Run on Clusters¶

Basic Steps¶

Mars can be deployed on a cluster. First, you need to run

pip install 'pymars[distributed]'

on every node in the cluster. This will install dependencies needed for distributed execution on your cluster. After that, you may select a node as scheduler and another as web service, leaving other nodes as workers. The scheduler can be started with the following command:

mars-scheduler -a <scheduler_ip> -p <scheduler_port>

Web service can be started with the following command:

mars-web -a <web_ip> -p <web_port> -s <scheduler_ip>:<scheduler_port>

Workers can be started with the following command:

mars-worker -a <worker_ip> -p <worker_port> -s <scheduler_ip>:<scheduler_port>

After all Mars processes are started, you can open a Python console and run

import mars.tensor as mt
import mars.dataframe as md
from mars.session import new_session
new_session('http://<web_ip>:<web_port>').as_default()
a = mt.ones((2000, 2000), chunk_size=200)
b = mt.inner(a, a)
b.execute()  # submit tensor to cluster
df = md.DataFrame(a).sum()
df.execute()  # submit DataFrame to cluster

You can open a web browser and type http://<web_ip>:<web_port> to open Mars UI to look up resource usage of workers and execution progress of the task submitted just now.

Using Command Lines¶

When running Mars with command line, you can specify arguments to control the behavior of Mars processes. All Mars services have common arguments listed below.

Argument	Description
`-a`	Advertise address exposed to other processes in the cluster, useful when the server has multiple IP addresses, or the service is deployed inside a VM or container
`-H`	Service IP binding, `0.0.0.0` by default
`-p`	Port of the service. If absent, a randomized port will be used
`-s`	List of scheduler endpoints, separated by commas. Useful for workers and webs to spot schedulers, or when you want to run more than one schedulers
`--log-level`	Log level, can be `debug`, `info`, `warning`, `error`
`--log-format`	Log format, can be Python logging format
`--log-conf`	Python logging configuration file, `logging.conf` by default

Extra arguments for schedulers are listed below.

Argument	Description
`--nproc`	Number of processes. If absent, the value will be the available number of cores

Extra arguments for workers are listed below. Details about memory tuning can be found at the next section.

Argument	Description
`--cpu-procs`	Number of computation processes on CPUs. If absent, the value will be the available number of cores
`--net-procs`	Number of processes for network transfer. 4 by default
`--cuda-device`	Index of the CUDA device to use. If not specified, CPUs will be used only.
`--phy-mem`	Limit of physical memory, can be percentages of total memory or multiple of bytes. For instance, `4g` or `80%` are both acceptable. If absent, the size of physical memory will be used
`--cache-mem`	Size of shared memory, can be percentages of total memory or multiple of bytes. For instance, `4g` or `80%` are both acceptable. If absent, 50% of free memory will be used
`--min-mem`	Minimal free memory to start worker, can be percentages of total memory or multiple of bytes. For instance, `4g` or `80%` are both acceptable. `128m` by default
`--spill-dir`	Directories to spill to, separated by : in MacOS or Linux.
`--plasma-dir`	Directory of plasma store. When specified, the size of plasma store will not be considered in memory management.

For instance, if you want to start a Mars cluster with two schedulers, two workers and one web service, you can run commands below (memory and CPU tunings are omitted):

On Scheduler 1 (192.168.1.10):

mars-scheduler -a 192.168.1.10 -p 7001 -s 192.168.1.10:7001,192.168.1.11:7002

On Scheduler 2 (192.168.1.11):

mars-scheduler -a 192.168.1.11 -p 7002 -s 192.168.1.10:7001,192.168.1.11:7002

On Worker 1 (192.168.1.20):

mars-worker -a 192.168.1.20 -p 7003 -s 192.168.1.10:7001,192.168.1.11:7002 \
    --spill-dirs /mnt/disk2/spill:/mnt/disk3/spill

On Worker 2 (192.168.1.21):

mars-worker -a 192.168.1.21 -p 7004 -s 192.168.1.10:7001,192.168.1.11:7002 \
    --spill-dirs /mnt/disk2/spill:/mnt/disk3/spill

On the web server (192.168.1.30):

mars-web -p 7005 -s 192.168.1.10:7001,192.168.1.11:7002

Memory Tuning¶

Mars worker manages two different parts of memory. The first is private process memory and the second is shared memory between all worker processes handled by plasma_store in Apache Arrow. When Mars Worker starts, it will take 50% of free memory space by default as shared memory and the left as private process memory. What’s more, Mars provides soft and hard memory limits for memory allocations, which are 75% and 90% by default. If these configurations does not meet your need, you can configure them when Mars Worker starts. You can use --cache-mem argument to configure the size of shared memory, --phy-mem to configure total memory size, from which the soft and hard limits are computed.

For instance, by using

mars-worker -a localhost -p 9012 -s localhost:9010 --cache-mem 512m --phy-mem 90%

We limit the size of shared memory as 512MB and the worker can use up to 90% of total physical memory.

Installation Run on Kubernetes