You can install Mars via pip:
pip install pymars
After installation, you can simply open a Python console and run
import mars.tensor as mt from mars.session import new_session a = mt.ones((5, 5), chunk_size=3) b = a * 4 # if there isn't a local session, # execute will create a default one first b.execute() # or create a session explicitly sess = new_session() sess.run(b) # run b
Users can start the distributed runtime of Mars on a single machine. First, install Mars distributed by run
pip install 'pymars[distributed]'
For now, local cluster mode can only run on Linux and Mac OS.
Then start a local cluster by run
import mars.tensor as mt from mars.deploy.local import new_cluster from mars.session import new_session cluster = new_cluster() # new cluster will start a session and set it as default one # execute will then run in the local cluster a = mt.random.rand(10, 10) a.dot(a.T).execute() # cluster.session is the session created cluster.session.run(a + 1) # users can also create a session explicitly # cluster.endpoint needs to be passed to new_session session2 = new_session(cluster.endpoint) session2.run(a * 2)
Run on Clusters¶
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 from mars.session import new_session sess = new_session('http://<web_ip>:<web_port>') a = mt.ones((2000, 2000), chunk_size=200) b = mt.inner(a, a) sess.run(b)
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.
||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|
||Service IP binding,
||Port of the service. If absent, a randomized port will be used|
||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, can be
||Log format, can be Python logging format|
||Python logging configuration file,
Extra arguments for schedulers are listed below.
||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.
||Number of computation processes on CPUs. If absent, the value will be the available number of cores|
||Number of processes for network transfer. 4 by default|
||Index of the CUDA device to use. If not specified, CPUs will be used only.|
||Limit of physical memory, can be percentages of total memory
or multiple of bytes. For instance,
||Size of shared memory, can be percentages of total memory or
multiple of bytes. For instance,
||Minimal free memory to start worker, can be percentages of
total memory or multiple of bytes. For instance,
||Directories to spill to, separated by : in MacOS or Linux.|
||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
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
--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.