Python Workspace.create方法代碼示例

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def create(self, name):
        name = self._get_name(name)
        region = self.ctx.config.get('cluster/region', 'eastus2')
        resource_group = self.ctx.config.get(
            'resource_group', name+'-resources')
        self.ctx.log('Creating %s' % name)

        self.ws = Workspace.create(
            name=name,
            subscription_id=self.credentials.subscription_id,
            resource_group=resource_group,
            create_resource_group=True,
            location=region,
            auth=self.credentials.get_serviceprincipal_auth())
        self._select(name)
        self.ctx.log('%s created' % name)
        return {'created': name} 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def _create_mlflow_wheel(mlflow_dir, out_dir):
    """
    Create the wheel of MLFlow by using setup.py bdist_wheel in the outdir.

    :param mlflow_dir: The absolute path to base of the MLflow Repo to create a wheel from..
    :param out_dir: The absolute path to the outdir.
                    Will be created if it does not exist.
    :return: The absolute path to the wheel.
    """
    unresolved = Path(out_dir)
    unresolved.mkdir(parents=True, exist_ok=True)
    out_path = unresolved.resolve()
    subprocess.run([sys.executable, "setup.py", "bdist_wheel", "-d", out_path],
                   cwd=mlflow_dir, check=True)
    files = list(out_path.glob("./*.whl"))
    if len(files) < 1:
        raise MlflowException("Error creating MLFlow Wheel - couldn't"
                              " find it in dir {} - found {}".format(out_path, files))
    if len(files) > 1:
        raise MlflowException(
            "Error creating MLFlow Wheel - couldn't"
            " find it in dir {} - found several wheels {}".format(out_path, files))
    return files[0] 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def _get_ws(self, name = None, create_if_not_exist = False):
        name = self._get_name(name)
        try:
            self.ws = Workspace.get(
                name, 
                subscription_id=self.credentials.subscription_id, 
                auth=self.credentials.get_serviceprincipal_auth())
        except Exception as e:
            if create_if_not_exist:
                self.create(name)
            else:
                raise e
        return self.ws 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def main(argv):  
  try:
     opts, args = getopt.getopt(argv,"hs:rg:wn:wr:",["subscription_id=","resource_group=","workspace_name=", "workspace_region="])
  except getopt.GetoptError:
     print 'aml_creation.py -s <subscription_id> -rg <resource_group> -wn <workspace_name> -wr <workspace_region>'
     sys.exit(2)
  for opt, arg in opts:
     if opt == '-h':
        print 'aml_creation.py -s <subscription_id> -rg <resource_group> -wn <workspace_name> -wr <workspace_region>'
        sys.exit()
     elif opt in ("-s", "--subscription_id"):
        subscription_id = arg
     elif opt in ("-rg", "--resource_group"):
        resource_group = arg
     elif opt in ("-wn", "--workspace_name"):
        workspace_name = arg
     elif opt in ("-wr", "--workspace_region"):
        workspace_region = arg
        
    env_path = find_dotenv()
    if env_path == "":
        Path(".env").touch()
        env_path = find_dotenv()

    ws = Workspace.create(
      name=workspace_name,
      subscription_id=subscription_id,
      resource_group=resource_group,
      location=workspace_region,
      create_resource_group=True,
      auth=get_auth(env_path),
      exist_ok=True,
    ) 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def main(argv):  
  try:
     opts, args = getopt.getopt(argv,"hs:rg:wn:wr:dsn:cn:an:ak:drg:",
      ["subscription_id=","resource_group=","workspace_name=", "workspace_region=","blob_datastore_name=","container_name=","account_name=","account_key=","datastore_rg="])
  except getopt.GetoptError:
     print 'aml_creation.py -s <subscription_id> -rg <resource_group> -wn <workspace_name> -wr <workspace_region>'
     sys.exit(2)
  for opt, arg in opts:
     if opt == '-h':
        print 'aml_creation.py -s <subscription_id> -rg <resource_group> -wn <workspace_name> -wr <workspace_region>'
        sys.exit()
     elif opt in ("-s", "--subscription_id"):
        subscription_id = arg
     elif opt in ("-rg", "--resource_group"):
        resource_group = arg
     elif opt in ("-wn", "--workspace_name"):
        workspace_name = arg
     elif opt in ("-wr", "--workspace_region"):
        workspace_region = arg
     elif opt in ("-dsn", "--blob_datastore_name"):
        workspace_region = arg
     elif opt in ("-cn", "--container_name"):
        workspace_region = arg
     elif opt in ("-an", "--account_name"):
        workspace_region = arg
     elif opt in ("-ak", "--account_key"):
        workspace_region = arg
     elif opt in ("-drg", "--datastore_rg"):
        workspace_region = arg
        
    env_path = find_dotenv()
    if env_path == "":
        Path(".env").touch()
        env_path = find_dotenv()

    ws = Workspace.create(
      name=workspace_name,
      subscription_id=subscription_id,
      resource_group=resource_group,
      location=workspace_region,
      create_resource_group=True,
      auth=get_auth(env_path),
      exist_ok=True,
    )
    blob_datastore = Datastore.register_azure_blob_container(workspace=ws, 
                                                         datastore_name=blob_datastore_name, 
                                                         container_name=container_name, 
                                                         account_name=account_name,
                                                         account_key=account_key,
                                                         resource_group=datastore_rg) 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def get_or_create_workspace(
    config_path="./.azureml", subscription_id=None, resource_group=None, workspace_name=None, workspace_region=None,
):
    """
    Method to get or create workspace.

    Args:
        config_path: optional directory to look for / store config.json file (defaults to current
            directory)
        subscription_id: Azure subscription id
        resource_group: Azure resource group to create workspace and related resources
        workspace_name: name of azure ml workspace
        workspace_region: region for workspace

    Returns:
        obj: AzureML workspace if one exists already with the name otherwise creates a new one.
    """
    config_file_path = "."

    if config_path is not None:
        config_dir, config_file_name = os.path.split(config_path)
        if config_file_name != "config.json":
            config_file_path = os.path.join(config_path, "config.json")

    try:
        # Get existing azure ml workspace
        if os.path.isfile(config_file_path):
            ws = Workspace.from_config(config_file_path, auth=get_auth())
        else:
            ws = Workspace.get(
                name=workspace_name, subscription_id=subscription_id, resource_group=resource_group, auth=get_auth(),
            )

    except ProjectSystemException:
        # This call might take a minute or two.
        print("Creating new workspace")
        ws = Workspace.create(
            name=workspace_name,
            subscription_id=subscription_id,
            resource_group=resource_group,
            create_resource_group=True,
            location=workspace_region,
            auth=get_auth(),
        )

        ws.write_config(path=config_path)
    return ws 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def get_or_create_amlcompute(
    workspace, compute_name, vm_size="", min_nodes=0, max_nodes=None, idle_seconds_before_scaledown=None, verbose=False,
):
    """
        Get or create AmlCompute as the compute target. If a cluster of the same name is found,
        attach it and rescale accordingly. Otherwise, create a new cluster.

    Args:
        workspace (Workspace): workspace
        compute_name (str): name
        vm_size (str, optional): vm size
        min_nodes (int, optional): minimum number of nodes in cluster
        max_nodes (None, optional): maximum number of nodes in cluster
        idle_seconds_before_scaledown (None, optional): how long to wait before the cluster
            autoscales down
        verbose (bool, optional): if true, print logs
    Returns:
        Compute target
    """
    try:
        if verbose:
            print("Found compute target: {}".format(compute_name))

        compute_target = ComputeTarget(workspace=workspace, name=compute_name)
        if len(compute_target.list_nodes()) < max_nodes:
            if verbose:
                print("Rescaling to {} nodes".format(max_nodes))
            compute_target.update(max_nodes=max_nodes)
            compute_target.wait_for_completion(show_output=verbose)

    except ComputeTargetException:
        if verbose:
            print("Creating new compute target: {}".format(compute_name))

        compute_config = AmlCompute.provisioning_configuration(
            vm_size=vm_size,
            min_nodes=min_nodes,
            max_nodes=max_nodes,
            idle_seconds_before_scaledown=idle_seconds_before_scaledown,
        )
        compute_target = ComputeTarget.create(workspace, compute_name, compute_config)
        compute_target.wait_for_completion(show_output=verbose)

    return compute_target 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def get_or_create_workspace(
    config_path="./.azureml",
    subscription_id=None,
    resource_group=None,
    workspace_name=None,
    workspace_region=None,
):
    """
    Method to get or create workspace.

    Args:
        config_path: optional directory to look for / store config.json file (defaults to current
            directory)
        subscription_id: Azure subscription id
        resource_group: Azure resource group to create workspace and related resources
        workspace_name: name of azure ml workspace
        workspace_region: region for workspace

    Returns:
        obj: AzureML workspace if one exists already with the name otherwise creates a new one.
    """
    config_file_path = "."

    if config_path is not None:
        config_dir, config_file_name = os.path.split(config_path)
        if config_file_name != "config.json":
            config_file_path = os.path.join(config_path, "config.json")

    try:
        # get existing azure ml workspace
        if os.path.isfile(config_file_path):
            ws = Workspace.from_config(config_file_path, auth=get_auth())
        else:
            ws = Workspace.get(
                name=workspace_name,
                subscription_id=subscription_id,
                resource_group=resource_group,
                auth=get_auth(),
            )

    except ProjectSystemException:
        # this call might take a minute or two.
        print("Creating new workspace")
        ws = Workspace.create(
            name=workspace_name,
            subscription_id=subscription_id,
            resource_group=resource_group,
            create_resource_group=True,
            location=workspace_region,
            auth=get_auth(),
        )

        ws.write_config(path=config_path)
    return ws 

# 需要導入模塊: from azureml.core import Workspace [as 別名]
# 或者: from azureml.core.Workspace import create [as 別名]
def get_or_create_amlcompute(
    workspace,
    compute_name,
    vm_size="",
    min_nodes=0,
    max_nodes=None,
    idle_seconds_before_scaledown=None,
    verbose=False,
):
    """
        Get or create AmlCompute as the compute target. If a cluster of the same name is found,
        attach it and rescale accordingly. Otherwise, create a new cluster.

    Args:
        workspace (Workspace): workspace
        compute_name (str): name
        vm_size (str, optional): vm size
        min_nodes (int, optional): minimum number of nodes in cluster
        max_nodes (None, optional): maximum number of nodes in cluster
        idle_seconds_before_scaledown (None, optional): how long to wait before the cluster
            autoscales down
        verbose (bool, optional): if true, print logs
    Returns:
        Compute target
    """
    try:
        if verbose:
            print("Found compute target: {}".format(compute_name))

        compute_target = ComputeTarget(workspace=workspace, name=compute_name)
        if len(compute_target.list_nodes()) < max_nodes:
            if verbose:
                print("Rescaling to {} nodes".format(max_nodes))
            compute_target.update(max_nodes=max_nodes)
            compute_target.wait_for_completion(show_output=verbose)

    except ComputeTargetException:
        if verbose:
            print("Creating new compute target: {}".format(compute_name))

        compute_config = AmlCompute.provisioning_configuration(
            vm_size=vm_size,
            min_nodes=min_nodes,
            max_nodes=max_nodes,
            idle_seconds_before_scaledown=idle_seconds_before_scaledown,
        )
        compute_target = ComputeTarget.create(workspace, compute_name, compute_config)
        compute_target.wait_for_completion(show_output=verbose)

    return compute_target