create ml workspace
This commit is contained in:
Dennis Eikelenboom
Родитель
497733067a
Коммит
36206a523d
|
|
@ -0,0 +1 @@
|
|||
{"Id": null, "Scope": "/subscriptions/cf4e1704-b4bc-4554-bcd7-309394f2ee56/resourceGroups/azuremlworkshoprgp/providers/Microsoft.MachineLearningServices/workspaces/azuremlworkshopws"}
|
||||
|
|
@ -1,115 +0,0 @@
|
|||
# The script to run.
|
||||
script: train.py
|
||||
# The arguments to the script file.
|
||||
arguments: []
|
||||
# The name of the compute target to use for this run.
|
||||
target: local
|
||||
# Framework to execute inside. Allowed values are "Python" , "PySpark", "CNTK", "TensorFlow", and "PyTorch".
|
||||
framework: PySpark
|
||||
# Communicator for the given framework. Allowed values are "None" , "ParameterServer", "OpenMpi", and "IntelMpi".
|
||||
communicator: None
|
||||
# Automatically prepare the run environment as part of the run itself.
|
||||
autoPrepareEnvironment: true
|
||||
# Maximum allowed duration for the run.
|
||||
maxRunDurationSeconds:
|
||||
# Number of nodes to use for running job.
|
||||
nodeCount: 1
|
||||
# Environment details.
|
||||
environment:
|
||||
# Environment variables set for the run.
|
||||
environmentVariables:
|
||||
EXAMPLE_ENV_VAR: EXAMPLE_VALUE
|
||||
# Python details
|
||||
python:
|
||||
# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
|
||||
userManagedDependencies: false
|
||||
# The python interpreter path
|
||||
interpreterPath: python
|
||||
# Path to the conda dependencies file to use for this run. If a project
|
||||
# contains multiple programs with different sets of dependencies, it may be
|
||||
# convenient to manage those environments with separate files.
|
||||
condaDependenciesFile: ../conda_dependencies.yml
|
||||
# Docker details
|
||||
docker:
|
||||
# Set True to perform this run inside a Docker container.
|
||||
enabled: true
|
||||
# Base image used for Docker-based runs.
|
||||
baseImage: mcr.microsoft.com/azureml/base:0.2.0
|
||||
# Set False if necessary to work around shared volume bugs.
|
||||
sharedVolumes: true
|
||||
# Run with NVidia Docker extension to support GPUs.
|
||||
gpuSupport: false
|
||||
# Extra arguments to the Docker run command.
|
||||
arguments: []
|
||||
# Image registry that contains the base image.
|
||||
baseImageRegistry:
|
||||
# DNS name or IP address of azure container registry(ACR)
|
||||
address:
|
||||
# The username for ACR
|
||||
username:
|
||||
# The password for ACR
|
||||
password:
|
||||
# Spark details
|
||||
spark:
|
||||
# List of spark repositories.
|
||||
repositories:
|
||||
- https://mmlspark.azureedge.net/maven
|
||||
packages:
|
||||
- group: com.microsoft.ml.spark
|
||||
artifact: mmlspark_2.11
|
||||
version: '0.12'
|
||||
precachePackages: true
|
||||
# Databricks details
|
||||
databricks:
|
||||
# List of maven libraries.
|
||||
mavenLibraries: []
|
||||
# List of PyPi libraries
|
||||
pypiLibraries: []
|
||||
# List of RCran libraries
|
||||
rcranLibraries: []
|
||||
# List of JAR libraries
|
||||
jarLibraries: []
|
||||
# List of Egg libraries
|
||||
eggLibraries: []
|
||||
# History details.
|
||||
history:
|
||||
# Enable history tracking -- this allows status, logs, metrics, and outputs
|
||||
# to be collected for a run.
|
||||
outputCollection: true
|
||||
# whether to take snapshots for history.
|
||||
snapshotProject: true
|
||||
# Spark configuration details.
|
||||
spark:
|
||||
configuration:
|
||||
spark.app.name: Azure ML Experiment
|
||||
spark.yarn.maxAppAttempts: 1
|
||||
# HDI details.
|
||||
hdi:
|
||||
# Yarn deploy mode. Options are cluster and client.
|
||||
yarnDeployMode: cluster
|
||||
# Tensorflow details.
|
||||
tensorflow:
|
||||
# The number of worker tasks.
|
||||
workerCount: 1
|
||||
# The number of parameter server tasks.
|
||||
parameterServerCount: 1
|
||||
# Mpi details.
|
||||
mpi:
|
||||
# When using MPI, number of processes per node.
|
||||
processCountPerNode: 1
|
||||
# data reference configuration details
|
||||
dataReferences: {}
|
||||
# Project share datastore reference.
|
||||
sourceDirectoryDataStore:
|
||||
# AmlCompute details.
|
||||
amlcompute:
|
||||
# VM size of the Cluster to be created.Allowed values are Azure vm sizes.The list of vm sizes is available in 'https://docs.microsoft.com/en-us/azure/cloud-services/cloud-services-sizes-specs
|
||||
vmSize:
|
||||
# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
|
||||
vmPriority:
|
||||
# A bool that indicates if the cluster has to be retained after job completion.
|
||||
retainCluster: false
|
||||
# Name of the cluster to be created. If not specified, runId will be used as cluster name.
|
||||
name:
|
||||
# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
|
||||
clusterMaxNodeCount: 1
|
||||
|
|
@ -1,126 +0,0 @@
|
|||
# The script to run.
|
||||
script: train.py
|
||||
# The arguments to the script file.
|
||||
arguments: []
|
||||
# The name of the compute target to use for this run.
|
||||
target: dsvmcluster
|
||||
# Framework to execute inside. Allowed values are "Python" , "PySpark", "CNTK", "TensorFlow", and "PyTorch".
|
||||
framework: Python
|
||||
# Communicator for the given framework. Allowed values are "None" , "ParameterServer", "OpenMpi", and "IntelMpi".
|
||||
communicator: None
|
||||
# Automatically prepare the run environment as part of the run itself.
|
||||
autoPrepareEnvironment: true
|
||||
# Maximum allowed duration for the run.
|
||||
maxRunDurationSeconds:
|
||||
# Number of nodes to use for running job.
|
||||
nodeCount: 1
|
||||
# Environment details.
|
||||
environment:
|
||||
# Environment variables set for the run.
|
||||
environmentVariables:
|
||||
EXAMPLE_ENV_VAR: EXAMPLE_VALUE
|
||||
# Python details
|
||||
python:
|
||||
# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
|
||||
userManagedDependencies: false
|
||||
# The python interpreter path
|
||||
interpreterPath: python
|
||||
# Path to the conda dependencies file to use for this run. If a project
|
||||
# contains multiple programs with different sets of dependencies, it may be
|
||||
# convenient to manage those environments with separate files.
|
||||
condaDependenciesFile: ../conda_dependencies.yml
|
||||
# Docker details
|
||||
docker:
|
||||
# Set True to perform this run inside a Docker container.
|
||||
enabled: true
|
||||
# Base image used for Docker-based runs.
|
||||
baseImage: mcr.microsoft.com/azureml/base:0.2.0
|
||||
# Set False if necessary to work around shared volume bugs.
|
||||
sharedVolumes: true
|
||||
# Run with NVidia Docker extension to support GPUs.
|
||||
gpuSupport: false
|
||||
# Extra arguments to the Docker run command.
|
||||
arguments: []
|
||||
# Image registry that contains the base image.
|
||||
baseImageRegistry:
|
||||
# DNS name or IP address of azure container registry(ACR)
|
||||
address:
|
||||
# The username for ACR
|
||||
username:
|
||||
# The password for ACR
|
||||
password:
|
||||
# Spark details
|
||||
spark:
|
||||
# List of spark repositories.
|
||||
repositories:
|
||||
- https://mmlspark.azureedge.net/maven
|
||||
packages:
|
||||
- group: com.microsoft.ml.spark
|
||||
artifact: mmlspark_2.11
|
||||
version: '0.12'
|
||||
precachePackages: true
|
||||
# Databricks details
|
||||
databricks:
|
||||
# List of maven libraries.
|
||||
mavenLibraries: []
|
||||
# List of PyPi libraries
|
||||
pypiLibraries: []
|
||||
# List of RCran libraries
|
||||
rcranLibraries: []
|
||||
# List of JAR libraries
|
||||
jarLibraries: []
|
||||
# List of Egg libraries
|
||||
eggLibraries: []
|
||||
# History details.
|
||||
history:
|
||||
# Enable history tracking -- this allows status, logs, metrics, and outputs
|
||||
# to be collected for a run.
|
||||
outputCollection: true
|
||||
# whether to take snapshots for history.
|
||||
snapshotProject: true
|
||||
# Spark configuration details.
|
||||
spark:
|
||||
configuration:
|
||||
spark.app.name: Azure ML Experiment
|
||||
spark.yarn.maxAppAttempts: 1
|
||||
# HDI details.
|
||||
hdi:
|
||||
# Yarn deploy mode. Options are cluster and client.
|
||||
yarnDeployMode: cluster
|
||||
# Tensorflow details.
|
||||
tensorflow:
|
||||
# The number of worker tasks.
|
||||
workerCount: 1
|
||||
# The number of parameter server tasks.
|
||||
parameterServerCount: 1
|
||||
# Mpi details.
|
||||
mpi:
|
||||
# When using MPI, number of processes per node.
|
||||
processCountPerNode: 1
|
||||
# data reference configuration details
|
||||
dataReferences:
|
||||
workspaceblobstore:
|
||||
# Name of the datastore.
|
||||
dataStoreName: workspaceblobstore
|
||||
# relative path on the datastore.
|
||||
pathOnDataStore:
|
||||
# operation on the datastore, mount, download, upload
|
||||
mode: mount
|
||||
# whether to overwrite the data if existing
|
||||
overwrite: false
|
||||
# the path on the compute target.
|
||||
pathOnCompute:
|
||||
# Project share datastore reference.
|
||||
sourceDirectoryDataStore:
|
||||
# AmlCompute details.
|
||||
amlcompute:
|
||||
# VM size of the Cluster to be created.Allowed values are Azure vm sizes.The list of vm sizes is available in 'https://docs.microsoft.com/en-us/azure/cloud-services/cloud-services-sizes-specs
|
||||
vmSize:
|
||||
# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
|
||||
vmPriority:
|
||||
# A bool that indicates if the cluster has to be retained after job completion.
|
||||
retainCluster: false
|
||||
# Name of the cluster to be created. If not specified, runId will be used as cluster name.
|
||||
name:
|
||||
# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
|
||||
clusterMaxNodeCount: 1
|
||||
|
|
@ -1,126 +0,0 @@
|
|||
# The script to run.
|
||||
script: train.py
|
||||
# The arguments to the script file.
|
||||
arguments: []
|
||||
# The name of the compute target to use for this run.
|
||||
target: local
|
||||
# Framework to execute inside. Allowed values are "Python" , "PySpark", "CNTK", "TensorFlow", and "PyTorch".
|
||||
framework: Python
|
||||
# Communicator for the given framework. Allowed values are "None" , "ParameterServer", "OpenMpi", and "IntelMpi".
|
||||
communicator: None
|
||||
# Automatically prepare the run environment as part of the run itself.
|
||||
autoPrepareEnvironment: true
|
||||
# Maximum allowed duration for the run.
|
||||
maxRunDurationSeconds:
|
||||
# Number of nodes to use for running job.
|
||||
nodeCount: 1
|
||||
# Environment details.
|
||||
environment:
|
||||
# Environment variables set for the run.
|
||||
environmentVariables:
|
||||
EXAMPLE_ENV_VAR: EXAMPLE_VALUE
|
||||
# Python details
|
||||
python:
|
||||
# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
|
||||
userManagedDependencies: false
|
||||
# The python interpreter path
|
||||
interpreterPath: python
|
||||
# Path to the conda dependencies file to use for this run. If a project
|
||||
# contains multiple programs with different sets of dependencies, it may be
|
||||
# convenient to manage those environments with separate files.
|
||||
condaDependenciesFile: ../conda_dependencies.yml
|
||||
# Docker details
|
||||
docker:
|
||||
# Set True to perform this run inside a Docker container.
|
||||
enabled: false
|
||||
# Base image used for Docker-based runs.
|
||||
baseImage: mcr.microsoft.com/azureml/base:0.2.0
|
||||
# Set False if necessary to work around shared volume bugs.
|
||||
sharedVolumes: true
|
||||
# Run with NVidia Docker extension to support GPUs.
|
||||
gpuSupport: false
|
||||
# Extra arguments to the Docker run command.
|
||||
arguments: []
|
||||
# Image registry that contains the base image.
|
||||
baseImageRegistry:
|
||||
# DNS name or IP address of azure container registry(ACR)
|
||||
address:
|
||||
# The username for ACR
|
||||
username:
|
||||
# The password for ACR
|
||||
password:
|
||||
# Spark details
|
||||
spark:
|
||||
# List of spark repositories.
|
||||
repositories:
|
||||
- https://mmlspark.azureedge.net/maven
|
||||
packages:
|
||||
- group: com.microsoft.ml.spark
|
||||
artifact: mmlspark_2.11
|
||||
version: '0.12'
|
||||
precachePackages: true
|
||||
# Databricks details
|
||||
databricks:
|
||||
# List of maven libraries.
|
||||
mavenLibraries: []
|
||||
# List of PyPi libraries
|
||||
pypiLibraries: []
|
||||
# List of RCran libraries
|
||||
rcranLibraries: []
|
||||
# List of JAR libraries
|
||||
jarLibraries: []
|
||||
# List of Egg libraries
|
||||
eggLibraries: []
|
||||
# History details.
|
||||
history:
|
||||
# Enable history tracking -- this allows status, logs, metrics, and outputs
|
||||
# to be collected for a run.
|
||||
outputCollection: true
|
||||
# whether to take snapshots for history.
|
||||
snapshotProject: true
|
||||
# Spark configuration details.
|
||||
spark:
|
||||
configuration:
|
||||
spark.app.name: Azure ML Experiment
|
||||
spark.yarn.maxAppAttempts: 1
|
||||
# HDI details.
|
||||
hdi:
|
||||
# Yarn deploy mode. Options are cluster and client.
|
||||
yarnDeployMode: cluster
|
||||
# Tensorflow details.
|
||||
tensorflow:
|
||||
# The number of worker tasks.
|
||||
workerCount: 1
|
||||
# The number of parameter server tasks.
|
||||
parameterServerCount: 1
|
||||
# Mpi details.
|
||||
mpi:
|
||||
# When using MPI, number of processes per node.
|
||||
processCountPerNode: 1
|
||||
# data reference configuration details
|
||||
dataReferences:
|
||||
workspaceblobstore:
|
||||
# Name of the datastore.
|
||||
dataStoreName: workspaceblobstore
|
||||
# relative path on the datastore.
|
||||
pathOnDataStore:
|
||||
# operation on the datastore, mount, download, upload
|
||||
mode: download
|
||||
# whether to overwrite the data if existing
|
||||
overwrite: false
|
||||
# the path on the compute target.
|
||||
pathOnCompute:
|
||||
# Project share datastore reference.
|
||||
sourceDirectoryDataStore:
|
||||
# AmlCompute details.
|
||||
amlcompute:
|
||||
# VM size of the Cluster to be created.Allowed values are Azure vm sizes.The list of vm sizes is available in 'https://docs.microsoft.com/en-us/azure/cloud-services/cloud-services-sizes-specs
|
||||
vmSize:
|
||||
# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
|
||||
vmPriority:
|
||||
# A bool that indicates if the cluster has to be retained after job completion.
|
||||
retainCluster: false
|
||||
# Name of the cluster to be created. If not specified, runId will be used as cluster name.
|
||||
name:
|
||||
# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
|
||||
clusterMaxNodeCount: 1
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
import os
|
||||
from azureml.core import Workspace
|
||||
from azureml.core.authentication import AzureCliAuthentication
|
||||
|
||||
# Create the workspace using the specified parameters
|
||||
ws = Workspace.create(
|
||||
name='azuremlworkshopws',
|
||||
subscription_id='cf4e1704-b4bc-4554-bcd7-309394f2ee56',
|
||||
resource_group='azuremlworkshoprgp',
|
||||
location='westeurope',
|
||||
create_resource_group=True,
|
||||
sku='basic',
|
||||
exist_ok=True,
|
||||
auth=AzureCliAuthentication()
|
||||
)
|
||||
|
||||
print(ws.get_details())
|
||||
|
||||
# write the details of the workspace to a configuration file in the project root
|
||||
ws.write_config(
|
||||
path=os.path.join(
|
||||
os.path.dirname(os.path.realpath(__file__)),
|
||||
'../'
|
||||
)
|
||||
)
|
||||
Загрузка…
Ссылка в новой задаче