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/mlflow/docker_environment/helm | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/values.yaml | # Default values for mlflow-tracking-server.
# This is a YAML-formatted file.
# Declare variables to be passed into your templates.
replicaCount: 1
env:
mlflowArtifactPath: ""
mlflowUser: "postgres"
mlflowPass: "mlflow"
mlflowDBName: "mlflow_db"
mlflowDBAddr: "mlf-db-postgresql"
mlflowDBPort: "5432"
imag... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/_helpers.tpl | {{/*
Expand the name of the chart.
*/}}
{{- define "mlflow-tracking-server.name" -}}
{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }}
{{- end }}
{{/*
Create a default fully qualified app name.
We truncate at 63 chars because some Kubernetes name fields are limited to this (by the DNS naming ... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/service.yaml | apiVersion: v1
kind: Service
metadata:
name: {{ include "mlflow-tracking-server.fullname" . }}
labels:
{{- include "mlflow-tracking-server.labels" . | nindent 4 }}
spec:
type: {{ .Values.service.type }}
ports:
- port: {{ .Values.service.port }}
targetPort: http
protocol: TCP
name: http... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/hpa.yaml | {{- if .Values.autoscaling.enabled }}
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: {{ include "mlflow-tracking-server.fullname" . }}
labels:
{{- include "mlflow-tracking-server.labels" . | nindent 4 }}
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/NOTES.txt | 1. Get the application URL by running these commands:
{{- if .Values.ingress.enabled }}
{{- range $host := .Values.ingress.hosts }}
{{- range .paths }}
http{{ if $.Values.ingress.tls }}s{{ end }}://{{ $host.host }}{{ . }}
{{- end }}
{{- end }}
{{- else if contains "NodePort" .Values.service.type }}
export NODE_... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/ingress.yaml | {{- if .Values.ingress.enabled -}}
{{- $fullName := include "mlflow-tracking-server.fullname" . -}}
{{- $svcPort := .Values.service.port -}}
{{- if semverCompare ">=1.14-0" .Capabilities.KubeVersion.GitVersion -}}
apiVersion: networking.k8s.io/v1beta1
{{- else -}}
apiVersion: extensions/v1beta1
{{- end }}
kind: Ingress... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/deployment.yaml | apiVersion: apps/v1
kind: Deployment
metadata:
name: {{ include "mlflow-tracking-server.fullname" . }}
labels:
{{- include "mlflow-tracking-server.labels" . | nindent 4 }}
spec:
{{- if not .Values.autoscaling.enabled }}
replicas: {{ .Values.replicaCount }}
{{- end }}
selector:
matchLabels:
{{- inc... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/helm/mlflow-tracking-server/templates/serviceaccount.yaml | {{- if .Values.serviceAccount.create -}}
apiVersion: v1
kind: ServiceAccount
metadata:
name: {{ include "mlflow-tracking-server.serviceAccountName" . }}
labels:
{{- include "mlflow-tracking-server.labels" . | nindent 4 }}
{{- with .Values.serviceAccount.annotations }}
annotations:
{{- toYaml . | nindent... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/images/network_diagram.svg | <svg version="1.1" viewBox="0.0 0.0 536.1338582677165 391.21522309711287" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><clipPath id="p.0"><path d="m0 0l536.13385 0l0 391.2152l-536.13385 0l0 -391.2152z" clip-rule="n... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src/rf_test/train.py | """Hyperparameter optimization with cuML, hyperopt, and MLFlow"""
import argparse
from functools import partial
import sys
import gcsfs
import mlflow
import mlflow.sklearn
import cuml
import cudf
from cuml.metrics.accuracy import accuracy_score
from cuml.model_selection import train_test_split
from cuml.ensemble im... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src/rf_test/test_query.py | import os
import json
import requests
host = 'localhost'
port = '56767'
headers = {
"Content-Type": "application/json",
"format": "pandas-split"
}
data = {
"columns": ["Year", "Month", "DayofMonth", "DayofWeek", "CRSDepTime", "CRSArrTime", "UniqueCarrier",
"FlightNum", "ActualElapsedTime... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src/k8s/entrypoint.sh | #!/bin/bash
# Activates the correct Anaconda environment, and runs the command passed to the container.
set -e
set -x
source activate rapids
nvidia-smi
ARGS=( "$@" )
python --version
echo "Calling: 'python ${ARGS[@]}'"
python ${ARGS[@]}
echo "Python call returned: $?"
| 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src | rapidsai_public_repos/cloud-ml-examples/mlflow/docker_environment/src/k8s/tracking_entrypoint.sh | #/bin/bash
# Launch our mlflow tracking server
set -e
set -x
mlflow server --backend-store-uri=postgresql://${MLFLOW_USER}:${MLFLOW_PASS}@${MLFLOW_DB_ADDR}:${MLFLOW_DB_PORT}/${MLFLOW_DB_NAME} --default-artifact-root=${MLFLOW_ARTIFACT_PATH} --host 0.0.0.0 --port 80
| 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/README-Databricks.md | ### Utilizing Databricks' for MLFlow Tracking and Job Training
#### Assumptions and Naming Conventions
- All shell commands are assumed to be run within the `/cloud-ml-examples/mlflow/docker_environment` directory.
- There are a number of configuration parameters that will be specific to your _environment_ and _deplo... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/README.md | ### Train and Publish Locally With MLFlow
#### Jupyter Notebook Workflow
[Jupyter Notebook](notebooks/rapids_mlflow_databricks_train_deploy.ipynb)
#### To reproduce this workflow, utilizing Databricks MLFlow tracking server, see:
- [Databricks MLFlow CLI](README-Databricks.md)
#### CLI Based Workflow
- Create a new c... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/MLproject | name: cuML RF test
conda_env: envs/conda.yaml
entry_points:
hyperopt:
parameters:
fpath: {type: str}
algo: {type: str, default: 'tpe'}
command: "python src/rf_test/train.py --fpath={fpath} --algo={algo}"
simple:
parameters:
conda_env: {type: str, def... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/cluster_definitions/training_cluster.json | {
"autoscale": {
"min_workers": 7,
"max_workers": 8
},
"spark_version": "6.6.x-gpu-ml-scala2.11",
"spark_conf": {
"spark.executor.cores": "2"
},
"aws_attributes": {
"first_on_demand": 1,
"availability": "SPOT_WITH_FALLBACK",
"zone_id": "us-west-2a"... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/envs/conda.yaml | name: mlflow
channels:
- rapidsai
- nvidia
- conda-forge
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20200225.2=he1b5a44_2
- appdirs=1.4.3=py_1
- arrow-cpp=0.17.1=py38h1234567_11_cuda
- arrow-cpp-proc=1.0.1=cuda
- asn1crypto=1.4.0=pyh9f0ad1d_0
- aws-c-commo... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/notebooks/databricks_mlflow_rapids.ipynb | import time
import subprocess
import sys
import threading
from queue import Queue, Empty
from functools import partial
import mlflow
import mlflow.sklearn
from cuml.metrics.accuracy import accuracy_score
from cuml.preprocessing.model_selection import train_test_split
from cuml.ensemble import RandomForestClassifier
... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/src/sample_server_query.sh | curl -X POST -H "Content-Type:application/json; format=pandas-split" --data '{"columns":["Year", "Month", "DayofMonth", "DayofWeek", "CRSDepTime", "CRSArrTime", "UniqueCarrier", "FlightNum", "ActualElapsedTime", "Origin" , "Dest", "Distance", "Diverted"],"data":[[1987, 10, 1, 4, 1, 556, 0, 190, 247, 202, 162, 1846, ... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/src | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/src/rf_test/train_simple.py | """Simple example integrating cuML with MLFlow"""
import argparse
import mlflow
import mlflow.sklearn
from cuml.metrics.accuracy import accuracy_score
from cuml.model_selection import train_test_split
from cuml.ensemble import RandomForestClassifier
def load_data(fpath):
"""
Simple helper function for load... | 0 |
rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/src | rapidsai_public_repos/cloud-ml-examples/mlflow/local_environment/src/rf_test/train.py | """Hyperparameter optimization with cuML, hyperopt, and MLFlow"""
import argparse
from functools import partial
import mlflow
import mlflow.sklearn
from cuml.metrics.accuracy import accuracy_score
from cuml.model_selection import train_test_split
from cuml.ensemble import RandomForestClassifier
from hyperopt import... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/README.md | ## Inferencing with Triton Inferencing Server with RAPIDS cuML FIL backend on Kubernetes
This folder contains examples to run the Triton Inferencing Server on a Kubernetes Server with a custom RAPIDS cuML FIL backend.
1. The directory [GCP](./GCP) contains examples on Google Kubernetes Engine.
2. The directory [AWS]... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/README.md | ## Deploying an autoscaling Triton service utilizing a custom plugin for the RAPIDS cuML Forest Inference Library (FIL).
#### Adapted from Dong Meng's `gke-marketplace-app` in the [triton-inference-server](https://github.com/triton-inference-server/server/tree/master/deploy/gke-marketplace-app) repository.
### Overvi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/interact_with_triton.ipynb | import os
import numpy
import subprocess
import sys
import time
import tritonclient.http as triton_http
import tritonclient.grpc as triton_grpchttp_port_cmd = "kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name==\"http2\")].port}'"
grpc_port_cmd = "kubectl -n istio-system get s... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/model_repository | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/model_repository/fil/config.pbtxt | name: "fil"
backend: "fil"
max_batch_size: 1048576
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 500 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 2 ]
}
]
instance_group [{ kind: KIND_GPU }]
dynamic_batching {
preferred_batch_size: [1, 2, 4, 8, 16, 32, 64,... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/model_repository/fil | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/model_repository/fil/1/xgboost.json | {"learner":{"attributes":{"scikit_learn":"{\"use_label_encoder\": false, \"n_estimators\": 500, \"objective\": \"binary:logistic\", \"max_depth\": 7, \"learning_rate\": null, \"verbosity\": null, \"booster\": null, \"tree_method\": \"gpu_hist\", \"gamma\": null, \"min_child_weight\": null, \"max_delta_step\": null, \"s... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/.helmignore | # Patterns to ignore when building packages.
# This supports shell glob matching, relative path matching, and
# negation (prefixed with !). Only one pattern per line.
.DS_Store
# Common VCS dirs
.git/
.gitignore
.bzr/
.bzrignore
.hg/
.hgignore
.svn/
# Common backup files
*.swp
*.bak
*.tmp
*~
# Various IDEs
.project
.id... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/Chart.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/values.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/templates/_helpers.tpl | {{/*
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/templates/service.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/templates/hpa.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/templates/istio-gateway.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/templates/deployment.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/GCP/FIL/helm/chart/triton/templates/istio-vs.yaml | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions a... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/README.md | ## Deploying a Triton service that uses a custom plugin for the RAPIDS cuML Forest Inference Library (FIL).
**NOTE:** For steps to setup a horizontally autoscalable Triton Service in EKS refer to the instructions in [Detailed_HPA_Setup](./Detailed_HPA_Setup.md).
### Overview
This example will illustrate the workflow... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/triton_inference.ipynb | import os
import numpy
import subprocess
import sys
import time
import tritonclient.http as triton_http
import tritonclient.grpc as triton_grpchttp_port_cmd = "kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=='http2')].port}'"
grpc_port_cmd = "kubectl -n istio-system get serv... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/Detailed_HPA_Setup.md |
## Deploying an autoscaling Triton service that uses a custom plugin for the RAPIDS cuML Forest Inference Library (FIL).
### Overview
This example will illustrate the workflow to deploy a [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server), with the [cuML Forest Inference Library (... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository/cuml_model/config.pbtxt | name: "cuml_model"
backend: "fil"
max_batch_size: 8192
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 32 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
instance_group [{ kind: KIND_GPU }]
parameters [
{
key: "model_type"
value: { string_value: "t... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository/xgb_model/config.pbtxt | name: "xgb_model"
backend: "fil"
max_batch_size: 8192
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 32 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
instance_group [{ kind: KIND_GPU }]
parameters [
{
key: "model_type"
value: { string_value: "xg... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository/light_model/config.pbtxt | name: "light_model"
backend: "fil"
max_batch_size: 8192
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 32 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
instance_group [{ kind: KIND_GPU }]
parameters [
{
key: "model_type"
value: { string_value: "... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository/light_model | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository/light_model/1/model.txt | tree
version=v3
num_class=1
num_tree_per_iteration=1
label_index=0
max_feature_idx=31
objective=binary sigmoid:1
feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Col... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/model_repository/sk_model/config.pbtxt | name: "sk_model"
backend: "fil"
max_batch_size: 8192
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [ 32 ]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [ 1 ]
}
]
instance_group [{ kind: KIND_GPU }]
parameters [
{
key: "model_type"
value: { string_value: "tre... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/.helmignore | # Patterns to ignore when building packages.
# This supports shell glob matching, relative path matching, and
# negation (prefixed with !). Only one pattern per line.
.DS_Store
# Common VCS dirs
.git/
.gitignore
.bzr/
.bzrignore
.hg/
.hgignore
.svn/
# Common backup files
*.swp
*.bak
*.tmp
*~
# Various IDEs
.project
.id... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/Chart.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/values.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/_helpers.tpl | {{/*
# Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of co... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/service.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/hpa.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/secrets.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/istio-gateway.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/deployment.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton | rapidsai_public_repos/cloud-ml-examples/triton/kubernetes/AWS/FIL/helm/charts/triton/templates/istio-vs.yaml | # Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditi... | 0 |
rapidsai_public_repos/cloud-ml-examples/optuna | rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/optuna_rapids.ipynb | # ## Run this cell to install optuna and dask_optuna
# Use Optuna 2.3.0 to avoid a bug with DaskStorage (jrbourbeau/dask-optuna#22)
# !pip install optuna==2.3.0 dask_optuna
# ## The plotting libraries
# !pip install plotly
# !pip install -U kaleido
# !pip install 'bokeh<2.0.0'import random
import time
from contextlib ... | 0 |
rapidsai_public_repos/cloud-ml-examples/optuna/notebooks | rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/azure-optuna/run_optuna.ipynb | # verify installation and check Azure ML SDK version
import azureml.core
print('SDK version:', azureml.core.VERSION)from azureml.core.workspace import Workspace
ws = Workspace.from_config()
print('Workspace name: ' + ws.name,
'Azure region: ' + ws.location,
'Subscription id: ' + ws.subscription_id,
... | 0 |
rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/azure-optuna | rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/azure-optuna/project_folder/train_optuna.py | import random
import time
from contextlib import contextmanager
import cudf
import cuml
import dask_cudf
import numpy as np
import optuna
import pandas as pd
import sklearn
import os
import dask
import dask_optuna
from cuml import LogisticRegression
from cuml.model_selection import train_test_split
from cuml.metrics ... | 0 |
rapidsai_public_repos/cloud-ml-examples/optuna/notebooks | rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/jupytercon_demo/RAPIDS_xfeat_Optuna-CPU.ipynb | import time
import json
import requests
import logging
import numpy as np
import mlflow
import mlflow.sklearn
from mlflow.tracking import MlflowClient
from mlflow.models.signature import infer_signature
import optuna
from optuna.integration.mlflow import MLflowCallback
from optuna.study import StudyDirection
from ... | 0 |
rapidsai_public_repos/cloud-ml-examples/optuna/notebooks | rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/jupytercon_demo/RAPIDS_xfeat_Optuna-GPU.ipynb | # !pip install mlflow
# !pip install optuna
# !pip install plotly
# !pip install kaleido
# !pip install xfeatimport time
import json
import requests
import logging
from contextlib import contextmanager
import numpy as np
import cupy
import cudf
import cuml
from cuml import LogisticRegression
from cuml.metrics import ... | 0 |
rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/jupytercon_demo | rapidsai_public_repos/cloud-ml-examples/optuna/notebooks/jupytercon_demo/conda/conda.yaml | name: xfeatOptuna
channels:
- rapidsai-nightly
- nvidia
- conda-forge
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=0_gnu
- abseil-cpp=20200225.2=he1b5a44_1
- aiohttp=3.6.2=py37h516909a_0
- appdirs=1.4.3=py_1
- arrow-cpp=0.17.1=py37h1234567_11_cuda
- arrow-cpp-proc=1.0.0=cuda
-... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/dask/README.md | # RAPIDS Hyperparameter Optimization (HPO) with Dask ML
[Dask-ML](https://ml.dask.org/) provides machine learning utilities
built on top of the scalable Dask platform. Dask already offers
[first-class integration with RAPIDS](https://rapids.ai/dask.html),
and Dask-ML is no exception.
The Dask-ML [hyperparameter searc... | 0 |
rapidsai_public_repos/cloud-ml-examples/dask | rapidsai_public_repos/cloud-ml-examples/dask/notebooks/HPO_demo.ipynb | import warnings
warnings.filterwarnings('ignore') # Reduce number of messages/warnings displayedimport time
import numpy as np
import cupy as cp
import pandas as pd
import cudf
import cuml
import rmm
import xgboost as xgb
import sklearn.model_selection as sk
import dask_ml.model_selection as dcv
from dask.distribute... | 0 |
rapidsai_public_repos/cloud-ml-examples/dask | rapidsai_public_repos/cloud-ml-examples/dask/kubernetes/Dask_cuML_Exploration.ipynb | import certifi
import cudf
import cuml
import cupy as cp
import gcsfs
import json
import numpy as np
import os
import pandas as pd
import random
import seaborn as sns
import time
import uuid
import yaml
from collections import OrderedDict
from functools import partial
from math import cos, sin, asin, sqrt, pi
from tqd... | 0 |
rapidsai_public_repos/cloud-ml-examples/dask | rapidsai_public_repos/cloud-ml-examples/dask/kubernetes/README.md | # Exploring cuML algorithms with dask-kubernetes
This guide aims to showcase a joint working example of [`cuML`](https://docs.rapids.ai/api/cuml/stable/),
[`dask-kubernetes`](https://kubernetes.dask.org/en/latest/index.html) and [Kubernetes cluster](https://kubernetes.io/).
## Prerequisite
A Kubernetes cluster capab... | 0 |
rapidsai_public_repos/cloud-ml-examples/dask | rapidsai_public_repos/cloud-ml-examples/dask/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/dask | rapidsai_public_repos/cloud-ml-examples/dask/kubernetes/Dockerfile | FROM rapidsai/rapidsai-core:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
# Install required package for notebook and cluster control
RUN mamba install -n rapids -c conda-forge --freeze-installed -y kubernetes google-cloud-sdk gcsfs seaborn dask-kubernetes
# Install gke-gcloud-auth-plugin, see https://cloud.google.com/bl... | 0 |
rapidsai_public_repos/cloud-ml-examples/dask/kubernetes | rapidsai_public_repos/cloud-ml-examples/dask/kubernetes/specs/sched-spec.yaml | apiVersion: v1
kind: Pod
metadata:
name: dask-scheduler
labels:
cluster_type: dask
dask_type: scheduler
spec:
restartPolicy: Never
containers:
- image: rapidsai/rapidsai-core:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
imagePullPolicy: IfNotPresent
env:
- name: DISABLE_JUPYTER
... | 0 |
rapidsai_public_repos/cloud-ml-examples/dask/kubernetes | rapidsai_public_repos/cloud-ml-examples/dask/kubernetes/specs/worker-spec.yaml | apiVersion: v1
kind: Pod
metadata:
labels:
cluster_type: dask
dask_type: GPU_worker
spec:
restartPolicy: Never
containers:
- image: rapidsai/rapidsai-core:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
imagePullPolicy: IfNotPresent
env:
- name: DISABLE_JUPYTER
value: "true"
-... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/databricks/README.md | # RAPIDS on Databricks
This directory contains sample notebooks for running RAPIDS on Databricks.
The `rapids_intro.ipynb` notebook has been tested with the latest RAPIDS version (0.19) by building a custom container, and contains basic examples to get started with cuDF and cuML.
The `rapids_airline_hyperopt.ipynb` ... | 0 |
rapidsai_public_repos/cloud-ml-examples/databricks | rapidsai_public_repos/cloud-ml-examples/databricks/notebooks/rapids_airline_hyperopt.ipynb | import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
import cudf
import cuml
import mlflow
import hyperopt
import numpy as np
import pandas as pd
import mlflow.sklearn
from mlflow.tracking.client import MlflowClient
from hyperopt im... | 0 |
rapidsai_public_repos/cloud-ml-examples/databricks | rapidsai_public_repos/cloud-ml-examples/databricks/notebooks/rapids_intro.ipynb | import cudf
import io, requests
# Download CSV file from GitHub
url="https://github.com/plotly/datasets/raw/master/tips.csv"
content = requests.get(url).content.decode('utf-8')
# Read CSV from memory
tips_df = cudf.read_csv(io.StringIO(content))
tips_df['tip_percentage'] = tips_df['tip']/tips_df['total_bill']*100
# ... | 0 |
rapidsai_public_repos/cloud-ml-examples/databricks | rapidsai_public_repos/cloud-ml-examples/databricks/docker/Dockerfile | ARG RAPIDS_IMAGE
FROM $RAPIDS_IMAGE as rapids
RUN conda list -n rapids --explicit > /rapids/rapids-spec.txt
FROM databricksruntime/gpu-conda:cuda11
COPY --from=rapids /rapids/rapids-spec.txt /tmp/spec.txt
RUN conda create --name rapids --file /tmp/spec.txt && \
rm -f /tmp/spec.txt
# Set an environment variable... | 0 |
rapidsai_public_repos/cloud-ml-examples/databricks | rapidsai_public_repos/cloud-ml-examples/databricks/src/rapids_install_cuml0.13_cuda10.0_ubuntu16.04.sh | #!/usr/bin/env bash
set -x
set -e
/databricks/python/bin/python -V
. /databricks/conda/etc/profile.d/conda.sh
conda activate /databricks/python
INSTALL_FILE="/opt/rapids_initialized.log"
if [[ -f "$INSTALL_FILE" ]]; then
TEST=$(cat "$INSTALL_FILE")
if (( $TEST == 1 )); then
echo "Node was previously... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/ray/README.md | # RAPIDS Hyperparameter Optimization with Ray Tune
Tune is a scalable hyperparameter optimization (HPO) framework, built
on top of the Ray framework for distributed applications. It includes
modern, scalable HPO algorithms, such as HyperBand and PBT, and it
supports a wide variety of machine learning models.
Ray can ... | 0 |
rapidsai_public_repos/cloud-ml-examples/ray | rapidsai_public_repos/cloud-ml-examples/ray/notebooks/Ray_RAPIDS_HPO.ipynb | # # Uncomment this line to install the packages
# !pip install tabulate nb_black
# !pip install -U ray
# !pip install ray[tune]
# !pip install bayesian-optimization scikit-optimize%load_ext lab_blackimport glob
import logging
import math
import multiprocessing
import os
import subprocess
import sys
import time
from dat... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/gcp/README.md | # **This guide is deprecated an no longer maintained.**
## Quick start guide
Here we will go over some common tasks, related to utilizing RAPIDS on the GCP AI Platform. Note that strings containing '[YOUR_XXX]' indicate items that you will need to supply, based on your specific resource names and environment.
### Cr... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp | rapidsai_public_repos/cloud-ml-examples/gcp/notebook_setup/README.md | # **This guide is deprecated an no longer maintained.**
## **Pack and Deploy Conda Environments for RAPIDS on Google Cloud Platform (GCP)**
This section describes the process required to:
1. Package and deploy a RAPIDS conda environment via helper script
1. Package and deploy a RAPIDS conda environment manually
1.... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp | rapidsai_public_repos/cloud-ml-examples/gcp/notebooks/container_build.ipynb | ## GCLOUD_BIN_PATH=[path to the location where 'gcloud' bin is installed]
## See: https://cloud.google.com/sdk/install
import json
import os
import subprocess
GCLOUD_BIN_PATH = "[/path/to/gcloud/location]"
GCP_PROJECT_NAME = "[YOUR PROJECT NAME]"
GCP_STORAGE_PATH = "[PATH TO GCP STORAGE LOCATION]" # Ex. gs://[path_to_... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp | rapidsai_public_repos/cloud-ml-examples/gcp/notebooks/custom_hpo.ipynb | ### Configure environmentimport json
import logging
import random
import sys
from ax import ParameterType, optimize# os
import sys, os, time, logging
# CPU DS stack
import pandas as pd
import numpy as np
import sklearn
# GPU DS stack [ rapids ]
import gcsfs
# scaling library
import dask
# data ingestion [ CPU ]
fr... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp | rapidsai_public_repos/cloud-ml-examples/gcp/docker/example_config.json | {
"trainingInput": {
"args": [
"--train",
"--do-hpo",
"--hpo-num-bins=64",
"--cloud-type=GCP",
"--compute-type=GPU",
"--data-input-path=gs://[YOUR STORAGE BUCKET]",
"--data-output-path=gs://[YOUR STORAGE BUCKET]/training_out... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp | rapidsai_public_repos/cloud-ml-examples/gcp/docker/launch_test.sh | #!/usr/bin/env bash
set -e
set -x
gcloud ai-platform jobs submit training $1 --config ./$2
| 0 |
rapidsai_public_repos/cloud-ml-examples/gcp | rapidsai_public_repos/cloud-ml-examples/gcp/docker/example_config_cpuonly.json | {
"trainingInput": {
"args": [
"--train",
"--do-hpo",
"--hpo-num-bins=64",
"--cloud-type=GCP",
"--compute-type=CPU",
"--data-input-path=gs://[YOUR STORAGE BUCKET]",
"--data-output-path=gs://[YOUR STORAGE BUCKET]/training_out... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp/docker | rapidsai_public_repos/cloud-ml-examples/gcp/docker/infrastructure/rapids_lib.py | # os
import sys, os, time, logging
# CPU DS stack
import pandas as pd
import numpy as np
import sklearn
# GPU DS stack [ rapids ]
import gcsfs
# scaling library
import dask
# data ingestion [ CPU ]
from pyarrow import orc as pyarrow_orc
# ML models
from sklearn import ensemble
import xgboost
# data set splits
fro... | 0 |
rapidsai_public_repos/cloud-ml-examples/gcp/docker | rapidsai_public_repos/cloud-ml-examples/gcp/docker/infrastructure/entrypoint.py | import argparse
import random
import os
import logging
import hypertune
import json
import sys
from ray import tune
from ray.tune import track
logger = logging.getLogger(tune.__name__)
logger.setLevel(level=logging.CRITICAL)
from rapids_lib import RapidsCloudML
default_sagemaker_paths = {
'base': '/opt/ml',
... | 0 |
rapidsai_public_repos/cloud-ml-examples | rapidsai_public_repos/cloud-ml-examples/azure/README.md | # RAPIDS on AzureML
These are a few examples to get started on Azure. We'll look at how to set up the environment locally and on Azure to run the notebooks provided.
Sections in README
1. Create an Azure Machine Learning Service Workspace
2. RAPIDS MNMG example using dask-clouprovider
3. RAPIDS Hyperparameter Optim... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebook_setup/README.md | ## **Pack and Deploy Conda Environments for RAPIDS on Microsoft Azure**
This section describes the process required to:
1. Package and deploy a RAPIDS conda environment via helper script
1. Package and deploy a RAPIDS conda environment manually
1. Initialize a RAPIDS conda environment.
1. Package the environmen... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/code/train_sklearn_RF.py | import argparse
import os
import time
#importing necessary libraries
import numpy as np
import pandas as pd
# import pyarrow
# from pyarrow import orc as pyarrow_orc
import sklearn
from sklearn.ensemble import RandomForestClassifier as sklRF
from sklearn.model_selection import train_test_split
from sklearn.metrics im... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/code/train_rapids.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/azure | rapidsai_public_repos/cloud-ml-examples/azure/code/rapids_csp_azure.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/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/Train-SKLearn.ipynb | import time
#check core SDK version
import azureml.core
print("SDK version:", azureml.core.VERSION)# data_dir = '../../data_airline_updated'from azureml.core.workspace import Workspace
# if a locally-saved configuration file for the workspace is not available, use the following to load workspace
# ws = Workspace(subs... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/Azure-MNMG-XGBoost.ipynb | # # Uncomment the following and install some libraries at the beginning.
# # If adlfs is not present, install adlfs to read from Azure data lake.
# ! pip install adlfs
# ! pip install "dask-cloudprovider[azure]" --upgradefrom dask.distributed import Client, wait, get_worker
from dask_cloudprovider.azure import AzureV... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/Train-RAPIDS.ipynb | # verify installation and check Azure ML SDK version
import azureml.core
print('SDK version:', azureml.core.VERSION)from azureml.core import Workspace
# if a locally-saved configuration file for the workspace is not available, use the following to load workspace
# ws = Workspace(subscription_id=subscription_id, resou... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/HPO-RAPIDS.ipynb | # verify installation and check Azure ML SDK version
import azureml.core
print('SDK version:', azureml.core.VERSION)from azureml.core import Dataset
airline_ds = Dataset.File.from_files("https://airlinedataset.blob.core.windows.net/airline-20m/*")
# larger dataset (10 years of airline data) is also available for mult... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/Dockerfile | FROM rapidsai/rapidsai-core:21.06-cuda11.0-base-ubuntu18.04-py3.8
RUN apt-get update && \
apt-get install -y fuse && \
source activate rapids && \
pip install azureml-mlflow && \
pip install azureml-dataprep && \
pip install dask-ml
| 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/HPO-SKLearn.ipynb | # verify installation and check Azure ML SDK version
import azureml.core
print('SDK version:', azureml.core.VERSION)from azureml.core.dataset import Dataset
airline_ds = Dataset.File.from_files('https://airlinedataset.blob.core.windows.net/airline-20m/*')
# larger dataset (10 years of airline data) is also available ... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/Azure-MNMG-RF.ipynb | # !pip install "dask-cloudprovider[azure]"
# !pip install azureml-core
# # Run the statements below one after the other in order.
# !pip install azureml-opendatasets
# !pip install --upgrade pandasimport math
from datetime import datetime
from math import asin, cos, pi, sin, sqrt
import cudf
import dask
import dask_c... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure/notebooks | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/remote-explanation/azure-gpu-shap.ipynb | # %%bash
# apt-get update && \
# apt-get install -y fuse && \
# apt-get install -y build-essential && \
# apt-get install -y python3-dev && \
# pip install azureml-core && \
# pip install azureml-interpret && \
# pip install -e git+https://github.com/interpretml/interpret-community.git#egg=interpret_community\&subdirec... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure/notebooks | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/remote-explanation/train_explain.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 ... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure/notebooks | rapidsai_public_repos/cloud-ml-examples/azure/notebooks/configs/cloud_init.yaml.j2 | #cloud-config
# Bootstrap
packages:
- apt-transport-https
- ca-certificates
- curl
- gnupg-agent
- software-properties-common
- ubuntu-drivers-common
# Enable ipv4 forwarding, required on CIS hardened machines
write_files:
- path: /etc/sysctl.d/enabled_ipv4_forwarding.conf
content: |
net.ipv4... | 0 |
rapidsai_public_repos/cloud-ml-examples/azure | rapidsai_public_repos/cloud-ml-examples/azure/kubernetes/Detailed_setup_guide.md | # [Detailed Guide to use Dask on Azure Kubernetes Service (AKS)](#anchor-start)
For all the next steps, we will be using the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli), however the same can be achieved through the [Azure Portal](https://portal.azure.com/#home).
### [Step 0: Install and... | 0 |
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