Memory Machine Cloud#

Tags: AWS, GCP, AliCloud, Integration, Advanced

MemVerge Memory Machine Cloud (MMCloud)—available on AWS, GCP, and AliCloud—empowers users to continuously optimize cloud resources during runtime, safely execute stateful tasks on spot instances, and monitor resource usage in real time. These capabilities make it an excellent fit for long-running batch workloads.

Flyte can be integrated with MMCloud, allowing you to execute Flyte tasks using MMCloud.


To install the plugin, run the following command:

pip install flytekitplugins-mmcloud

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

Configuring the backend to get MMCloud working#

The MMCloud plugin is enabled in FlytePropeller’s configuration.

Getting Started#

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

def to_str(i: int) -> str:
    return str(i)

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.


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

  • mmc_address: MMCloud OpCenter address

  • mmc_username: MMCloud OpCenter username

  • mmc_password: MMCloud OpCenter password


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.

Agent Image#

Install flytekitplugins-mmcloud in the agent image.

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

Sample Dockerfile for building an agent image:

FROM python:3.11-slim-bookworm


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

CMD pyflyte serve --port 8000