fix files
This commit is contained in:
Sheri Gilley
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.ipynb_checkpoints
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azureml-logs
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.azureml
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.git
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outputs
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azureml-setup
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docs
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# Conda environment specification. The dependencies defined in this file will
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# be automatically provisioned for runs with userManagedDependencies=False.
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# Details about the Conda environment file format:
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# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually
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name: project_environment
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dependencies:
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# The python interpreter version.
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# Currently Azure ML only supports 3.5.2 and later.
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@@ -1,115 +0,0 @@
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# The script to run.
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script: train.py
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# The arguments to the script file.
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arguments: []
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# The name of the compute target to use for this run.
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target: local
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# Framework to execute inside. Allowed values are "Python" , "PySpark", "CNTK", "TensorFlow", and "PyTorch".
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framework: PySpark
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# Communicator for the given framework. Allowed values are "None" , "ParameterServer", "OpenMpi", and "IntelMpi".
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communicator: None
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# Automatically prepare the run environment as part of the run itself.
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autoPrepareEnvironment: true
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# Maximum allowed duration for the run.
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maxRunDurationSeconds:
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# Number of nodes to use for running job.
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nodeCount: 1
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# Environment details.
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environment:
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# Environment variables set for the run.
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environmentVariables:
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EXAMPLE_ENV_VAR: EXAMPLE_VALUE
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# Python details
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python:
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# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
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userManagedDependencies: false
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# The python interpreter path
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interpreterPath: python
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# Path to the conda dependencies file to use for this run. If a project
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# contains multiple programs with different sets of dependencies, it may be
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# convenient to manage those environments with separate files.
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condaDependenciesFile: aml_config/conda_dependencies.yml
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# Docker details
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docker:
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# Set True to perform this run inside a Docker container.
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enabled: true
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# Base image used for Docker-based runs.
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baseImage: mcr.microsoft.com/azureml/base:0.2.0
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# Set False if necessary to work around shared volume bugs.
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sharedVolumes: true
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# Run with NVidia Docker extension to support GPUs.
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gpuSupport: false
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# Extra arguments to the Docker run command.
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arguments: []
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# Image registry that contains the base image.
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baseImageRegistry:
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# DNS name or IP address of azure container registry(ACR)
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address:
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# The username for ACR
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username:
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# The password for ACR
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password:
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# Spark details
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spark:
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# List of spark repositories.
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repositories:
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- https://mmlspark.azureedge.net/maven
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packages:
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- group: com.microsoft.ml.spark
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artifact: mmlspark_2.11
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version: '0.12'
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precachePackages: true
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# Databricks details
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databricks:
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# List of maven libraries.
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mavenLibraries: []
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# List of PyPi libraries
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pypiLibraries: []
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# List of RCran libraries
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rcranLibraries: []
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# List of JAR libraries
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jarLibraries: []
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# List of Egg libraries
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eggLibraries: []
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# History details.
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history:
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# Enable history tracking -- this allows status, logs, metrics, and outputs
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# to be collected for a run.
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outputCollection: true
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# whether to take snapshots for history.
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snapshotProject: true
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# Spark configuration details.
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spark:
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configuration:
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spark.app.name: Azure ML Experiment
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spark.yarn.maxAppAttempts: 1
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# HDI details.
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hdi:
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# Yarn deploy mode. Options are cluster and client.
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yarnDeployMode: cluster
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# Tensorflow details.
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tensorflow:
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# The number of worker tasks.
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workerCount: 1
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# The number of parameter server tasks.
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parameterServerCount: 1
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# Mpi details.
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mpi:
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# When using MPI, number of processes per node.
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processCountPerNode: 1
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# data reference configuration details
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dataReferences: {}
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# Project share datastore reference.
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sourceDirectoryDataStore:
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# AmlCompute details.
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amlcompute:
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# 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
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vmSize:
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# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
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vmPriority:
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# A bool that indicates if the cluster has to be retained after job completion.
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retainCluster: false
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# Name of the cluster to be created. If not specified, runId will be used as cluster name.
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name:
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# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
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clusterMaxNodeCount: 1
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@@ -1,115 +0,0 @@
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# The script to run.
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script: train.py
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# The arguments to the script file.
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arguments: []
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# The name of the compute target to use for this run.
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target: local
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# Framework to execute inside. Allowed values are "Python" , "PySpark", "CNTK", "TensorFlow", and "PyTorch".
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framework: Python
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# Communicator for the given framework. Allowed values are "None" , "ParameterServer", "OpenMpi", and "IntelMpi".
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communicator: None
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# Automatically prepare the run environment as part of the run itself.
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autoPrepareEnvironment: true
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# Maximum allowed duration for the run.
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maxRunDurationSeconds:
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# Number of nodes to use for running job.
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nodeCount: 1
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# Environment details.
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environment:
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# Environment variables set for the run.
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environmentVariables:
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EXAMPLE_ENV_VAR: EXAMPLE_VALUE
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# Python details
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python:
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# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
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userManagedDependencies: false
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# The python interpreter path
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interpreterPath: python
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# Path to the conda dependencies file to use for this run. If a project
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# contains multiple programs with different sets of dependencies, it may be
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# convenient to manage those environments with separate files.
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condaDependenciesFile: aml_config/conda_dependencies.yml
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# Docker details
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docker:
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# Set True to perform this run inside a Docker container.
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enabled: false
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# Base image used for Docker-based runs.
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baseImage: mcr.microsoft.com/azureml/base:0.2.0
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# Set False if necessary to work around shared volume bugs.
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sharedVolumes: true
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# Run with NVidia Docker extension to support GPUs.
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gpuSupport: false
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# Extra arguments to the Docker run command.
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arguments: []
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# Image registry that contains the base image.
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baseImageRegistry:
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# DNS name or IP address of azure container registry(ACR)
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address:
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# The username for ACR
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username:
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# The password for ACR
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password:
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# Spark details
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spark:
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# List of spark repositories.
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repositories:
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- https://mmlspark.azureedge.net/maven
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packages:
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- group: com.microsoft.ml.spark
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artifact: mmlspark_2.11
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version: '0.12'
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precachePackages: true
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# Databricks details
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databricks:
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# List of maven libraries.
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mavenLibraries: []
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# List of PyPi libraries
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pypiLibraries: []
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# List of RCran libraries
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rcranLibraries: []
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# List of JAR libraries
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jarLibraries: []
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# List of Egg libraries
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eggLibraries: []
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# History details.
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history:
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# Enable history tracking -- this allows status, logs, metrics, and outputs
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# to be collected for a run.
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outputCollection: true
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# whether to take snapshots for history.
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snapshotProject: true
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# Spark configuration details.
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spark:
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configuration:
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spark.app.name: Azure ML Experiment
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spark.yarn.maxAppAttempts: 1
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# HDI details.
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hdi:
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# Yarn deploy mode. Options are cluster and client.
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yarnDeployMode: cluster
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# Tensorflow details.
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tensorflow:
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# The number of worker tasks.
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workerCount: 1
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# The number of parameter server tasks.
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parameterServerCount: 1
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# Mpi details.
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mpi:
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# When using MPI, number of processes per node.
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processCountPerNode: 1
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# data reference configuration details
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dataReferences: {}
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# Project share datastore reference.
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sourceDirectoryDataStore:
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# AmlCompute details.
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amlcompute:
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# 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
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vmSize:
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# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
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vmPriority:
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# A bool that indicates if the cluster has to be retained after job completion.
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retainCluster: false
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# Name of the cluster to be created. If not specified, runId will be used as cluster name.
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name:
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# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
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clusterMaxNodeCount: 1
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@@ -1 +0,0 @@
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{"Id": "my-experiment", "Scope": "/subscriptions/65a1016d-0f67-45d2-b838-b8f373d6d52e/resourceGroups/sheri/providers/Microsoft.MachineLearningServices/workspaces/sheritestqs3/projects/my-experiment"}
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@@ -1,6 +1,6 @@
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# Code for Remote virtual machines
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compute_target_name = "sheri-linuxvm"
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compute_target_name = "sheri-linuxvm"
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#<run_dsvm>
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import azureml.core
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