Scaler provides a simple, efficient and reliable way to perform distributed computing using a centralized scheduler, with a stable and language agnostic protocol for client and worker communications.
import math
from scaler import Client
with Client(address="tcp://127.0.0.1:2345") as client:
# Submits 100 tasks
futures = [
client.submit(math.sqrt, i)
for i in range(0, 100)
]
# Collects the results and sums them
result = sum(future.result() for future in futures)
print(result) # 661.46Scaler is a suitable Dask replacement, offering significantly better scheduling performance for jobs with a large number of lightweight tasks while improving on load balancing, messaging and deadlocks.
- Distributed computing on multiple cores and multiple servers
- Python reference implementation, with language agnostic messaging protocol built on top of Cap'n Proto and ZeroMQ
- Graph scheduling, which supports Dask-like graph computing, optionally you can use GraphBLAS for very large graph tasks
- Automated load balancing. automatically balances load from busy workers to idle workers and tries to keep workers utilized as uniformly as possible
- Automated task recovery from faulting workers who have died
- Supports for nested tasks, tasks can themselves submit new tasks
top-like monitoring tools- GUI monitoring tool
Scaler's scheduler can be run on PyPy, which can provide a performance boost
$ pip install scaler
# or with graphblas and uvloop support
$ pip install scaler[graphblas,uvloop]Scaler operates around 3 components:
- A scheduler, responsible for routing tasks to available computing resources
- A set of workers, or cluster. Workers are independent computing units, each capable of executing a single task
- Clients running inside applications, responsible for submitting tasks to the scheduler.
A local scheduler and a local set of workers can be conveniently spawn using SchedulerClusterCombo:
from scaler import SchedulerClusterCombo
cluster = SchedulerClusterCombo(address="tcp://127.0.0.1:2345", n_workers=4)
...
cluster.shutdown()This will start a scheduler with 4 task executing workers on port 2345.
The scheduler and workers can also be started from the command line with scaler_scheduler and scaler_cluster.
First start the Scaler scheduler:
$ scaler_scheduler tcp://127.0.0.1:2345
[INFO]2023-03-19 12:16:10-0400: logging to ('/dev/stdout',)
[INFO]2023-03-19 12:16:10-0400: use event loop: 2
[INFO]2023-03-19 12:16:10-0400: Scheduler: monitor address is ipc:///tmp/127.0.0.1_2345_monitor
...Then start a set of workers (a.k.a. a Scaler cluster) that connect to the previously started scheduler:
$ scaler_cluster -n 4 tcp://127.0.0.1:2345
[INFO]2023-03-19 12:19:19-0400: logging to ('/dev/stdout',)
[INFO]2023-03-19 12:19:19-0400: ClusterProcess: starting 4 workers, heartbeat_interval_seconds=2, object_retention_seconds=3600
[INFO]2023-03-19 12:19:19-0400: Worker[0] started
[INFO]2023-03-19 12:19:19-0400: Worker[1] started
[INFO]2023-03-19 12:19:19-0400: Worker[2] started
[INFO]2023-03-19 12:19:19-0400: Worker[3] started
...Multiple Scaler clusters can be connected to the same scheduler, providing distributed computation over multiple servers.
-h lists the available options for the scheduler and the cluster executables:
$ scaler_scheduler -h
$ scaler_cluster -hKnowing the scheduler address, you can connect and submit tasks from a client in your Python code:
from scaler import Client
def square(value: int):
return value * value
with Client(address="tcp://127.0.0.1:2345") as client:
future = client.submit(square, 4)
print(future.result()) # 16Client.submit() returns a standard Python future.
Scaler also supports graph tasks, for example:
from scaler import Client
def inc(i):
return i + 1
def add(a, b):
return a + b
def minus(a, b):
return a - b
graph = {
"a": 2,
"b": 2,
"c": (inc, "a"), # c = a + 1 = 2 + 1 = 3
"d": (add, "a", "b"), # d = a + b = 2 + 2 = 4
"e": (minus, "d", "c") # e = d - c = 4 - 3 = 1
}
with Client(address="tcp://127.0.0.1:2345") as client:
result = client.get(graph, keys=["e"])
print(result) # {"e": 1}Scaler allows tasks to submit new tasks while being executed. Scaler also supports recursive task calls.
from scaler import Client
def fibonacci(clnt: Client, n: int):
if n == 0:
return 0
elif n == 1:
return 1
else:
a = clnt.submit(fibonacci, clnt, n - 1)
b = clnt.submit(fibonacci, clnt, n - 2)
return a.result() + b.result()
with Client(address="tcp://127.0.0.1:2345") as client:
result = client.submit(fibonacci, client, 8).result()
print(result) # 21For better async performance, you can install uvloop (pip install uvloop) and supply uvloop for the CLI argument
--event-loop or as a keyword argument for event_loop in Python code when initializing the scheduler.
scaler_scheduler --event-loop uvloop tcp://127.0.0.1:2345from scaler import SchedulerClusterCombo
scheduler = SchedulerClusterCombo(address="tcp://127.0.0.1:2345", event_loop="uvloop", n_workers=4)Use scaler_top to connect to the scheduler's monitor address (printed by the scheduler on startup) to see
diagnostics/metrics information about the scheduler and its workers.
$ scaler_top ipc:///tmp/127.0.0.1_2345_monitorIt will look similar to top, but provides information about the current Scaler setup:
scheduler | task_manager | scheduler_sent | scheduler_received
cpu 0.0% | unassigned 0 | ObjectResponse 24 | Heartbeat 183,109
rss 37.1 MiB | running 0 | TaskEcho 200,000 | ObjectRequest 24
| success 200,000 | Task 200,000 | Task 200,000
| failed 0 | TaskResult 200,000 | TaskResult 200,000
| canceled 0 | BalanceRequest 4 | BalanceResponse 4
--------------------------------------------------------------------------------------------------
Shortcuts: worker[n] cpu[c] rss[m] free[f] working[w] queued[q]
Total 10 worker(s)
worker agt_cpu agt_rss [cpu] rss free sent queued | object_id_to_tasks
W|Linux|15940|3c9409c0+ 0.0% 32.7m 0.0% 28.4m 1000 0 0 |
W|Linux|15946|d6450641+ 0.0% 30.7m 0.0% 28.2m 1000 0 0 |
W|Linux|15942|3ed56e89+ 0.0% 34.8m 0.0% 30.4m 1000 0 0 |
W|Linux|15944|6e7d5b99+ 0.0% 30.8m 0.0% 28.2m 1000 0 0 |
W|Linux|15945|33106447+ 0.0% 31.1m 0.0% 28.1m 1000 0 0 |
W|Linux|15937|b031ce9a+ 0.0% 31.0m 0.0% 30.3m 1000 0 0 |
W|Linux|15941|c4dcc2f3+ 0.0% 30.5m 0.0% 28.2m 1000 0 0 |
W|Linux|15939|e1ab4340+ 0.0% 31.0m 0.0% 28.1m 1000 0 0 |
W|Linux|15938|ed582770+ 0.0% 31.1m 0.0% 28.1m 1000 0 0 |
W|Linux|15943|a7fe8b5e+ 0.0% 30.7m 0.0% 28.3m 1000 0 0 |- scheduler section shows scheduler resource usage
- task_manager section shows count for each task status
- scheduler_sent section shows count for each type of messages scheduler sent
- scheduler_received section shows count for each type of messages scheduler received
- function_id_to_tasks section shows task count for each function used
- worker section shows worker details, you can use shortcuts to sort by columns, the char * on column header show which
column is sorted right now
- agt_cpu/agt_rss means cpu/memory usage of worker agent
- cpu/rss means cpu/memory usage of worker
- free means number of free task slots for this worker
- sent means how many tasks scheduler sent to the worker
- queued means how many tasks worker received and queued
scaler_ui provides a web monitoring interface for Scaler.
$ scaler_ui ipc:///tmp/127.0.0.1_2345_monitor --port 8081This will open a web server on port 8081.
Your contributions are at the core of making this a true open source project. Any contributions you make are greatly appreciated.
We welcome you to:
- Fix typos or touch up documentation
- Share your opinions on existing issues
- Help expand and improve our library by opening a new issue
Please review our community contribution guidelines and functional contribution guidelines to get started đź‘Ť.
We are committed to making open source an enjoyable and respectful experience for our community. See
CODE_OF_CONDUCT for more information.
This project is distributed under the Apache-2.0 License. See
LICENSE for more information.
If you have a query or require support with this project, raise an issue. Otherwise, reach out to [email protected].