minimalcluster - A Minimal Cluster Computing Framework with Python
"minimal" here means minimal dependency or platform requirements, as well as its nature of "minimum viable product". It's mainly for tackling straightforward "embarrassingly parallel" tasks using multiple commodity machines, also a good choice for experimental and learning purposes. The idea came from Eli Bendersky's blog.
minimalcluster is built only using plain Python and its standard libraries (mainly
multiprocessing). This brought a few advantages, including
- no additional installation or configuration is needed
- 100% cross-platform (you can even have Linux, MacOS, and Windows nodes within a single cluster).
This package can be used with Python 2.7+ or 3.6+. But within a cluster, you can only choose a single version of Python, either 2 or 3.
For more frameworks for parallel or cluster computing, you may also want to refer to Parallel Processing and Multiprocessing in Python.
Usage & Examples
Step 1 - Install this package
pip install minimalcluster
Step 2 - Start master node
Open your Python terminal on your machine which will be used as Master Node, and run
from minimalcluster import MasterNode your_host = '<your master node hostname or IP>' # or use '0.0.0.0' if you have high enough privilege your_port= <port to use> your_authkey = '<the authkey for your cluster>' master = MasterNode(HOST = your_host, PORT = your_port, AUTHKEY = your_authkey) master.start_master_server()
Please note the master node will join the cluster as worker node as well by default. If you prefer otherwise, you can have argument
start_master_server() to be
False. In addition, you can also remove it from the cluster by invoking
master.stop_as_worker() and join as worker node again by invoking
Step 3 - Start worker nodes
On all your Worker Nodes, run the command below in your Python terminal
from minimalcluster import WorkerNode your_host = '<your master node hostname or IP>' your_port= <port to use> your_authkey = '<the authkey for your cluster>' N_processors_to_use = <how many processors on your worker node do you want to use> worker = WorkerNode(your_host, your_port, your_authkey, nprocs = N_processors_to_use) worker.join_cluster()
Note: if your
nprocs is bigger than the number of processors on your machine, it will be changed to be the number of processors.
After the operations on the worker nodes, you can go back to your Master node and check the list of connected Worker nodes.
Step 4 - Prepare environment to share with worker nodes
We need to specify the task function (as well as its potential dependencies) and the arguments to share with worker nodes, including
environmentis simply the codes that's going to run on worker nodes. There are two ways to set up environment. The first one is to prepare a separate
.pyfile as environment file and declare all the functions you need inside, then use
master.load_envir('<path of the environment file>')to load the environment. Another way is for simple cases. You can use
master.load_envir('<your statements>', from_file = False)to load the environment, for example
master.load_envir("f = lambda x: x * 2", from_file = False).
Task Function: We need to register the task function using
master.register_target_function('<function name>'), like
master.register_target_function("f"). Please note the task function itself must be declared in the environment file or statement.
Arguments: The argument must be a list. It will be passed to the task function. Usage:
master.load_args(args). Note the elements in list
argsmust be unique.
Step 5 - Submit jobs
Now your cluster is ready. you can try the examples below in your Python terminal on your Master node.
Example 1 - Estimate value of Pi
envir_statement = ''' from random import random example_pi_estimate_throw = lambda x: 1 if (random() * 2 - 1)**2 + (random() * 2 - 1)**2 < 1 else 0 ''' master.load_envir(envir_statement, from_file = False) master.register_target_function("example_pi_estimate_throw") N = int(1e6) master.load_args(range(N)) result = master.execute() print("Pi is roughly %f" % (4.0 * sum([x2 for x1, x2 in result.items()]) / N))
Example 2 - Factorization
envir_statement = ''' # A naive factorization method. Take integer 'n', return list of factors. # Ref: https://eli.thegreenplace.net/2012/01/24/distributed-computing-in-python-with-multiprocessing def example_factorize_naive(n): if n < 2: return  factors =  p = 2 while True: if n == 1: return factors r = n % p if r == 0: factors.append(p) n = n / p elif p * p >= n: factors.append(n) return factors elif p > 2: p += 2 else: p += 1 assert False, "unreachable" ''' #Create N large numbers to factorize. def make_nums(N): nums =  for i in range(N): nums.append(nums[-1] + 2) return nums master.load_args(make_nums(5000)) master.load_envir(envir_statement, from_file = False) master.register_target_function("example_factorize_naive") result = master.execute() for x in result.items()[:10]: # if running on Python 3, use `list(result.items())` rather than `result.items()` print(x)
Example 3 - Feed multiple arguments to target function
It's possible that you need to feed multiple arguments to target function. A small trick will be needed here: you need to wrap your arguments into a tuple, then pass the tuple to the target function as a "single" argument. Within your argument function, you can "unzip" this tuple and obtain your arguments.
envir_statement = ''' f = lambda x:x+x ''' master.load_envir(envir_statement, from_file = False) master.register_target_function("f") master.load_args([(1,2), (3,4), (5, 6), (7, 8)]) result = master.execute() print(result)
Step 6 - Shutdown the cluster
You can shutdown the cluster by running