compas_cloud is the further development of compas.rpc module. It uses websocktes instead of RESTful APIs to allow bi-directional communications between various front-end programs like Rhino, GH, RhinoVault2, blender or web-based viewers that are implemented in different enviroments including CPython, IronPython and Javascript. It also allows to save certain variables to backend inside a user session to avoid overheads created by redundant data transfers.
git clone https://github.com/BlockResearchGroup/compas_cloud.git
pip install -e .python -m compas_rhino.install -p compas_cloud- Start from command line:
python -m compas_cloud.server
- The proxy will automatically start a server in background if there isn't one to connect to. If the server is started this way, it will keep operating in background and reconnect if a new proxy is create later.
One of the main purposes of compas_cloud is to allow usage of full COMPAS functionalities in more closed envinroments like IronPython. The following example shows how to use a numpy based COMPAS function through a proxy which can be run in softwares like Rhino:
basic.py
from compas_cloud import Proxy
from compas.geometry import Translation
proxy = Proxy()
transform_points_numpy = proxy.function('compas.geometry.transform_points_numpy')
# create a proxy funciton
pts = [[0,0,0], [1,0,0]]
T = Translation([100, 0, 0]).matrix
transform_points_numpy(pts, T) # call the function through proxy
print(result)
# will print: [[100.0, 0.0 ,0.0], [101.0, 0.0, 0.0]]Compas_cloud allows to cache data or function outputs at server side instead of sending them to the front-end all the time. This can vastly improve the performance for long iterative operations that involves large amount of data inputs and outputs.
from compas_cloud import Proxy
from compas.geometry import Translation
# CACHING INPUT PARAMETERS
proxy = Proxy()
transform_points_numpy = proxy.function('compas.geometry.transform_points_numpy')
# create a proxy funciton
pts = [[0,0,0], [1,0,0]]
pts_cache = proxy.cache(pts) # cache the object to server side and return its reference
print(pts_cache) # will print: {'cached': some_unique_id}
T = Translation([100, 0, 0]).matrix
result = transform_points_numpy(pts_cache, T) # call the function through proxy
print(result) # will print: [[100.0, 0.0 ,0.0], [101.0, 0.0, 0.0]]
# CACHING RETURNED DATA
transform_points_numpy = proxy.function('compas.geometry.transform_points_numpy', cache=True)
# this function will now return a cache object instead of the actual data
pts = [[0,0,0], [1,0,0]]
pts_cache = proxy.cache(pts)
print(pts_cache) # will print: {'cached': some_unique_id}
T = Translation([100, 0, 0]).matrix
result_cache = transform_points_numpy(pts_cache, T) # call the function through proxy
print(result_cache) # will print: {'cached': some_unique_id}
result = proxy.get(result_cache) # fetch the actual data of the cache object
print(result) # will print: [[100.0, 0.0 ,0.0], [101.0, 0.0, 0.0]]User can restart/check/shutdown a connected server from proxy with commands in following example: server_control.py
from compas_cloud import Proxy
import time
print("\n starting a new Proxy and by default starts a server in background")
proxy = Proxy(background=True)
time.sleep(3)
print("\n restarting the background server and open a new one in a prompt console")
proxy.background = False
proxy.restart()
time.sleep(3)
print("\n check if the proxy is healthily connected to server")
print(proxy.check())
time.sleep(3)
print("\n shut the the server and quite the program")
proxy.shutdown()
time.sleep(3)A benchmark test comparing pure python and numpy with caching to transform 10k points for 100 times:
python examples/benchmark.pyIterative plotting example with callbacks:
python examples/dr_numpy.pyUsing non-compas packages like numpy with IronPython:
run examples/example_numpy.py with Rhino
Compas_cloud.Sessions is a task-manager class that helps to execute a batch of long-lasting tasks such as FEA and DEM simulations. It creates a queue of tasks and a collection of workers to execute the tasks in parallel and save the program logs into each corresponding locations. Sessions can be run either locally or in a background server through Proxy.
python examples/sessions_local.pyfrom compas_cloud import Sessions
# define a psuedo task that will take few seconds to finish
def func(a):
import time
for i in range(a):
time.sleep(1)
print('sleeped ', i, 's')
# initiate a session object, and specify where the logs will be stored and number of workers
# if no log_path is given, all logs will be streamed to terminal and not saved
# the default worker_num is equal to the number of cpus accessible on the computer
s = Sessions(log_path=None, worker_num=4)
# add several tasks to the session using different parameters
s.add_task(func, 1)
s.add_task(func, 2)
s.add_task(func, 3)
s.add_task(func, 4)
s.add_task(func, 5)
# kick of the taks and start to listen to the events when tasks start or finish
s.start()
s.listen()You should see following logs:
{'waiting': 5, 'running': 0, 'failed': 0, 'finished': 0, 'total': 5} ________ START
{'waiting': 5, 'running': 0, 'failed': 0, 'finished': 0, 'total': 5} ________ using 4 workers
{'waiting': 5, 'running': 0, 'failed': 0, 'finished': 0, 'total': 5} ________ worker 58884 started
{'waiting': 4, 'running': 1, 'failed': 0, 'finished': 0, 'total': 5} ________ task-0: started
{'waiting': 4, 'running': 1, 'failed': 0, 'finished': 0, 'total': 5} ________ worker 58885 started
{'waiting': 4, 'running': 1, 'failed': 0, 'finished': 0, 'total': 5} ________ task-0: streaming log to temp/task-0.log
{'waiting': 3, 'running': 2, 'failed': 0, 'finished': 0, 'total': 5} ________ task-1: started
...
{'waiting': 0, 'running': 0, 'failed': 0, 'finished': 5, 'total': 5} ________ task-4: finished
{'waiting': 0, 'running': 0, 'failed': 0, 'finished': 5, 'total': 5} ________ worker 58884 terminated
{'waiting': 0, 'running': 0, 'failed': 0, 'finished': 5, 'total': 5} ________ FINISHED
python examples/sessions_remote.pyfrom compas_cloud import Proxy
# define a psuedo task that will take few seconds to finish
def func(a):
import time
for i in range(a):
time.sleep(1)
print('sleeped ', i, 's')
# initiate a Sessions object through Proxy that connects to a background server
p = Proxy()
s = p.Sessions()
# add several tasks to the session using different parameters
s.add_task(func, 1)
s.add_task(func, 2)
s.add_task(func, 3)
s.add_task(func, 4)
s.add_task(func, 5)
# kick of the taks and start to listen to the events when tasks start or finish
s.start()
s.listen()You should be able to see same logs from above example