artifact作为结果信息展示的工具，主要服务于kubeflow notebook server和kubeflow pipelines，现结合artifact的应用作介绍。
Metadata
kubeflow artifact store最早称之为metadata store，它的定位是记录和管理kubeflow机器学习工作流中的元数据。
想要记录工程中的metadata，你需要使用专用的Metadata SDK，在python中使用pip安装即可:
pip install kubeflow-metadata
Metadata SDK简介
sdk默认配置配合kubeflow Metadata gRPC service使用，在这里将对sdk的主要api简单介绍。
首先介绍sdk中重要的基本类：
Class Store
class Store(object):
"""Metadata Store that connects to the Metadata gRPC service."""
def __init__(self,
grpc_host: str = "metadata-grpc-service.kubeflow",
grpc_port: int = 8080,
root_certificates: Optional[bytes] = None,
private_key: Optional[bytes] = None,
certificate_chain: Optional[bytes] = None):
"""
Args:
grpc_host: Required gRPC service host, e.g."metadata-grpc-service.kubeflow".
grpc_host: Required gRPC service port.
root_certificates: Optional SSL certificate for secure connection.
private_key: Optional private_key for secure connection.
certificate_chain: Optional certificate_chain for secure connection.
The optional parameters are the same as in grpc.ssl_channel_credentials.
https://grpc.github.io/grpc/python/grpc.html#grpc.ssl_channel_credentials
"""
Store类定义了连接到的metadate grpc服务信息，由于当前kubeflow版本提供了全套组件，连接默认服务名称和端口即可。
Class Workspace
class Workspace(object):
"""Groups a set of runs of pipelines, notebooks and their related artifacts
and executions.
"""
CONTEXT_TYPE_NAME = "kubeflow.org/alpha/workspace"
def __init__(self,
store: Store = None,
name: str = None,
description: Optional[str] = None,
labels: Optional[Mapping[str, str]] = None,
reuse_workspace_if_exists: Optional[bool] = True,
backend_url_prefix: Optional[str] = None):
"""
Args:
store: Required store object to connect to MLMD gRPC service.
name: Required name for the workspace.
description: Optional string for description of the workspace.
labels: Optional key/value string pairs to label the workspace.
reuse_workspace_if_exists: Optional boolean value to indicate whether a
workspace of the same name should be reused.
backend_url_prefix: Deprecated. Please use 'store' parameter.
Raises:
ValueError: If a workspace of the same name already exists and
`reuse_workspace_if_exists` is set to False.
"""
Workspace类定义一个工作空间对象，在该工作空间下可记录pipelines和notebook中的运行工作流信息、参数指标信息等。
配置参数reuse_workspace_if_exists，可以实现workspace的复用。
Class Run
class Run(object):
"""Run captures a run of pipeline or notebooks in a workspace and group
executions.
"""
def __init__(self,
workspace: Workspace = None,
name: str = None,
description: Optional[str] = None):
"""
Args:
workspace: Required workspace object to which this run belongs.
name: Required name of this run.
description: Optional description.
"""
Run类定义了一个工作空间中的运行对象，能记录在该对象下的多次任务执行。
sdk中描述工作流信息的类：
Class Exection
class Execution(object):
"""Captures a run of pipeline or notebooks in a workspace and group executions.
Execution also serves as object for logging artifacts as its input or output.
"""
EXECUTION_TYPE_NAME = "kubeflow.org/alpha/execution"
def __init__(self,
name: str = None,
workspace: Workspace = None,
run: Optional[Run] = None,
description: Optional[str] = None):
"""
Args:
name: Required name of this run.
workspace: Required workspace object where this execution belongs to.
run: Optional run object.
description: Optional description.
Creates a new execution in a workspace and run.
The execution.log_XXX() methods will attach corresponding artifacts as the
input or output of this execution.
"""
exection类可用来记录输入输出artifacts信息
Class DataSet
class DataSet(Artifact):
""" Dataset captures a data set in a machine learning workflow.
Attributes:
uri: Required uri of the data set.
name: Required name of the data set.
workspace: Optional name of the workspace.
description: Optional description of the data set.
owner: Optional owner of the data set.
version: Optional version tagged by the user.
query: Optional query string on how this data set being fetched from a data
source.
labels: Optional string key value pairs for labels.
Example:
>>> metadata.DataSet(description="an example data",
... name="mytable-dump",
... owner="[email protected]",
... uri="file://path/to/dataset",
... version="v1.0.0",
... query="SELECT * FROM mytable",
... labels={"label1","val1"}))
"""
dataset类用来记录输入输出数据集信息
Class Model
class Model(Artifact):
"""Captures a machine learning model.
Attributes:
uri: Required uri of the model artifact, e.g. "gcs://path/to/model.h5".
name: Required name of the model.
workspace: Optional name of the workspace.
description: Optional description of the model.
owner: Optional owner of the model.
model_type: Optional type of the model.
training_framework: Optional framework used to train the model.
hyperparameters: Optional map from hyper param name to its value.
labels: Optional string key value pairs for labels.
kwargs: Optional additional keyword arguments are saved as additional
properties of this model.
Example:
>>> metadata.Model(name="MNIST",
... description="model to recognize handwritten digits",
... owner="[email protected]",
... uri="gcs://my-bucket/mnist",
... model_type="neural network",
... training_framework={
... "name": "tensorflow",
... "version": "v1.0"
... },
... hyperparameters={
... "learning_rate": 0.5,
... "layers": [10, 3, 1],
... "early_stop": True
... },
... version="v0.0.1",
... labels={"mylabel": "l1"}))
"""
model类用来记录输出模型信息
Class Metrics
class Metrics(Artifact):
"""Captures an evaluation metrics of a model on a data set.
Attributes:
uri: Required uri of the metrics.
name: Required name of the metrics.
workspace: Optional name of the workspace.
description: Optional description of the metrics.
owner: Optional owner of the metrics.
data_set_id: Optional id of the data set used for evaluation.
model_id: Optional id of a evaluated model.
metrics_type: Optional type of the evaluation.
values: Optional map from metrics name to its value.
labels: Optional string key value pairs for labels.
Example:
>>> metadata.Metrics(
... name="MNIST-evaluation",
... description=
... "validating the MNIST model to recognize handwritten digits",
... owner="[email protected]",
... uri="gcs://my-bucket/mnist-eval.csv",
... data_set_id="123",
... model_id="12345",
... metrics_type=metadata.Metrics.VALIDATION,
... values={"accuracy": 0.95},
... labels={"mylabel": "l1"}))
"""
metrics类用来记录模型评估的信息
Artifacts应用
pipelines
notebook servers
artifact store在notebook servers中的使用可以参考工程例子：
http://gitlab.travelsky.com/BasicPlatform_DataProduct/kubeflow-examples/blob/master/artifact-example/notebook-servers/mnist-artifact.py
在artifact store ui界面中则可以查看工作流的环节信息：
如下面图中框住的，分别是同一个工作空间下的数据集、模型、模型预测信息。
以及artifact详细信息
