ORCO (Organized Computing) is a Python package for defining, executing and persisting computations in a straightforward fashion.
If you often find yourself in a situation where you need to:
- run many computations with various combinations of input parameters
- easily distribute the computation among many cores
- persist the results to disk in a structured way
- define dependencies between computations and pipeline them together
- avoid recomputing already executed computations when you add new inputs
- observe the status of the computations while they are running
then ORCO is the right tool for you.
ORCO prevents problems like accidentally overwriting data, computing already computed data, non-invalidating old computations when the input data are changed, overwriting data in simultaneous computations.
ORCO combines an execution engine for running computations with a database that stores their results and allows you to query them. It forces you to be very explicit in specifying how is your computation defined and what are its dependencies. This makes the code a bit verbose, but facilitates reproducibility of your experiments.
importorco@orco.builder()deftrain(n_layers, layer_size):
importtensorflowastfmodel= ...
# skipped TF code# creating a model with n_layers with layer_size# TF method, prints on stdout# ORCO captures stdout and save it with resultmodel.summary()
test_accuracy= ...
train_accuracy= ...
# save model into directory 'model', function runs in own directorymodel.save("model")
orco.attach_directory("model")
orco.attach_object("train_acc", train_accuracy)
orco.attach_object("test_acc", test_accuracy)
@orco.builder()defsummary(configurations):
importseabornassnsfromorco.ext.pyplotimportattach_figure# Defining dependencies by calling buildersresults= [train(**cfg) forcfginconfigurations]
yield# All dependencies are computed after this point# We can now read the results# Create pyplot chartvalues= [r.get_object("test_acc") forrinresults]
names= [f"#l={c['n_layers']} s={r.c['layer_size']}"forcinconfigurations]
sns.barplot(x=names, y=values)
attach_figure("chart", tight_layout=True)# DB as fileorco.start_runtime("sqlite:///my.db")
# OR# orco.start_runtime("postgresql://USER:PASS@HOST/DB")# Start computation and store result in databasejob=orco.compute(train(3, 32))
# Get resultsaccuracy=job.get_object("train_accuracy")
job.extract_tar("model", "./target_path") # model is a directory# Compute summary, this needs 3 'train' computations# train(3, 32) is skipped as it is already computedorco.compute(summary([
{"n_layers": 3, "layer_size": 32},
{"n_layers": 4, "layer_size": 64},
{"n_layers": 5, "layer_size": 64},
]))