Replace deco with standard multiprocessing pool

README.md

@@ -63,17 +63,17 @@ python ./cli.py --help
 or simply run the project with default options:
 
 ```bash
-python ./cli.py optimize-train-test
+python ./cli.py optimize
 ```
 
 If you have a standard set of configs you want to run the trader against, you can specify a config file to load configuration from. Rename config/config.ini.dist to config/config.ini and run
 
 ```bash
-python ./cli.py --from-config config/config.ini optimize-train-test
+python ./cli.py --from-config config/config.ini optimize
 ```
 
 ```bash
-python ./cli.py optimize-train-test
+python ./cli.py optimize
 ```
 
 ### Testing with vagrant
@@ -92,7 +92,7 @@ Note: With vagrant you cannot take full advantage of your GPU, so is mainly for
 If you want to run everything within a docker container, then just use:
 
 ```bash
-./run-with-docker (cpu|gpu) (yes|no) optimize-train-test
+./run-with-docker (cpu|gpu) (yes|no) optimize
 ```
 
 - cpu - start the container using CPU requirements
@@ -101,7 +101,7 @@ If you want to run everything within a docker container, then just use:
   Note: in case using yes as second argument, use
 
 ```bash
-python ./ cli.py --params-db-path "postgres://rl_trader:rl_trader@localhost" optimize-train-test
+python ./ cli.py --params-db-path "postgres://rl_trader:rl_trader@localhost" optimize
 ```
 
 The database and it's data are pesisted under `data/postgres` locally.

cli.py

@@ -1,6 +1,5 @@
 import numpy as np
-
-from deco import concurrent
+import multiprocessing
 
 from lib.RLTrader import RLTrader
 from lib.cli.RLTraderCLI import RLTraderCLI
@@ -12,27 +11,34 @@
 args = trader_cli.get_args()
 
 
-@concurrent(processes=args.parallel_jobs)
-def run_concurrent_optimize(trader: RLTrader, args):
-    trader.optimize(args.trials, args.trials, args.parallel_jobs)
+def run_concurrent_optimize():
+    trader = RLTrader(**vars(args))
+    trader.optimize(args.trials)
+
+
+def concurrent_optimize():
+    processes = []
+    for i in range(args.parallel_jobs):
+        processes.append(multiprocessing.Process(target=run_concurrent_optimize, args=()))
+
+    print(processes)
+
+    for p in processes:
+        p.start()
+
+    for p in processes:
+        p.join()
 
 
 if __name__ == '__main__':
     logger = init_logger(__name__, show_debug=args.debug)
     trader = RLTrader(**vars(args), logger=logger)
 
     if args.command == 'optimize':
-        run_concurrent_optimize(trader, args)
+        concurrent_optimize()
     elif args.command == 'train':
         trader.train(n_epochs=args.epochs)
     elif args.command == 'test':
         trader.test(model_epoch=args.model_epoch, should_render=args.no_render)
-    elif args.command == 'optimize-train-test':
-        run_concurrent_optimize(trader, args)
-        trader.train(
-            n_epochs=args.train_epochs,
-            test_trained_model=args.no_test,
-            render_trained_model=args.no_render
-        )
     elif args.command == 'update-static-data':
         download_data_async()

lib/RLTrader.py

@@ -5,9 +5,8 @@
 from os import path
 from typing import Dict
 
-from deco import concurrent
 from stable_baselines.common.base_class import BaseRLModel
-from stable_baselines.common.policies import BasePolicy, MlpPolicy
+from stable_baselines.common.policies import BasePolicy, MlpLnLstmPolicy
 from stable_baselines.common.vec_env import DummyVecEnv, SubprocVecEnv
 from stable_baselines.common import set_global_seeds
 from stable_baselines import PPO2
@@ -31,7 +30,7 @@ class RLTrader:
     data_provider = None
     study_name = None
 
-    def __init__(self, modelClass: BaseRLModel = PPO2, policyClass: BasePolicy = MlpPolicy, exchange_args: Dict = {}, **kwargs):
+    def __init__(self, modelClass: BaseRLModel = PPO2, policyClass: BasePolicy = MlpLnLstmPolicy, exchange_args: Dict = {}, **kwargs):
         self.logger = kwargs.get('logger', init_logger(__name__, show_debug=kwargs.get('show_debug', True)))
 
         self.Model = modelClass
@@ -162,7 +161,6 @@ def optimize_params(self, trial, n_prune_evals_per_trial: int = 2, n_tests_per_e
 
         return -1 * last_reward
 
-    @concurrent
     def optimize(self, n_trials: int = 100, n_parallel_jobs: int = 1, *optimize_params):
         try:
             self.optuna_study.optimize(

lib/cli/RLTraderCLI.py

@@ -44,12 +44,6 @@ def __init__(self):
 
         subparsers = self.parser.add_subparsers(help='Command', dest="command")
 
-        opt_train_test_parser = subparsers.add_parser('optimize-train-test', description='Optimize train and test')
-        opt_train_test_parser.add_argument('--trials', type=int, default=20, help='Number of trials')
-        opt_train_test_parser.add_argument('--train-epochs', type=int, default=10, help='Train for how many epochs')
-        opt_train_test_parser.add_argument('--no-render', action='store_false', help='Should render the model')
-        opt_train_test_parser.add_argument('--no-test', action='store_false', help='Should test the model')
-
         optimize_parser = subparsers.add_parser('optimize', description='Optimize model parameters')
         optimize_parser.add_argument('--trials', type=int, default=1, help='Number of trials')
 

requirements.base.txt

@@ -10,5 +10,4 @@ statsmodels==0.10.0rc2
 empyrical
 ccxt
 psycopg2
-deco
 configparser