mlrun.platforms#

mlrun.platforms.VolumeMount#

alias of Mount

mlrun.platforms.auto_mount(pvc_name='', volume_mount_path='', volume_name=None)[source]#

choose the mount based on env variables and params

volume will be selected by the following order: - k8s PVC volume when both pvc_name and volume_mount_path are set - k8s PVC volume when env var is set: MLRUN_PVC_MOUNT=<pvc-name>:<mount-path> - k8s PVC volume if it's configured as the auto mount type - iguazio v3io volume when V3IO_ACCESS_KEY and V3IO_USERNAME env vars are set

mlrun.platforms.mount_configmap(configmap_name, mount_path, volume_name='configmap', items=None)[source]#

Modifier function to mount kubernetes configmap as files(s)

Parameters:
  • configmap_name -- k8s configmap name

  • mount_path -- path to mount inside the container

  • volume_name -- unique volume name

  • items -- If unspecified, each key-value pair in the Data field of the referenced Configmap will be projected into the volume as a file whose name is the key and content is the value. If specified, the listed keys will be projected into the specified paths, and unlisted keys will not be present.

mlrun.platforms.mount_hostpath(host_path, mount_path, volume_name='hostpath')[source]#

Modifier function to mount kubernetes configmap as files(s)

Parameters:
  • host_path -- host path

  • mount_path -- path to mount inside the container

  • volume_name -- unique volume name

mlrun.platforms.mount_pvc(pvc_name=None, volume_name='pipeline', volume_mount_path='/mnt/pipeline')[source]#

Modifier function to apply to a Container Op to simplify volume, volume mount addition and enable better reuse of volumes, volume claims across container ops.

Usage:

train = train_op(...)
train.apply(mount_pvc('claim-name', 'pipeline', '/mnt/pipeline'))
mlrun.platforms.mount_s3(secret_name=None, aws_access_key='', aws_secret_key='', endpoint_url=None, prefix='', aws_region=None, non_anonymous=False)[source]#

Modifier function to add s3 env vars or secrets to container

Warning: Using this function to configure AWS credentials will expose these credentials in the pod spec of the runtime created. It is recommended to use the secret_name parameter, or set the credentials as project-secrets and avoid using this function.

Parameters:
  • secret_name -- kubernetes secret name (storing the access/secret keys)

  • aws_access_key -- AWS_ACCESS_KEY_ID value. If this parameter is not specified and AWS_ACCESS_KEY_ID env. variable is defined, the value will be taken from the env. variable

  • aws_secret_key -- AWS_SECRET_ACCESS_KEY value. If this parameter is not specified and AWS_SECRET_ACCESS_KEY env. variable is defined, the value will be taken from the env. variable

  • endpoint_url -- s3 endpoint address (for non AWS s3)

  • prefix -- string prefix to add before the env var name (for working with multiple s3 data stores)

  • aws_region -- amazon region

  • non_anonymous -- force the S3 API to use non-anonymous connection, even if no credentials are provided (for authenticating externally, such as through IAM instance-roles)

mlrun.platforms.mount_secret(secret_name, mount_path, volume_name='secret', items=None)[source]#

Modifier function to mount kubernetes secret as files(s)

Parameters:
  • secret_name -- k8s secret name

  • mount_path -- path to mount inside the container

  • volume_name -- unique volume name

  • items -- If unspecified, each key-value pair in the Data field of the referenced Secret will be projected into the volume as a file whose name is the key and content is the value. If specified, the listed keys will be projected into the specified paths, and unlisted keys will not be present.

mlrun.platforms.mount_v3io(name='v3io', remote='', access_key='', user='', secret=None, volume_mounts=None)[source]#

Modifier function to apply to a Container Op to volume mount a v3io path

Parameters:
  • name -- the volume name

  • remote -- the v3io path to use for the volume. ~/ prefix will be replaced with /users/<username>/

  • access_key -- the access key used to auth against v3io. if not given V3IO_ACCESS_KEY env var will be used

  • user -- the username used to auth against v3io. if not given V3IO_USERNAME env var will be used

  • secret -- k8s secret name which would be used to get the username and access key to auth against v3io.

  • volume_mounts -- list of VolumeMount. empty volume mounts & remote will default to mount /v3io & /User.

mlrun.platforms.pprint(object, stream=None, indent=1, width=80, depth=None, *, compact=False, sort_dicts=True)[source]#

Pretty-print a Python object to a stream [default is sys.stdout].

mlrun.platforms.set_env_variables(env_vars_dict: dict[str, str] | None = None, **kwargs)[source]#

Modifier function to apply a set of environment variables to a runtime. Variables may be passed as either a dictionary of name-value pairs, or as arguments to the function. See KubeResource.apply for more information on modifiers.

Usage:

function.apply(set_env_variables({"ENV1": "value1", "ENV2": "value2"}))
or
function.apply(set_env_variables(ENV1=value1, ENV2=value2))
Parameters:
  • env_vars_dict -- dictionary of env. variables

  • kwargs -- environment variables passed as args

mlrun.platforms.sleep(seconds)#

Delay execution for a given number of seconds. The argument may be a floating point number for subsecond precision.

mlrun.platforms.v3io_cred(api='', user='', access_key='')[source]#

Modifier function to copy local v3io env vars to container

Usage:

train = train_op(...)
train.apply(use_v3io_cred())
mlrun.platforms.watch_stream(url, shard_ids: list | None = None, seek_to: str | None = None, interval=None, is_json=False, **kwargs)[source]#

watch on a v3io stream and print data every interval

example:

watch_stream('v3io:///users/admin/mystream')
Parameters:
  • url -- stream url

  • shard_ids -- range or list of shard IDs

  • seek_to -- where to start/seek ('EARLIEST', 'LATEST', 'TIME', 'SEQUENCE')

  • interval -- watch interval time in seconds, 0 to run once and return

  • is_json -- indicate the payload is json (will be deserialized)