repo_id stringlengths 21 96 | file_path stringlengths 31 155 | content stringlengths 1 92.9M | __index_level_0__ int64 0 0 |
|---|---|---|---|
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/README.md | ### Example Notebooks to run RAPIDS with Azure Kubernetes Service and Dask Kubernetes
- Detailed instructions to set up RAPIDS with AKS is in the markdown file [Detailed_setup_guide.md](Detailed_setup_guide.md) . Go through this before you try to run any other notebooks.
- Shorter example notebook using Dask + RAPIDS ... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/nvidia-device-plugin-ds.yml | apiVersion: apps/v1
kind: DaemonSet
metadata:
name: nvidia-device-plugin-daemonset
namespace: gpu-resources
spec:
selector:
matchLabels:
name: nvidia-device-plugin-ds
updateStrategy:
type: RollingUpdate
template:
metadata:
# Mark this pod as a critical add-on; when enabled, the critica... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/Dask_cuML_Exploration_Full.ipynb | import certifi
import cudf
import cuml
import cupy as cp
import numpy as np
import os
import pandas as pd
import random
import seaborn as sns
import time
import yaml
from functools import partial
from math import cos, sin, asin, sqrt, pi
from tqdm import tqdm
from typing import Optional
import dask
import dask.array ... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/MNMG_XGBoost.ipynb | ## Uncomment the following and install some libraries at the beginning.
# # If azureml is not present install azureml-core.
# # Further opendatasets is in preview mode hence it is not available in the main sdk and needs to be installed separately.
# # Installing azureml-opendatasets with for some weird reason install... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/Dockerfile | FROM docker.io/rapidsai/rapidsai-core:21.06-cuda11.2-runtime-ubuntu18.04-py3.8
RUN source activate rapids \
&& pip install xgboost \
&& pip install gcsfs \
&& pip install adlfs
| 0 |
rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/podspecs | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/podspecs/azure/cpu-worker-specs.yml | kind: Pod
metadata:
labels:
dask_type: "worker"
spec:
restartPolicy: Never
tolerations:
- key: "daskrole"
operator: "Equal"
value: "worker"
effect: "NoSchedule"
containers:
- image: <username>.azurecr.io/aks-mnmg/dask-unified:21.06
imagePullPolicy: IfNotPresent
args: [ ... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/podspecs | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/podspecs/azure/cuda-worker-specs.yml | kind: Pod
metadata:
labels:
dask_type: "worker"
spec:
restartPolicy: Never
tolerations:
- key: "daskrole"
operator: "Equal"
value: "worker"
effect: "NoSchedule"
containers:
- image: <username>.azurecr.io/aks-mnmg/dask-unified:21.06
imagePullPolicy: IfNotPresent
args: [ ... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/podspecs | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/podspecs/azure/scheduler-specs.yml | kind: Pod
metadata:
labels:
dask_type: "scheduler"
spec:
restartPolicy: Never
tolerations:
- key: "daskrole"
operator: "Equal"
value: "scheduler"
effect: "NoSchedule"
containers:
- image: <username>.azurecr.io/aks-mnmg/dask-unified:21.06
imagePullPolicy: IfNotPresent
ar... | 0 |
rapidsai_public_repos/cloud-ml-examples/k8s-dask | rapidsai_public_repos/cloud-ml-examples/k8s-dask/notebooks/xgboost-gpu-hpo-job-parallel-k8s.ipynb | # Choose the same RAPIDS image you used for launching the notebook session
rapids_image = "rapidsai/rapidsai-core:22.10-cuda11.5-runtime-ubuntu20.04-py3.9"
# Use the number of worker nodes in your Kubernetes cluster.
n_workers = 4from dask_kubernetes.operator import KubeCluster
cluster = KubeCluster(name="rapids-dask"... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/rapids_ec2_mnmg.ipynb | # !pip install "dask-cloudprovider[aws]"import math
from datetime import datetime
import cudf
import dask
import dask_cudf
import numpy as np
from cuml.dask.common import utils as dask_utils
from cuml.dask.ensemble import RandomForestRegressor
from cuml.metrics import mean_squared_error
from dask_cloudprovider.aws im... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/README.md | # RAPIDS on AWS
There are a few example notebooks to help you get started with running RAPIDS on AWS. Here are the instructions to setup the environment locally to run the examples.
Sections in README
1. Instructions for Running RAPIDS + SageMaker HPO
2. Instructions to run multi-node multi-GPU (MNMG) example on EC2
... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_hpo.ipynb | import sagemaker
import string
import randomexecution_role = sagemaker.get_execution_role()
session = sagemaker.Session()
account=!(aws sts get-caller-identity --query Account --output text)
region=!(aws configure get region)account, regionestimator_info = {
'rapids_container': 'rapidsai/rapidsai-cloud-ml:latest',... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/helper_functions.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/rapids_intro.ipynb | import cudf
url = "https://github.com/plotly/datasets/raw/master/tips.csv"
tips_df = cudf.read_csv(url)
tips_df['tip_percentage'] = tips_df['tip']/tips_df['total_bill']*100
# Display average tip by dining party size
print(tips_df.groupby('size').tip_percentage.mean())from cuml import make_regression, train_test_spli... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_hpo_extended.ipynb | %pip install --upgrade boto3import sagemaker
from helper_functions import *execution_role = sagemaker.get_execution_role()
session = sagemaker.Session()
account=!(aws sts get-caller-identity --query Account --output text)
region=!(aws configure get region)account, region# please choose dataset S3 bucket and directory
... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/aws/rapids_studio_hpo.ipynb | import sagemaker
from helper_functions import *execution_role = sagemaker.get_execution_role()
session = sagemaker.Session()
account=!(aws sts get-caller-identity --query Account --output text)
region = [session.boto_region_name]account, region# please choose dataset S3 bucket and directory
data_bucket = 'sagemaker-ra... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_higgs/sagemaker_rapids_higgs.ipynb | import sagemaker
import time
import boto3execution_role = sagemaker.get_execution_role()
session = sagemaker.Session()
region = boto3.Session().region_name
account = boto3.client('sts').get_caller_identity().get('Account')account, regions3_data_dir = session.upload_data(path='dataset', key_prefix='dataset/higgs-datase... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_higgs/README.md | This repository contains code and config files supporting the following blog post:
https://medium.com/@shashankprasanna/running-rapids-experiments-at-scale-using-amazon-sagemaker-d516420f165b
| 0 |
rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_higgs | rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_higgs/docker/rapids-higgs.py | #!/usr/bin/env python
# coding: utf-8
from cuml import RandomForestClassifier as cuRF
from cuml.preprocessing.model_selection import train_test_split
import cudf
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
import os
from urllib.request import urlretrieve
import gzip
import argpars... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_higgs | rapidsai_public_repos/cloud-ml-examples/aws/rapids_sagemaker_higgs/docker/Dockerfile | FROM rapidsai/rapidsai-core:22.12-cuda11.5-runtime-ubuntu18.04-py3.9
# add sagemaker-training-toolkit [ requires build tools ], flask [ serving ], and dask-ml
RUN apt-get update && apt-get install -y --no-install-recommends build-essential \
&& source activate rapids \
&& pip3 install sagemaker-training cupy-... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/gpu_tree_shap/gpu_tree_shap.ipynb | import io
import os
import boto3
import sagemaker
import time
role = sagemaker.get_execution_role()
region = boto3.Session().region_name
# S3 bucket for saving code and model artifacts.
# Feel free to specify a different bucket here if you wish.
bucket = sagemaker.Session().default_bucket()
prefix = "sagemaker/DEMO-x... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/gpu_tree_shap/train.py | from __future__ import print_function
import argparse
import json
import logging
import os
import pickle as pkl
import pandas as pd
import xgboost as xgb
from sagemaker_containers import entry_point
from sagemaker_xgboost_container import distributed
# from sagemaker_xgboost_container.data_utils import get_dmatrix
im... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/gpu_tree_shap/inference.py | import json
import os
import pickle as pkl
import numpy as np
import sagemaker_xgboost_container.encoder as xgb_encoders
import xgboost as xgb
def model_fn(model_dir):
"""
Deserialize and return fitted model.
"""
model_file = "xgboost-model"
booster = pkl.load(open(os.path.join(model_dir, model_f... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/entrypoint.sh | #!/bin/bash
source activate rapids
if [[ "$1" == "serve" ]]; then
echo -e "@ entrypoint -> launching serving script \n"
python serve.py
else
echo -e "@ entrypoint -> launching training script \n"
python train.py
fi | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/train.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/MLWorkflow.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/Dockerfile | FROM rapidsai/rapidsai-core:22.12-cuda11.5-runtime-ubuntu18.04-py3.9
ENV AWS_DATASET_DIRECTORY="10_year"
ENV AWS_ALGORITHM_CHOICE="XGBoost"
ENV AWS_ML_WORKFLOW_CHOICE="multiGPU"
ENV AWS_CV_FOLDS="10"
# ensure printed output/log-messages retain correct order
ENV PYTHONUNBUFFERED=True
# add sagemaker-training-too... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/HPOConfig.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/HPODatasets.py | """ Airline Dataset target label and feature column names """
airline_label_column = 'ArrDel15'
airline_feature_columns = ['Year', 'Quarter', 'Month', 'DayOfWeek',
'Flight_Number_Reporting_Airline',
'DOT_ID_Reporting_Airline',
'OriginCity... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/code/serve.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/workflows/MLWorkflowSingleCPU.py | #
# Copyright (c) 2019-2021, 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 a... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/workflows/MLWorkflowMultiCPU.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/workflows/MLWorkflowMultiGPU.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/workflows/MLWorkflowSingleGPU.py | #
# Copyright (c) 2019-2021, 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 ag... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/local_testing/Dockerfile.14 | FROM rapidsai/rapidsai:0.14-cuda11.0-base-ubuntu18.04-py3.7
ENV AWS_DATASET_DIRECTORY="1_year"
ENV AWS_ALGORITHM_CHOICE="XGBoost"
ENV AWS_ML_WORKFLOW_CHOICE="singleGPU"
ENV AWS_CV_FOLDS="3"
# ensure printed output/log-messages retain correct order
ENV PYTHONUNBUFFERED=True
# add sagemaker-training-toolkit [ require... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/local_testing/build_and_run_local_hpo.sh | #!/usr/bin/env bash
# launch container with local directory paths mounted to mirror SageMaker
echo 'run RAPIDS HPO container with local directory mirroring SageMaker paths'
# --------------------------------
# decide what runs in this script
# --------------------------------
# test multiple configurations [ xgboost... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/local_testing/Dockerfile.16 | FROM rapidsai/rapidsai-nightly:0.16-cuda11.0-base-ubuntu18.04-py3.7
ENV AWS_DATASET_DIRECTORY="1_year"
ENV AWS_ALGORITHM_CHOICE="XGBoost"
ENV AWS_ML_WORKFLOW_CHOICE="singleGPU"
ENV AWS_CV_FOLDS="3"
# ensure printed output/log-messages retain correct order
ENV PYTHONUNBUFFERED=True
# add sagemaker-training-toolkit [ ... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws/code | rapidsai_public_repos/cloud-ml-examples/aws/code/local_testing/Dockerfile.15 | FROM rapidsai/rapidsai:0.15-cuda11.0-base-ubuntu18.04-py3.7
ENV AWS_DATASET_DIRECTORY="1_year"
ENV AWS_ALGORITHM_CHOICE="XGBoost"
ENV AWS_ML_WORKFLOW_CHOICE="singleGPU"
ENV AWS_CV_FOLDS="3"
# ensure printed output/log-messages retain correct order
ENV PYTHONUNBUFFERED=True
# add sagemaker-training-toolkit [ require... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/environment_setup/README.md | ## **Augment SageMaker with a RAPIDS Conda Kernel**
This section describes the process required to augment a SageMaker notebook instance with a RAPIDS conda environment.
The RAPIDS Ops team builds and publishes the latest RAPIDS release as a packed conda tarball.
> e.g.: https://data.rapids.ai/conda-pack/rapidsai/rapi... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/environment_setup/lifecycle_script | #!/bin/bash
set -e
sudo -u ec2-user -i <<'EOF'
mkdir -p rapids_kernel
cd rapids_kernel
wget -q https://data.rapids.ai/conda-pack/rapidsai/rapids22.06_cuda11.5_py3.8.tar.gz
echo "wget completed"
tar -xzf *.gz
echo "unzip completed"
source /home/ec2-user/rapids_kernel/bin/activate
conda-unpack
echo "unpack complete... | 0 |
rapidsai_public_repos/cloud-ml-examples/aws | rapidsai_public_repos/cloud-ml-examples/aws/autogluon/autogluon_airline.ipynb | import warnings
warnings.filterwarnings('ignore')from autogluon.tabular import TabularDataset, TabularPredictor
from autogluon.core.utils import generate_train_test_splitpath_prefix = 'https://sagemaker-rapids-hpo-us-west-2.s3-us-west-2.amazonaws.com/autogluon/'
path_train = path_prefix + 'train_data.parquet'
data = T... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/ci/axis.yaml | CUDA_VER:
- "11.5"
- "11.2"
- "11.0"
IMG_TYPE:
- base
LINUX_VER:
- ubuntu20.04
PYTHON_VER:
- "3.9"
RAPIDS_VER:
- "22.10"
| 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/ci/run.sh | #!/bin/bash
set -e
# Overwrite HOME to WORKSPACE
export HOME=$WORKSPACE
# Install gpuCI tools
curl -s https://raw.githubusercontent.com/rapidsai/gpuci-tools/main/install.sh | bash
source ~/.bashrc
cd ~
# Set vars
export DOCKER_IMG="rapidsai/rapidsai-cloud-ml"
export DOCKER_TAG="${RAPIDS_VER}-cuda${CUDA_VER}-${IMG_TY... | 0 |
rapidsai_public_repos/cloud-ml-examples/common | rapidsai_public_repos/cloud-ml-examples/common/code/create_packed_conda_env | #!/usr/bin/env bash
# Copyright (c) 2019-2021, NVIDIA CORPORATION.
# RAPIDS conda packing script
# This script is used to build the component(s) in this repo from
# source, and can be called with various options to customize the
# build as needed (see the help output for details)
# Abort script on first error
set -e... | 0 |
rapidsai_public_repos/cloud-ml-examples/common | rapidsai_public_repos/cloud-ml-examples/common/docker/DockerHubREADME.md | # RAPIDS Cloud Machine Learning
RAPIDS is a suite of open-source libraries that bring GPU acceleration to data science pipelines. Users building cloud-based machine learning experiments can take advantage of this acceleration throughout their workloads to build models faster, cheaper, and more easily on the cloud plat... | 0 |
rapidsai_public_repos/cloud-ml-examples/common | rapidsai_public_repos/cloud-ml-examples/common/docker/Dockerfile.training.unified | ARG RAPIDS_VER="22.10"
ARG CUDA_VER="11.5"
ARG IMG_TYPE="base"
ARG LINUX_VER="ubuntu20.04"
ARG PYTHON_VER="3.9"
FROM rapidsai/rapidsai-core:${RAPIDS_VER}-cuda${CUDA_VER}-${IMG_TYPE}-${LINUX_VER}-py${PYTHON_VER}
# ensure printed output/log-messages retain correct order
ENV PYTHONUNBUFFERED=True
RUN apt update -y \
... | 0 |
rapidsai_public_repos/cloud-ml-examples/common/docker | rapidsai_public_repos/cloud-ml-examples/common/docker/infrastructure/entrypoint.sh | source /conda/etc/profile.d/conda.sh
conda activate rapids
ARGS=( "$@" )
EXEC_CONTEXT=""
# If we're doing SageMaker HPO, this file will exist
aws_hpo_params_path="/opt/ml/input/config/hyperparameters.json"
if [[ -f "${aws_hpo_params_path}" ]]; then
EXEC_CONTEXT="aws_sagemaker_hpo"
fi
# If we're doing GCP AI-Platfo... | 0 |
rapidsai_public_repos | rapidsai_public_repos/integration/README.md | # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/> Integration
RAPIDS - combined conda package for all of RAPIDS libraries
## RAPIDS Meta-packages
The conda recipe in the `conda` folder provides the RAPIDS meta-packages, which when installed will provide the latest RA... | 0 |
rapidsai_public_repos | rapidsai_public_repos/integration/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
rapidsai_public_repos/integration/conda | rapidsai_public_repos/integration/conda/recipes/README.md | # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/> Meta-packages
## Overview
These packages provide one-line installs for RAPIDS as well as environment
setups for RAPIDS users and the RAPIDS [containers](https://github.com/rapidsai/build).
## Meta-packages
### Packag... | 0 |
rapidsai_public_repos/integration/conda | rapidsai_public_repos/integration/conda/recipes/versions.yaml | # Copyright (c) 2021-2023, NVIDIA CORPORATION.
# Versions for `rapids-xgboost` meta-pkg
xgboost_version:
- '=1.7.6'
cuda11_cuda_python_version:
- '>=11.7.1,<12.0a'
cuda12_cuda_python_version:
- '>=12.0.0,<13.0a'
cupy_version:
- '>=12.0.0'
nccl_version:
- '>=2.9.9,<3.0a0'
networkx_version:
- '>=2.5.1'
numb... | 0 |
rapidsai_public_repos/integration/conda/recipes | rapidsai_public_repos/integration/conda/recipes/rapids-xgboost/meta.yaml | # Copyright (c) 2019-2023, NVIDIA CORPORATION.
{% set rapids_version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
{% set major_minor_version = rapids_version.split('.')[0] + '.' + rapids_version.split('.')[1] %}
{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
{% set cu... | 0 |
rapidsai_public_repos/integration/conda/recipes | rapidsai_public_repos/integration/conda/recipes/rapids-xgboost/LICENSE | The license of this package is a combination of the dependent packages contained herein.
| 0 |
rapidsai_public_repos/integration/conda/recipes | rapidsai_public_repos/integration/conda/recipes/rapids/meta.yaml | # Copyright (c) 2019-2023, NVIDIA CORPORATION.
{% set rapids_version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
{% set major_minor_version = rapids_version.split('.')[0] + '.' + rapids_version.split('.')[1] %}
{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
{% set cu... | 0 |
rapidsai_public_repos/integration/conda/recipes | rapidsai_public_repos/integration/conda/recipes/rapids/LICENSE | The license of this package is a combination of the dependent packages contained herein.
| 0 |
rapidsai_public_repos/integration | rapidsai_public_repos/integration/ci/build_python.sh | #!/bin/bash
# Copyright (c) 2022-2023, NVIDIA CORPORATION.
set -euo pipefail
source rapids-env-update
CONDA_CONFIG_FILE="conda/recipes/versions.yaml"
rapids-print-env
rapids-logger "Build rapids-xgboost"
rapids-conda-retry mambabuild \
--use-local \
--variant-config-files "${CONDA_CONFIG_FILE}" \
conda/reci... | 0 |
rapidsai_public_repos/integration | rapidsai_public_repos/integration/ci/conda-pack.sh | #!/bin/bash
# Copyright (c) 2023, NVIDIA CORPORATION.
set -e
RAPIDS_VER="23.12"
VERSION_DESCRIPTOR="a"
CONDA_USERNAME="rapidsai-nightly"
if [ "$GITHUB_REF_TYPE" = "tag" ]; then
VERSION_DESCRIPTOR=""
CONDA_USERNAME="rapidsai"
fi
CUDA_VERSION="${RAPIDS_CUDA_VERSION%.*}"
CONDA_ENV_NAME="rapids${RAPIDS_VER}${VER... | 0 |
rapidsai_public_repos/integration/ci | rapidsai_public_repos/integration/ci/release/update-version.sh | #!/bin/bash
# Copyright (c) 2021-2023, NVIDIA CORPORATION.
###############################
# Integration Version Updater #
###############################
## Usage
# bash update-version.sh <new_version>
# Workaround for MacOS where BSD sed doesn't support the flags
# Install MacOS gsed with `brew install gnu-sed`
un... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cugraph-pg/README.md | # cugraph-pg
| 0 |
rapidsai_public_repos | rapidsai_public_repos/dask-cuda-benchmarks/.pre-commit-config.yaml | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
repos:
- repo: https://github.com/PyCQA/isort
rev: 5.12.0
hooks:
- id: isort
types: [python]
- repo: https://github.com/psf/black
rev: 22.10.0
hooks:
-... | 0 |
rapidsai_public_repos | rapidsai_public_repos/dask-cuda-benchmarks/setup.cfg | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
[flake8]
filename = *.py
max-line-length = 88
extend-ignore =
# line break before binary operator
W503,
# whitespace before :
E203
| 0 |
rapidsai_public_repos | rapidsai_public_repos/dask-cuda-benchmarks/pyproject.toml | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
[tool.black]
line-length = 88
target-version = ["py38"]
include = '\.pyi?$'
[tool.isort]
atomic = true
profile = "black"
line_length = 88
skip_gitignore = true
known_dask = """
dask
distributed
dask_cuda
"""
known_rapids = """... | 0 |
rapidsai_public_repos | rapidsai_public_repos/dask-cuda-benchmarks/README.md | # RAPIDS benchmarks
This repository contains a collection of benchmarks and run scripts
for single and multi-node benchmarking of RAPIDS components with a
focus on [dask/distributed](https://dask.org) with
[CUDF-](https://github.com/rapidsai/cudf)) and
[cupy-](https://github.com/cupy/cupy) accelerated backends.
| 0 |
rapidsai_public_repos | rapidsai_public_repos/dask-cuda-benchmarks/CONTRIBUTING.md | # Contributing
If you are interested in contributing to dask-cuda-benchmarks, your contributions will fall
into three categories:
1. You want to report a bug, feature request, or documentation issue
- File an [issue](https://github.com/rapidsai/dask-cuda-benchmarks/issues/new)
describing what you encountered o... | 0 |
rapidsai_public_repos | rapidsai_public_repos/dask-cuda-benchmarks/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks | rapidsai_public_repos/dask-cuda-benchmarks/analysis/make-multi-node-charts.py | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
from collections.abc import Iterable
from itertools import chain
from pathlib import Path
import altair as alt
import numpy as np
import pandas as pd
import typer
from altair import datum, expr
from altair.utils import sanitize_dataframe
... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks | rapidsai_public_repos/dask-cuda-benchmarks/analysis/pull-and-update-data.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -ex
SINGLE_NODE_REMOTE_LOCATION=$1
MULTI_NODE_REMOTE_LOCATION=$2
LOCAL_DATA_LOCATION=$3
WEBSITE_DIRECTORY=$4
rsync -rvupm ${SINGLE_NODE_REMOTE_LOCATION} ${LOCAL_DATA_LOCATION}/single-node \
--filter '+ */' \
-... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks | rapidsai_public_repos/dask-cuda-benchmarks/analysis/make-single-node-charts.py | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
import ast
import json
import re
from collections.abc import Callable
from functools import partial
from itertools import chain
from operator import itemgetter, methodcaller
from pathlib import Path
from typing import cast
from warnings im... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks | rapidsai_public_repos/dask-cuda-benchmarks/analysis/build-and-submit.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -ex
DOCKER_BUILD_SERVER=$1
DOCKER_BUILD_DIRECTORY=$3
JOB_SUBMISSION_SERVER=$2
JOB_SUBMISSION_DIRECTORY=$4
ssh ${DOCKER_BUILD_SERVER} "(cd ${DOCKER_BUILD_DIRECTORY}; ./build-images.sh)"
ssh ${JOB_SUBMISSION_SERVER} "(cd ~/$... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/merge-outputs.py | import glob
import os
from itertools import chain
import altair as alt
import click
import numpy as np
import pandas as pd
from altair import datum, expr
from altair.utils import sanitize_dataframe
def hmean(a):
"""Harmonic mean"""
if len(a):
return 1 / np.mean(1 / a)
else:
return 0
def... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/gc-workers.py | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
import click
from distributed import Client
def cleanup_lru_cache():
import gc
from distributed.worker import cache_loads
cache_loads.clear()
gc.collect()
@click.command()
@click.argument("scheduler_file", type=str)
... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/README.md | ## Run scripts for benchmarking on Draco
These scripts run benchmarks from
[`dask-cuda`](https://github.com/rapidsai/dask-cuda) in a multi-node
setting. These are set up to run on Draco.
Draco is a SLURM-based system that uses pyxis and enroot for
containerisation.
These scripts assume that the containers are alread... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/job.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
source /opt/conda/etc/profile.d/conda.sh
source /opt/conda/etc/profile.d/mamba.sh
mamba activate ucx
SCHED_FILE=${SCRATCHDIR}/scheduler-${SLURM_JOBID}.json
if [[ $SLURM_PROCID == 0 ]]; then
echo "******* UCX INFORMATION... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/job.slurm | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
#SBATCH -p batch_dgx1_m2
#SBATCH -t 02:00:00
#SBATCH -A sw_rapids_testing
#SBATCH --nv-meta=ml-model.rapids-benchmarks
#SBATCH --gpus-per-node 8
#SBATCH --ntasks-per-node 1
#SBATCH --cpus-per-task 16
#SBATCH -e slurm-%x-%J.err
#... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/get-versions.py | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
import json
import subprocess
import click
def get_versions():
import cupy
import numpy
import ucp
import dask
import dask_cuda
import distributed
import cudf
import rmm
ucx_info = subprocess.check_... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/pull-images.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
DATE=$(date +%Y%m%d)
DOCKER_HOST=gitlab-master.nvidia.com
REPO=lmitchell/docker
OUTPUT_DIR=$(readlink -f ~/workdir/enroot-images)
for ucx_version in v1.12.x v1.13.x v1.14.x master; do
TAG=${DOCKER_HOST}\#${REPO}:ucx-py-${ucx... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/build-ucx.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -ex
UCX_VERSION_TAG=${1:-"v1.13.0"}
CONDA_HOME=${2:-"/opt/conda"}
CONDA_ENV=${3:-"ucx"}
CUDA_HOME=${4:-"/usr/local/cuda"}
# Send any remaining arguments to configure
CONFIGURE_ARGS=${@:5}
source ${CONDA_HOME}/etc/profile.d... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/environment.yml | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
channels:
- rapidsai-nightly
- dask/label/dev
- numba
- conda-forge
- nvidia
dependencies:
- dask
- distributed
- cudf
- dask-cudf
- cupy
- rmm
- dask-cuda
- dask-cudf
- pynvml>=11.0.0,<11.5
- numba>=0.46
- ... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/UCXPy-rdma-core.dockerfile | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
ARG CUDA_VERSION=11.2.2
ARG DISTRIBUTION_VERSION=ubuntu20.04
FROM nvidia/cuda:${CUDA_VERSION}-devel-${DISTRIBUTION_VERSION}
# Tag to checkout from UCX repository
ARG UCX_VERSION_TAG=v1.12.x
# Where to install conda, and what to name the c... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/post-install.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -ex
CONDA_HOME=${1:-"/opt/conda"}
CONDA_ENV=${2:-"ucx"}
source ${CONDA_HOME}/etc/profile.d/conda.sh
source ${CONDA_HOME}/etc/profile.d/mamba.sh
mamba activate ${CONDA_ENV}
git clone https://github.com/gjoseph92/dask-noop.... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/build-images.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
DATE=$(date +%Y%m%d)
DOCKER_HOST=gitlab-master.nvidia.com:5005
REPO=lmitchell/docker
for ucx_version in v1.12.x v1.13.x v1.14.x master; do
TAG=${DOCKER_HOST}/${REPO}:ucx-py-${ucx_version}-${DATE}
docker build --build-ar... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco | rapidsai_public_repos/dask-cuda-benchmarks/runscripts/draco/docker/build-ucx-py.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -ex
CONDA_HOME=${1:-"/opt/conda"}
CONDA_ENV=${2:-"ucx"}
source ${CONDA_HOME}/etc/profile.d/conda.sh
source ${CONDA_HOME}/etc/profile.d/mamba.sh
mamba activate ${CONDA_ENV}
git clone https://github.com/rapidsai/ucx-py.git
... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/src | rapidsai_public_repos/dask-cuda-benchmarks/src/distributed_merge/cudf_merge.py | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
import asyncio
import sys
from enum import Enum
from itertools import chain
from typing import TYPE_CHECKING, Any, Optional, Tuple
import cupy as cp
import numpy as np
import typer
from cuda import cuda, cudart
from mpi4py import MPI
from... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/src | rapidsai_public_repos/dask-cuda-benchmarks/src/distributed_merge/README.md | ## Overview
This implements some distributed memory joins using CUDF and Pandas
built on top of MPI and UCX-Py.
The CUDF implementation uses MPI for UCX bringup (so UCX must be
CUDA-aware, but the MPI need not be), but then the core all-to-all is
performed using UCX-Py calls.
The Pandas implementation just uses MPI.... | 0 |
rapidsai_public_repos/dask-cuda-benchmarks/src | rapidsai_public_repos/dask-cuda-benchmarks/src/distributed_merge/pandas_merge.py | # Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
import sys
from typing import Any, Tuple
import numpy as np
import pandas as pd
import typer
from mpi4py import MPI
from pandas._libs import algos as libalgos
from pandas.core.util.hashing import hash_pandas_object
try:
import nvtx
ex... | 0 |
rapidsai_public_repos | rapidsai_public_repos/asvdb/README.md | # ASVDb
Python and command-line interface to a ASV "database", as described [here](https://asv.readthedocs.io/en/stable/dev.html?highlight=%24results_dir#benchmark-suite-layout-and-file-formats).
`asvdb` can be used for creating and updating an ASV database from another benchmarking tool, notebook, test code, etc., w... | 0 |
rapidsai_public_repos | rapidsai_public_repos/asvdb/CHANGELOG.md | # asvdb (unreleased)
## New Features
- ...
## Improvements
- ...
## Bug Fixes
- ...
# asvdb 0.3.3 (19 Jun 2020)
- Initial release
| 0 |
rapidsai_public_repos | rapidsai_public_repos/asvdb/build.sh | #!/bin/bash
set -e
UPLOAD_FILE=`conda build ./conda --output`
UPLOAD_FILES=$(echo ${UPLOAD_FILE}|sed -e 's/\-py[0-9][0-9]/\-py36/')
UPLOAD_FILES="${UPLOAD_FILES} $(echo ${UPLOAD_FILE}|sed -e 's/\-py[0-9][0-9]/\-py37/')"
UPLOAD_FILES="${UPLOAD_FILES} $(echo ${UPLOAD_FILE}|sed -e 's/\-py[0-9][0-9]/\-py38/')"
conda buil... | 0 |
rapidsai_public_repos | rapidsai_public_repos/asvdb/CONTRIBUTING.md | # Contributing to asvdb
If you are interested in contributing to asvdb, your contributions will fall
into three categories:
1. You want to report a bug, feature request, or documentation issue
- File an [issue](https://github.com/rapidsai/asvdb/issues/new/choose)
describing what you encountered or what you wa... | 0 |
rapidsai_public_repos | rapidsai_public_repos/asvdb/setup.py | from setuptools import setup
setup(name="asvdb",
version="0.4.2",
packages=["asvdb"],
install_requires=["botocore", "boto3"],
description='ASV "database" interface',
entry_points={
"console_scripts": [
"asvdb = asvdb.__main__:main"
]
},
)
| 0 |
rapidsai_public_repos | rapidsai_public_repos/asvdb/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
rapidsai_public_repos/asvdb | rapidsai_public_repos/asvdb/tests/test_asvdb.py | from os import path
import os
import tempfile
import json
import threading
import time
import pytest
import boto3
datasetName = "dolphins.csv"
algoRunResults = [('loadDataFile', 3.2228727098554373),
('createGraph', 3.00713360495865345),
('pagerank', 3.00899268127977848),
... | 0 |
rapidsai_public_repos/asvdb | rapidsai_public_repos/asvdb/conda/meta.yaml | {% set version = load_setup_py_data().get('version') %}
package:
name: asvdb
version: {{ version }}
source:
path: ..
build:
string: {{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
script: {{ PYTHON }} -m pip install . --no-deps
noarch: python
requirements:
host:
- python
r... | 0 |
rapidsai_public_repos/asvdb | rapidsai_public_repos/asvdb/asvdb/asvdb.py | import json
import os
from os import path
from pathlib import Path
import tempfile
import itertools
import glob
import time
import random
import stat
from urllib.parse import urlparse
from botocore import exceptions
import boto3
BenchmarkInfoKeys = set([
"machineName",
"cudaVer",
"osType",
"pythonVer"... | 0 |
rapidsai_public_repos/asvdb | rapidsai_public_repos/asvdb/asvdb/__init__.py | from .asvdb import (
ASVDb,
BenchmarkInfo,
BenchmarkResult,
BenchmarkInfoKeys,
BenchmarkResultKeys,
)
from . import utils
| 0 |
rapidsai_public_repos/asvdb | rapidsai_public_repos/asvdb/asvdb/utils.py | import subprocess
def getRepoInfo():
out = getCommandOutput("git remote -v")
repo = out.split("\n")[-1].split()[1]
branch = getCommandOutput("git rev-parse --abbrev-ref HEAD")
return (repo, branch)
def getCommandOutput(cmd):
result = subprocess.run(cmd,
stdout=subproc... | 0 |
rapidsai_public_repos/asvdb | rapidsai_public_repos/asvdb/asvdb/__main__.py | import argparse
from os import path
import asvdb
DESCRIPTION = "Examine or update an ASV 'database' row-by-row."
EPILOG = """
The database is read and each 'row' (an individual result and its context) has
the various expressions evaluated in the context of the row (see --list-keys for
all the keys that can be used i... | 0 |
rapidsai_public_repos | rapidsai_public_repos/dependency-file-generator/.pre-commit-config.yaml | repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: 'v4.3.0'
hooks:
- id: end-of-file-fixer
- id: trailing-whitespace
- id: check-builtin-literals
- id: check-executables-have-shebangs
- id: check-json
- id: check-yaml
- id: debug-statements
- id:... | 0 |
rapidsai_public_repos | rapidsai_public_repos/dependency-file-generator/package.json | {
"name": "rapids-dependency-file-generator",
"version": "1.7.1",
"description": "`rapids-dependency-file-generator` is a Python CLI tool that generates conda `environment.yaml` files and `requirements.txt` files from a single YAML file, typically named `dependencies.yaml`.",
"repository": {
"type": "git",
... | 0 |
rapidsai_public_repos | rapidsai_public_repos/dependency-file-generator/.pre-commit-hooks.yaml | - id: rapids-dependency-file-generator
name: RAPIDS dependency file generator
description: Update dependency files according to the RAPIDS dependencies spec
entry: rapids-dependency-file-generator
language: python
files: "dependencies.yaml"
pass_filenames: false
| 0 |
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