in prog

README.md

@@ -10,17 +10,32 @@ by BlockScience
 through simulation, with support for Monte Carlo methods, A/B testing and parameter sweeping. 
 
 # Getting Started
-## 1. Install cadCAD
+## 1. Installation: 
-cadCAD requires [Python 3](https://www.python.org/downloads/)  
+Requires [Python 3](https://www.python.org/downloads/) 
-cadCAD can be installed using Python’s package manager, [pip](https://pypi.org/project/cadCAD/)
-```bash
-pip install cadCAD
-```
-## 2. Learn the basics
-Check out our tutorials (available both as [Jupyter Notebooks](tutorials) and 
-[videos](https://www.youtube.com/watch?v=uJEiYHRWA9g&list=PLmWm8ksQq4YKtdRV-SoinhV6LbQMgX1we)) to familiarize yourself 
-with some system modelling concepts and cadCAD terminology. Alternatively, go straight to the 
-[documentation](documentation).
 
-## 3. Connect
+**Option A: Install Using [pip](https://pypi.org/project/cadCAD/)** 
+```bash
+pip3 install cadCAD
+```
+
+**Option B:** Build From Source
+```
+pip3 install -r requirements.txt
+python3 setup.py sdist bdist_wheel
+pip3 install dist/*.whl
+```
+
+ 
+## 2. Learn the basics
+**Tutorials:** available both as [Jupyter Notebooks](tutorials) and [videos](https://www.youtube.com/watch?v=uJEiYHRWA9g&list=PLmWm8ksQq4YKtdRV-SoinhV6LbQMgX1we) 
+
+Familiarize yourself with some system modelling concepts and cadCAD terminology.
+
+## 3. Documentation:
+* [System Model Configuration](documentation/Simulation_Configuration.md)
+* [System Simulation Execution](documentation/Simulation_Execution.md)
+* [Policy Aggregation](documentation/Policy_Aggregation.md)
+* [System Model Parameter Sweep](documentation/System_Model_Parameter_Sweep.md)
+
+## 4. Connect
 Find other cadCAD users at our [Discourse](https://community.cadcad.org/). We are a small but rapidly growing community.

cadCAD/__init__.py

@@ -1,2 +1,11 @@
 name = "cadCAD"
-configs = []
+configs = []
+
+print(r'''
+                    __________   ____ 
+  ________ __ _____/ ____/   |  / __ \
+ / ___/ __` / __  / /   / /| | / / / /
+/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ / 
+\___/\__,_/\__,_/\____/_/  |_/_____/  
+by BlockScience
+''')

cadCAD/configuration/__init__.py

@@ -3,6 +3,7 @@ from typing import Dict, Callable, List, Tuple
 from functools import reduce
 import pandas as pd
 from pandas.core.frame import DataFrame
+# import cloudpickle, pickle
 
 from cadCAD import configs
 from cadCAD.utils import key_filter
@@ -101,7 +102,10 @@ class Identity:
     def state_identity(self, k: str) -> Callable:
         return lambda var_dict, sub_step, sL, s, _input: (k, s[k])
 
+    # state_identity = cloudpickle.dumps(state_identity)
+
     def apply_identity_funcs(self, identity: Callable, df: DataFrame, cols: List[str]) -> List[DataFrame]:
+        # identity = pickle.loads(identity)
         def fillna_with_id_func(identity, df, col):
             return df[[col]].fillna(value=identity(col))
 

cadCAD/distroduce/executor/spark/jobs/__init__.py

@@ -12,7 +12,6 @@ ascii_art = r'''
 by Joshua E. Jodesty   
 '''
 
-
 def distributed_produce(
         sc: SparkContext,
         spark_run,

cadCAD/engine/__init__.py

@@ -2,6 +2,7 @@ from pprint import pprint
 from typing import Callable, Dict, List, Any, Tuple
 from pathos.multiprocessing import ProcessingPool as PPool
 from pandas.core.frame import DataFrame
+from pyspark.context import SparkContext
 
 from cadCAD.utils import flatten
 from cadCAD.configuration import Configuration, Processor
@@ -17,6 +18,7 @@ EnvProcessesType = Dict[str, Callable]
 class ExecutionMode:
     single_proc = 'single_proc'
     multi_proc = 'multi_proc'
+    dist_proc = 'dist_proc'
 
 
 def single_proc_exec(
@@ -52,7 +54,7 @@ def parallelize_simulations(
         userIDs,
         sessionIDs,
         simulationIDs,
-        runIDs: List[int],
+        runIDs: List[int]
     ):
     params = list(zip(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, Ns,
                       userIDs, sessionIDs, simulationIDs, runIDs))
@@ -60,6 +62,38 @@ def parallelize_simulations(
         results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5], t[6], t[7], t[8], t[9], t[10]), params)
     return results
 
+def distributed_simulations(
+        sc: SparkContext,
+        simulation_execs: List[Callable],
+        var_dict_list: List[VarDictType],
+        states_lists: List[StatesListsType],
+        configs_structs: List[ConfigsType],
+        env_processes_list: List[EnvProcessesType],
+        Ts: List[range],
+        Ns: List[int],
+        userIDs,
+        sessionIDs,
+        simulationIDs,
+        runIDs: List[int]
+    ):
+    params = list(zip(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, Ns,
+                      userIDs, sessionIDs, simulationIDs, runIDs))
+    pprint(runIDs)
+    with PPool(len(configs_structs)) as p:
+        results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5], t[6], t[7], t[8], t[9], t[10]), params)
+    return results
+
+    # _pickle.PicklingError: Can't pickle <function <lambda> at 0x1115149e0>: attribute lookup <lambda> on
+    # simulations.regression_tests.config1 failed
+    # simulation_execs, env_processes_list
+    # Configuration Layer: configs_structs
+    # AttributeError: Can't pickle local object 'Identity.state_identity.<locals>.<lambda>'
+    # pprint(configs_structs)
+    # sc.parallelize([configs_structs])
+    # exit()
+    # result = sc.parallelize(params) \
+    #             .map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5], t[6], t[7], t[8], t[9], t[10])) \
+    #             .collect()
 
 class ExecutionContext:
     def __init__(self, context: str = ExecutionMode.multi_proc) -> None:
@@ -70,10 +104,13 @@ class ExecutionContext:
             self.method = single_proc_exec
         elif context == 'multi_proc':
             self.method = parallelize_simulations
+        elif context == 'dist_proc':
+            self.method = distributed_simulations
 
 
 class Executor:
-    def __init__(self, exec_context: ExecutionContext, configs: List[Configuration]) -> None:
+    def __init__(self, exec_context: ExecutionContext, configs: List[Configuration], spark_context: SparkContext = None) -> None:
+        self.sc = spark_context
         self.SimExecutor = SimExecutor
         self.exec_method = exec_context.method
         self.exec_context = exec_context.name
@@ -83,14 +120,6 @@ class Executor:
         config_proc = Processor()
         create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
 
-        print(r'''
-                            __________   ____ 
-          ________ __ _____/ ____/   |  / __ \
-         / ___/ __` / __  / /   / /| | / / / /
-        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / 
-        \___/\__,_/\__,_/\____/_/  |_/_____/  
-        by BlockScience
-        ''')
         print(f'Execution Mode: {self.exec_context + ": " + str(self.configs)}')
         print(f'Configurations: {self.configs}')
 
@@ -141,5 +170,16 @@ class Executor:
                 results.append((flatten(result), create_tensor_field(partial_state_updates, ep)))
 
             final_result = results
+        elif self.exec_context == ExecutionMode.dist_proc:
+            # if len(self.configs) > 1:
+            simulations = self.exec_method(
+                self.sc,
+                simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, Ns,
+                userIDs, sessionIDs, simulationIDs, runIDs
+            )
+            results = []
+            for result, partial_state_updates, ep in list(zip(simulations, partial_state_updates, eps)):
+                results.append((flatten(result), create_tensor_field(partial_state_updates, ep)))
+            final_result = None
 
         return final_result

requirements.txt

@@ -5,4 +5,5 @@ fn
 tabulate
 funcy
 pyspark
-kafka-python
+kafka-python
+cloudpickle

simulations/test_executions/dist_prod_exec.py

@@ -0,0 +1,31 @@
+from pprint import pprint
+
+import pandas as pd
+from tabulate import tabulate
+
+from simulations.test_executions.spark.session import spark_context as sc
+from simulations.regression_tests import config1, config2
+
+from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
+from cadCAD.utils import arrange_cols
+from cadCAD import configs
+
+exec_mode = ExecutionMode()
+
+print("Simulation Execution: Distributed Execution")
+dist_proc_ctx = ExecutionContext(context=exec_mode.dist_proc)
+run = Executor(exec_context=dist_proc_ctx, configs=configs, spark_context=sc)
+# pprint(dist_proc_ctx)
+
+# print(configs)
+i = 0
+config_names = ['config1', 'config2']
+for raw_result, tensor_field in run.execute():
+    result = arrange_cols(pd.DataFrame(raw_result), False)
+    print()
+    # print(f"Tensor Field: {config_names[i]}")
+    print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
+    print("Output:")
+    print(tabulate(result, headers='keys', tablefmt='psql'))
+    print()
+    i += 1

simulations/test_executions/spark/session/__init__.py

@@ -0,0 +1,15 @@
+from pyspark.sql import SparkSession
+from pyspark.context import SparkContext
+import os
+
+os.environ['PYSPARK_PYTHON'] = '/usr/local/bin/python3'
+os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/local/bin/python3'
+
+spark = SparkSession\
+        .builder\
+        .appName("distroduce")\
+        .getOrCreate()
+
+spark_context: SparkContext = spark.sparkContext
+print(f"Spark UI: {spark_context.uiWebUrl}")
+print()