flytekitplugins-mmcloud 1.16.21b0

MemVerge Flyte plugin

Flytekit Memory Machine Cloud Plugin

Flyte Connector plugin to allow executing Flyte tasks using MemVerge Memory Machine Cloud.

To install the plugin, run the following command:

pip install flytekitplugins-mmcloud

To get started with MMCloud, refer to the MMCloud User Guide.

Getting Started

This plugin allows executing PythonFunctionTask using MMCloud without changing any function code.

Resource (cpu and mem) requests and limits, container images, and environment variable specifications are supported.

ImageSpec may be used to define images to run tasks.

Credentials

The following secrets are required to be defined for the connector server:

• mmc_address: MMCloud OpCenter address
• mmc_username: MMCloud OpCenter username
• mmc_password: MMCloud OpCenter password

Defaults

Compute resources:

• If only requests are specified, there are no limits.
• If only limits are specified, the requests are equal to the limits.
• If neither resource requests nor limits are specified, the default requests used for job submission are cpu="1" and mem="1Gi", and there are no limits.

Example

example.py workflow example:

import pandas as pd
from flytekit import ImageSpec, Resources, task, workflow
from sklearn.datasets import load_wine
from sklearn.linear_model import LogisticRegression

from flytekitplugins.mmcloud import MMCloudConfig

image_spec = ImageSpec(packages=["scikit-learn"], registry="docker.io/memverge")


@task
def get_data() -> pd.DataFrame:
    """Get the wine dataset."""
    return load_wine(as_frame=True).frame


@task(task_config=MMCloudConfig(), container_image=image_spec)  # Task will be submitted as MMCloud job
def process_data(data: pd.DataFrame) -> pd.DataFrame:
    """Simplify the task from a 3-class to a binary classification problem."""
    return data.assign(target=lambda x: x["target"].where(x["target"] == 0, 1))


@task(
    task_config=MMCloudConfig(submit_extra="--migratePolicy [enable=true]"),
    requests=Resources(cpu="1", mem="1Gi"),
    limits=Resources(cpu="2", mem="4Gi"),
    container_image=image_spec,
    environment={"KEY": "value"},
)
def train_model(data: pd.DataFrame, hyperparameters: dict) -> LogisticRegression:
    """Train a model on the wine dataset."""
    features = data.drop("target", axis="columns")
    target = data["target"]
    return LogisticRegression(max_iter=3000, **hyperparameters).fit(features, target)


@workflow
def training_workflow(hyperparameters: dict) -> LogisticRegression:
    """Put all of the steps together into a single workflow."""
    data = get_data()
    processed_data = process_data(data=data)
    return train_model(
        data=processed_data,
        hyperparameters=hyperparameters,
    )

Connector Image

Install flytekitplugins-mmcloud in the connector image.

A float binary (obtainable via the OpCenter) is required. Copy it to the connector image PATH.

Sample Dockerfile for building an connector image:

FROM python:3.11-slim-bookworm

WORKDIR /root
ENV PYTHONPATH /root

# flytekit will autoload the connector if package is installed.
RUN pip install flytekitplugins-mmcloud
COPY float /usr/local/bin/float

CMD pyflyte serve connector --port 8000