Kaggle data and rdkit
#
# It also give the possibilit to create cv folds as .npy files with molecule names
#
#
#
#####################################################################################
from common import *
from atom_features import *
from coll... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/atom_features.py | import numpy as np
from collections import defaultdict
import copy
import itertools
import networkx as nx
#External package
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import ChemicalFeatures
from rdkit import RDConfig
import rdkit.Chem.Draw
from rdkit.Chem.Draw.MolDrawing import MolDrawin... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/baseline_node_frame_from_csv_pandas.ipynb | import cudf as gdpath = '/rapids/notebooks/srabhi/champs-2019/input/csv/'%%time
train = gd.read_csv('%s/train.csv'%path)
test = gd.read_csv('%s/test.csv'%path)
print(train.shape,test.shape)
for col in train.columns:
if train[col].dtype!='O':
train[col] = train[col].astype('float32')
if col in test.c... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/create_parquet.py | #
#
#
#
# This module aims to create a parquet file from .pkl graph files
#
# It also gives the possibility to compute the gaussrank and create
# train/validation .parquet files for each fold with. each line represents
# a molecules and its information in the following order :
# 'molecule_name',
# 'num_node'... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/build_train_validation.ipynb | #from build_predictions import *
#from GaussRank import GaussRankMap
import pandas as pd
#from create_parquet import *
#from data import *
import warnings
warnings.filterwarnings("ignore") DATA_DIR='/rapids/notebooks/srabhi/champs-2019/input/'node_frame = pd.read_csv(DATA_DIR+'parquet/baseline_node_frame.csv')node_fr... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/build_train_validation_rnn_per_type.ipynb | from build_predictions import *
from GaussRank import GaussRankMap
import pandas as pd
from create_parquet import *
from data import *
import warnings
warnings.filterwarnings("ignore") DATA_DIR='/rapids/notebooks/srabhi/champs-2019/input/'COUPLING_TYPE# node frame
molecule_node = pd.read_parquet(DATA_DIR+'parquet/mo... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/baseline_coupling_frame_from_csv_cudf.ipynb | import cudf as gdpath = '/rapids/notebooks/srabhi/champs-2019/input/csv/'%%time
train = gd.read_csv('%s/train.csv'%path)
test = gd.read_csv('%s/test.csv'%path)
print(train.shape,test.shape)
test['scalar_coupling_constant'] = 0.0
for col in train.columns:
if train[col].dtype!='O':
train[col] = train[col].as... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/build_baseline_dataframes.ipynb | from create_parquet import *
from data import *
import warnings
warnings.filterwarnings("ignore") def get_node_from_graph(molecule_file):
'''
- molecule file: path to %molecule_name.pickle
Returns:
Convert the pickled graph to a padded vector with all the molecule information
'''
molecul... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib/include.py | import os
from datetime import datetime
PROJECT_PATH = os.path.dirname(os.path.realpath(__file__).replace('/lib',''))
IDENTIFIER = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
#numerical libs
import math
import numpy as np
import random
import PIL
#import cv2
import matplotlib
#matplotlib.use('TkAgg')
#matplotlib.u... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib/net/rate.py | # learning rate schduler
from lib.include import *
# http://elgoacademy.org/anatomy-matplotlib-part-1/
def plot_rates(fig, lrs, title=''):
N = len(lrs)
epoches = np.arange(0,N)
#get limits
max_lr = np.max(lrs)
xmin=0
xmax=N
dx=2
ymin=0
ymax=max_lr*1.2
dy=(ymax-ymin)/10
... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib/utility/draw.py | import os
#qt bug ???
os.environ['QT_XKB_CONFIG_ROOT']='/usr/share/X11/xkb/'
from lib.include import *
import matplotlib.cm
# draw -----------------------------------
def image_show(name, image, resize=1):
H,W = image.shape[0:2]
cv2.namedWindow(name, cv2.WINDOW_GUI_NORMAL) #WINDOW_NORMAL
#cv2.namedWindo... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib | rapidsai_public_repos/deeplearning/champs-scalar-coupling/build_data/lib/utility/file.py | from lib.include import *
import builtins
import re
class Struct(object):
def __init__(self, is_copy=False, **kwargs):
self.add(is_copy, **kwargs)
def add(self, is_copy=False, **kwargs):
#self.__dict__.update(kwargs)
if is_copy == False:
for key, value in kwargs.items():
... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/parallel_process.py | from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
"""
Credit to http://danshiebler.com
"""
def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=3):
"""
A parallel version of the map function with a progress bar.
Args:
array (... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/common.py | from .lib.include import *
from .lib.utility.draw import *
from .lib.utility.file import *
from .lib.net.rate import *
#---------------------------------------------------------------------------------
COMMON_STRING ='@%s: \n' % os.path.basename(__file__)
if 1:
SEED = int(time.time()) #35202 #35202 #123 #
... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/train_loss.py | import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
#############################################################################################################
# ... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/data.py | #
#
#
# This module aims to create molecule graphs from Kaggle data and rdkit
#
# It also give the possibilit to create cv folds as .npy files with molecule names
#
#
#
#####################################################################################
#from atom_features import *
from collections import defau... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/common_model.py | from mpnn_model.common import *
from torch_scatter import *
from torch_geometric.utils import scatter_
import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
# Fast ai
from fastai.tabular import *
from fastai.callbacks import SaveModelCallback
__all__ = ['LinearBn', 'MlpBn', 'CustomTabula... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/GaussRank.py | import numpy as np
from scipy.special import erfinv
from bisect import bisect_left
import pandas as pd
class GaussRankMap():
def __init__(self, training_maps=[], coupling_order=[]):
self.epsilon = 0.001
self.lower = -1 + self.epsilon
self.upper = 1 - self.epsilon
self.range = self.... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/dataset.py | #
# coupling_cols = ['atom_index_0', 'atom_index_1','coupling_type','scalar_coupling',
# 'gaussrank_coupling','fc','sd','pso','dso','id',]
#
# edge_cols : ['atom_index_0', 'atom_index_1', 'edge_type', 'distance', 'angle' ]
#
# nodes cols : ['symbol','acceptor', 'donor', 'aromatic', 'hybridization', '... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/message_passing.py | from mpnn_model.common_model import *
from mpnn_model.common import *
from torch_scatter import *
from torch_geometric.utils import scatter_
import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
__all__ = ['message_pass' , 'MessagePassing', 'GRUUpdate', 'Set2Set']
#####################... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/common_constants.py | import torch
### Helpers for normalization
NUM_COUPLING_TYPE=8
COUPLING_TYPE_STATS=[
#type #mean, std, min, max
'1JHC', 94.9761528641869, 18.27722399839607, 66.6008, 204.8800,
'2JHC', -0.2706244378832, 4.52360876732858, -36.2186, 42.8192,
'3JHC', 3.6884695895355, 3.070906470054... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/build_predictions.py | import os
from datetime import datetime
from functools import partial
from timeit import default_timer as timer
from time import time
import warnings
warnings.filterwarnings("ignore")
from mpnn_model.common import *
from mpnn_model.common_constants import *
from mpnn_model.train_loss import lmae_criterion
from mpnn_... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/data_collate.py | import torch
import torch.nn.functional as F
from mpnn_model.common import *
from mpnn_model.common_constants import *
from mpnn_model.data import *
import copy
DATA_DIR = '/rapids/notebooks/srabhi/champs-2019/input'
__all__ = ['tensor_collate_rnn', 'tensor_collate_baseline']
def tensor_collate_rnn(batch, batch_... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/callback.py | import numpy as np
import pandas as pd
from fastai.callbacks import SaveModelCallback
from fastai.callbacks import Callback
from fastai.torch_core import add_metrics
import torch
from torch import nn
import torch.nn.functional as F
import pdb
from mpnn_model.common_constants import NUM_COUPLING_TYPE, COUPLING_T... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/model.py | from mpnn_model.common import *
from torch_scatter import *
from torch_geometric.utils import scatter_
import torch
import torch.nn as nn
import numbers
from mpnn_model.common_model import *
from mpnn_model.regression_head import *
from mpnn_model.message_passing import *
from mpnn_model.RNN_attention import *
... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/RNN_attention.py | from mpnn_model.common import *
import torch
import torch.nn as nn
#############################################################################################################
# #
# ... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/radam.py | import math
import torch
from torch.optim.optimizer import Optimizer, required
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
self.buffer = [[None, None, None] for in... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/helpers.py | import yaml
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
#############################################################################################################
# #
# ... | 0 |
rapidsai_public_repos/deeplearning/champs-scalar-coupling | rapidsai_public_repos/deeplearning/champs-scalar-coupling/mpnn_model/regression_head.py | from mpnn_model.common import *
from mpnn_model.common_model import *
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['get_regression_module']
#############################################################################################################
# ... | 0 |
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