From bedfbd649ee86dc71af45e454c38881db24bc5f1 Mon Sep 17 00:00:00 2001 From: Sheri Gilley Date: Mon, 7 Jan 2019 13:06:02 -0600 Subject: [PATCH] fix files --- .../how-to-set-up-training-targets/.amlignore | 7 -- .../aml_config/conda_dependencies.yml | 15 --- .../aml_config/docker.runconfig | 115 ------------------ .../aml_config/local.runconfig | 115 ------------------ .../aml_config/project.json | 1 - .../how-to-set-up-training-targets/dsvm.py | 2 +- 6 files changed, 1 insertion(+), 254 deletions(-) delete mode 100644 ignore/doc-qa/how-to-set-up-training-targets/.amlignore delete mode 100644 ignore/doc-qa/how-to-set-up-training-targets/aml_config/conda_dependencies.yml delete mode 100644 ignore/doc-qa/how-to-set-up-training-targets/aml_config/docker.runconfig delete mode 100644 ignore/doc-qa/how-to-set-up-training-targets/aml_config/local.runconfig delete mode 100644 ignore/doc-qa/how-to-set-up-training-targets/aml_config/project.json diff --git a/ignore/doc-qa/how-to-set-up-training-targets/.amlignore b/ignore/doc-qa/how-to-set-up-training-targets/.amlignore deleted file mode 100644 index 616345c4..00000000 --- a/ignore/doc-qa/how-to-set-up-training-targets/.amlignore +++ /dev/null @@ -1,7 +0,0 @@ -.ipynb_checkpoints -azureml-logs -.azureml -.git -outputs -azureml-setup -docs diff --git a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/conda_dependencies.yml b/ignore/doc-qa/how-to-set-up-training-targets/aml_config/conda_dependencies.yml deleted file mode 100644 index 5e49a89d..00000000 --- a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/conda_dependencies.yml +++ /dev/null @@ -1,15 +0,0 @@ -# Conda environment specification. The dependencies defined in this file will -# be automatically provisioned for runs with userManagedDependencies=False. - -# Details about the Conda environment file format: -# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually - -name: project_environment -dependencies: - # The python interpreter version. - # Currently Azure ML only supports 3.5.2 and later. -- python=3.6.2 - -- pip: - # Required packages for AzureML execution, history, and data preparation. - - azureml-defaults diff --git a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/docker.runconfig b/ignore/doc-qa/how-to-set-up-training-targets/aml_config/docker.runconfig deleted file mode 100644 index d79398c8..00000000 --- a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/docker.runconfig +++ /dev/null @@ -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: aml_config/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 diff --git a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/local.runconfig b/ignore/doc-qa/how-to-set-up-training-targets/aml_config/local.runconfig deleted file mode 100644 index ccfa6195..00000000 --- a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/local.runconfig +++ /dev/null @@ -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: 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: aml_config/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: {} -# 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 diff --git a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/project.json b/ignore/doc-qa/how-to-set-up-training-targets/aml_config/project.json deleted file mode 100644 index 2ad24da1..00000000 --- a/ignore/doc-qa/how-to-set-up-training-targets/aml_config/project.json +++ /dev/null @@ -1 +0,0 @@ -{"Id": "my-experiment", "Scope": "/subscriptions/65a1016d-0f67-45d2-b838-b8f373d6d52e/resourceGroups/sheri/providers/Microsoft.MachineLearningServices/workspaces/sheritestqs3/projects/my-experiment"} \ No newline at end of file diff --git a/ignore/doc-qa/how-to-set-up-training-targets/dsvm.py b/ignore/doc-qa/how-to-set-up-training-targets/dsvm.py index d077cdb2..9033d618 100644 --- a/ignore/doc-qa/how-to-set-up-training-targets/dsvm.py +++ b/ignore/doc-qa/how-to-set-up-training-targets/dsvm.py @@ -1,6 +1,6 @@ # Code for Remote virtual machines -compute_target_name = "sheri-linuxvm" + compute_target_name = "sheri-linuxvm" # import azureml.core