entropy you silly nugget

main omg entropy.py

@@ -0,0 +1,362 @@
+
+import numpy as np
+
+from ai_economist import foundation
+from stable_baselines3.common.vec_env import vec_frame_stack
+from stable_baselines3.common.evaluation import evaluate_policy
+from sb3_contrib.ppo_mask import MaskablePPO
+import envs
+import wrapper
+import resources
+import pprint
+from agents import trading_agent
+from wrapper.base_econ_wrapper import BaseEconWrapper
+from wrapper.reciever_econ_wrapper import RecieverEconWrapper
+from wrapper.sb3_econ_converter import SB3EconConverter
+from tqdm import tqdm
+import components
+from stable_baselines3.common.env_checker import check_env
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env.vec_monitor import VecMonitor
+from stable_baselines3.common.vec_env.vec_normalize import VecNormalize
+from sb3_contrib import RecurrentPPO
+from envs.econ_wrapper import EconVecEnv
+from stable_baselines3.common.callbacks import BaseCallback
+import yaml
+import time
+from threading import Thread 
+
+env_config = {
+    # ===== SCENARIO CLASS =====
+    # Which Scenario class to use: the class's name in the Scenario Registry (foundation.scenarios).
+    # The environment object will be an instance of the Scenario class.
+    'scenario_name': 'econ',
+    
+    # ===== COMPONENTS =====
+    # Which components to use (specified as list of ("component_name", {component_kwargs}) tuples).
+    #   "component_name" refers to the Component class's name in the Component Registry (foundation.components)
+    #   {component_kwargs} is a dictionary of kwargs passed to the Component class
+    # The order in which components reset, step, and generate obs follows their listed order below.
+    'components': [
+        # (1) Building houses
+        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':1.5}),
+        # (2) Trading collectible resources
+        ('ContinuousDoubleAuction', {'max_num_orders': 10}),
+        # (3) Movement and resource collection
+        ('SimpleGather', {}),
+        ('ExternalMarket',{'market_demand':{
+            'Gem': 15
+        }}),
+    ],
+    
+    # ===== SCENARIO CLASS ARGUMENTS =====
+    # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment)
+    
+    'starting_agent_coin': 50,
+    'fixed_four_skill_and_loc': True,
+    
+    # ===== STANDARD ARGUMENTS ======
+    # kwargs that are used by every Scenario class (i.e. defined in BaseEnvironment)
+    'agent_composition': {"BasicMobileAgent": 20,"TradingAgent":5},        # Number of non-planner agents (must be > 1)
+    'world_size': [5, 5], # [Height, Width] of the env world
+    'episode_length': 256, # Number of timesteps per episode
+    'isoelastic_eta':0.001,
+    'allow_observation_scaling': True,
+    'dense_log_frequency': 100, 
+    'world_dense_log_frequency':1,
+    'energy_cost':0,
+    'energy_warmup_method': "auto",
+    'energy_warmup_constant': 4000,
+    
+    # In multi-action-mode, the policy selects an action for each action subspace (defined in component code).
+    # Otherwise, the policy selects only 1 action.
+    'multi_action_mode_agents': False,
+    'multi_action_mode_planner': False,
+    
+    # When flattening observations, concatenate scalar & vector observations before output.
+    # Otherwise, return observations with minimal processing.
+    'flatten_observations': False,
+    # When Flattening masks, concatenate each action subspace mask into a single array.
+    # Note: flatten_masks = True is required for masking action logits in the code below.
+    'flatten_masks': True,
+}
+
+
+eval_env_config = {
+    # ===== SCENARIO CLASS =====
+    # Which Scenario class to use: the class's name in the Scenario Registry (foundation.scenarios).
+    # The environment object will be an instance of the Scenario class.
+    'scenario_name': 'econ',
+    
+    # ===== COMPONENTS =====
+    # Which components to use (specified as list of ("component_name", {component_kwargs}) tuples).
+    #   "component_name" refers to the Component class's name in the Component Registry (foundation.components)
+    #   {component_kwargs} is a dictionary of kwargs passed to the Component class
+    # The order in which components reset, step, and generate obs follows their listed order below.
+    'components': [
+        # (1) Building houses
+        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':1.5}),
+        # (2) Trading collectible resources
+        ('ContinuousDoubleAuction', {'max_num_orders': 10}),
+        # (3) Movement and resource collection
+        ('SimpleGather', {}),
+        ('ExternalMarket',{'market_demand':{
+            'Gem': 15
+        }}),
+    ],
+    
+    # ===== SCENARIO CLASS ARGUMENTS =====
+    # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment)
+    
+    'starting_agent_coin': 50,
+    'fixed_four_skill_and_loc': True,
+    
+    # ===== STANDARD ARGUMENTS ======
+    # kwargs that are used by every Scenario class (i.e. defined in BaseEnvironment)
+    'agent_composition': {"BasicMobileAgent": 20,"TradingAgent":5},            # Number of non-planner agents (must be > 1)
+    'world_size': [1, 1], # [Height, Width] of the env world
+    'episode_length': 256, # Number of timesteps per episode
+    'allow_observation_scaling': True,
+    'isoelastic_eta':0.001,
+    'dense_log_frequency': 1, 
+    'world_dense_log_frequency':1, 
+    'energy_cost':0,
+    'energy_warmup_method': "auto",
+    'energy_warmup_constant': 4000,
+    
+    # In multi-action-mode, the policy selects an action for each action subspace (defined in component code).
+    # Otherwise, the policy selects only 1 action.
+    'multi_action_mode_agents': False,
+    'multi_action_mode_planner': False,
+    
+    # When flattening observations, concatenate scalar & vector observations before output.
+    # Otherwise, return observations with minimal processing.
+    'flatten_observations': False,
+    # When Flattening masks, concatenate each action subspace mask into a single array.
+    # Note: flatten_masks = True is required for masking action logits in the code below.
+    'flatten_masks': True,
+}
+
+num_frames=1
+
+class TensorboardCallback(BaseCallback):
+    """
+    Custom callback for plotting additional values in tensorboard.
+    """
+
+    def __init__(self,econ, verbose=0):
+        super().__init__(verbose)
+        self.econ=econ
+        self.metrics=econ.scenario_metrics()
+    def _on_step(self) -> bool:
+        # Log scalar value (here a random variable)
+        if econ.world.timestep==0:
+            prev_metrics=self.metrics
+            if self.econ.previous_episode_metrics is None:
+                self.metrics=self.econ.scenario_metrics()
+            else:
+                self.metrics=self.econ.previous_episode_metrics
+            curr_prod=self.metrics["social/productivity"]
+            trend_pord=curr_prod/np.maximum(0.0001,prev_metrics["social/productivity"])
+            self.logger.record("social/total_productivity", curr_prod)
+            self.logger.record("social/delta_productivity", trend_pord)
+        
+        return True
+
+min_at_target_basic=0.5
+min_lr_basic=5e-6
+start_lr_basic=9e-4
+
+min_at_target_trade=0.5
+min_lr_trade=5e-6
+start_lr_trade=9e-4
+
+def learning_rate_adj_basic(x) -> float:
+    diff=start_lr_basic-min_lr_basic
+    lr=min_lr_basic+x*diff
+    return lr
+
+def learning_rate_adj_trade(x) -> float:
+    diff=start_lr_trade-min_lr_trade
+    lr=min_lr_basic+x*diff
+    return lr
+
+def printMarket(market):
+    for i in range(len(market)):
+        step=market[i]
+        if len(step)>0:
+            print("=== Step {} ===".format(i))
+            for transaction in step:
+                t=transaction
+                transstring = "({}) {} -> {} | [{}/{}] {} Coins\n".format(t["commodity"],t["seller"],t["buyer"],t["ask"],t["bid"],t["price"])
+                print(transstring)
+    return ""
+
+def printBuilds(builds):
+    for i in range(len(builds)):
+        step=builds[i]
+        if len(step)>0:
+            for build in step:
+                t=build
+                transstring = "({}) Builder: {}, Skill: {}, Income {} ".format(i,t["builder"],t["build_skill"],t["income"])
+                print(transstring)
+    return ""
+def printReplay(econ,agentid):
+    worldmaps=["Stone","Wood"]
+
+    log=econ.previous_episode_dense_log
+    agent=econ.world.agents[agentid]
+  
+    agentid=str(agentid)
+    maxsetp=len(log["states"])-1
+
+    for step in range(maxsetp):
+        print()
+        print("=== Step {} ===".format(step))
+        # state
+        print("--- World ---")
+        world=log['world'][step]
+        for res in worldmaps:
+            print("{}: {}".format(res,world[res][0][0]))
+        print("--- State ---")
+        state=log['states'][step][agentid]
+       
+        pprint.pprint(state)
+        print("--- Action ---")
+        action=log["actions"][step][agentid]
+        
+
+        if action=={}:
+            print("Action: 0 -> NOOP")
+        else:
+            for k in action:
+                formats="Action:  {}({})".format(k,action[k])
+                print(formats)
+        print("--- Reward ---")
+        reward=log["rewards"][step][agentid]
+        print("Reward: {}".format(reward))
+
+#Setup Env Objects
+econ=foundation.make_env_instance(**env_config)
+
+market=econ.get_component("ContinuousDoubleAuction")
+action=market.get_n_actions("TradingAgent")
+baseEconWrapper=BaseEconWrapper(econ)
+baseEconWrapper.run()
+time.sleep(0.5)
+mobileRecieverEconWrapper=RecieverEconWrapper(base_econ=baseEconWrapper,agent_classname="BasicMobileAgent")
+tradeRecieverEconWrapper=RecieverEconWrapper(base_econ=baseEconWrapper,agent_classname="TradingAgent")
+sb3_traderConverter=SB3EconConverter(tradeRecieverEconWrapper,econ,"TradingAgent",True)
+sb3Converter=SB3EconConverter(mobileRecieverEconWrapper,econ,"BasicMobileAgent",True)
+# attach sb3 wrappers
+
+monenv=VecMonitor(venv=sb3Converter,info_keywords=["social/productivity","trend/productivity"])
+montraidingenv=VecMonitor(venv=sb3_traderConverter)
+
+stackenv_basic=vec_frame_stack.VecFrameStack(venv=monenv,n_stack=num_frames)
+stackenv_traid=vec_frame_stack.VecFrameStack(venv=montraidingenv,n_stack=num_frames)
+# Model setup complete
+
+# Setup Eval Env
+econ_eval=foundation.make_env_instance(**eval_env_config)
+
+
+baseEconWrapper_eval=BaseEconWrapper(econ_eval)
+baseEconWrapper_eval.run()
+time.sleep(0.5)
+mobileRecieverEconWrapper_eval=RecieverEconWrapper(base_econ=baseEconWrapper_eval,agent_classname="BasicMobileAgent")
+tradeRecieverEconWrapper_eval=RecieverEconWrapper(base_econ=baseEconWrapper_eval,agent_classname="TradingAgent")
+sb3_traderConverter_eval=SB3EconConverter(tradeRecieverEconWrapper_eval,econ_eval,"TradingAgent",False)
+sb3Converter_eval=SB3EconConverter(mobileRecieverEconWrapper_eval,econ_eval,"BasicMobileAgent",False)
+# attach sb3 wrappers
+
+monenv_eval=VecMonitor(venv=sb3Converter_eval,info_keywords=["social/productivity","trend/productivity"])
+montraidingenv_eval=VecMonitor(venv=sb3_traderConverter_eval)
+
+stackenv_basic_eval=vec_frame_stack.VecFrameStack(venv=monenv_eval,n_stack=num_frames)
+stackenv_traid_eval=vec_frame_stack.VecFrameStack(venv=montraidingenv_eval,n_stack=num_frames)
+
+
+obs=monenv.reset()
+
+
+# define training functions
+def train(model,timesteps, econ_call,process_bar,name,db,index):
+    db[index]=model.learn(total_timesteps=timesteps,progress_bar=process_bar,reset_num_timesteps=False,tb_log_name=name,callback=TensorboardCallback(econ_call))
+
+
+
+# prepare training
+run_number=int(np.random.rand()*100)
+runname="run_{}".format(run_number)
+model_db=[None,None] # object for storing model
+
+
+model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.001, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_basic,env=stackenv_basic, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.001, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_trade,env=stackenv_traid, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+
+n_agents=econ.n_agents
+
+total_required_for_episode_basic=len(mobileRecieverEconWrapper.agnet_idx)*env_config['episode_length']
+total_required_for_episode_traid=len(tradeRecieverEconWrapper.agnet_idx)*env_config['episode_length']
+
+print("this is run {}".format(runname))
+
+while True:
+ 
+
+    #Train
+    runname="run_{}_{}".format(run_number,"basic")
+
+    thread_model=Thread(target=train,args=(model,total_required_for_episode_basic*150,econ,True,runname,model_db,0))
+    runname="run_{}_{}".format(run_number,"trader")
+    thread_model_traid=Thread(target=train,args=(model_trade,total_required_for_episode_traid*150,econ,False,runname,model_db,1))
+    
+    thread_model.start()
+    thread_model_traid.start()
+    thread_model.join()
+    thread_model_traid.join()
+    #normenv.save("temp-normalizer.ai")
+    model=model_db[0]
+    model_trade=model_db[1]
+    model.save("basic.ai")
+    model_trade.save("trade.ai")
+   
+    ## Run Eval
+    print("### EVAL ###")
+    obs_basic=stackenv_basic_eval.reset()
+    obs_trade=stackenv_traid_eval.reset()
+    done=False
+    for i in tqdm(range(eval_env_config['episode_length'])):
+        #create masks
+        masks_basic=stackenv_basic_eval.action_masks()
+        masks_trade=stackenv_traid_eval.action_masks()
+        # get actions
+        action_basic=model.predict(obs_basic,action_masks=masks_basic)
+        action_trade=model_trade.predict(obs_trade,action_masks=masks_trade)
+        #submit async directly for non blocking operation
+        sb3Converter_eval.step_async(action_basic[0])
+        sb3_traderConverter_eval.step_async(action_trade[0])
+        # retieve full results
+        obs_basic,rew_basic,done_e,info=stackenv_basic_eval.step(action_basic[0])
+        obs_trade,rew_trade,done_e,info=stackenv_traid_eval.step(action_trade[0])
+        done=done_e[0]
+
+
+
+    market=econ_eval.get_component("ContinuousDoubleAuction")
+    craft=econ_eval.get_component("Craft")
+   # trades=market.get_dense_log()
+    build=craft.get_dense_log()
+    met=econ.previous_episode_metrics
+    printReplay(econ_eval,0)
+   # printMarket(trades)
+  #  printBuilds(builds=build)
+    print("social/productivity: {}".format(met["social/productivity"]))
+    print("labor/weighted_cost: {}".format(met["labor/weighted_cost"]))
+    print("labor/warmup_integrator: {}".format(met["labor/warmup_integrator"]))
+
+    time.sleep(1)
+
+
+

main.py

@@ -157,7 +157,7 @@ class TensorboardCallback(BaseCallback):
             else:
                 self.metrics=self.econ.previous_episode_metrics
             curr_prod=self.metrics["social/productivity"]
-            trend_pord=curr_prod-prev_metrics["social/productivity"]
+            trend_pord=curr_prod/np.maximum(0.0001,prev_metrics["social/productivity"])
             self.logger.record("social/total_productivity", curr_prod)
             self.logger.record("social/delta_productivity", trend_pord)
         
@@ -292,8 +292,8 @@ runname="run_{}".format(run_number)
 model_db=[None,None] # object for storing model
 
 
-model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_basic,env=stackenv_basic, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
-model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_trade,env=stackenv_traid, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.001, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_basic,env=stackenv_basic, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.001, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_trade,env=stackenv_traid, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
 
 n_agents=econ.n_agents
 

reqirements.txt

@@ -1,3 +1,5 @@
+dash
+pandas
 
 gym
 ray[rllib]

test.py

@@ -39,7 +39,7 @@ env_config = {
     # The order in which components reset, step, and generate obs follows their listed order below.
     'components': [
         # (1) Building houses
-        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':1.5}),
+        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':2}),
         # (2) Trading collectible resources
         ('ContinuousDoubleAuction', {'max_num_orders': 10}),
         # (3) Movement and resource collection
@@ -52,7 +52,7 @@ env_config = {
     # ===== SCENARIO CLASS ARGUMENTS =====
     # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment)
     
-    'starting_agent_coin': 10,
+    'starting_agent_coin': 20,
     'fixed_four_skill_and_loc': True,
     
     # ===== STANDARD ARGUMENTS ======
@@ -60,6 +60,7 @@ env_config = {
     'agent_composition': {"BasicMobileAgent": 20,"TradingAgent":5},        # Number of non-planner agents (must be > 1)
     'world_size': [5, 5], # [Height, Width] of the env world
     'episode_length': 256, # Number of timesteps per episode
+    'isoelastic_eta':0.001,
     'allow_observation_scaling': True,
     'dense_log_frequency': 100, 
     'world_dense_log_frequency':1,
@@ -94,7 +95,7 @@ eval_env_config = {
     # The order in which components reset, step, and generate obs follows their listed order below.
     'components': [
         # (1) Building houses
-        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':1.5}),
+        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':2}),
         # (2) Trading collectible resources
         ('ContinuousDoubleAuction', {'max_num_orders': 10}),
         # (3) Movement and resource collection
@@ -107,7 +108,7 @@ eval_env_config = {
     # ===== SCENARIO CLASS ARGUMENTS =====
     # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment)
     
-    'starting_agent_coin': 10,
+    'starting_agent_coin': 20,
     'fixed_four_skill_and_loc': True,
     
     # ===== STANDARD ARGUMENTS ======
@@ -116,6 +117,7 @@ eval_env_config = {
     'world_size': [1, 1], # [Height, Width] of the env world
     'episode_length': 256, # Number of timesteps per episode
     'allow_observation_scaling': True,
+    'isoelastic_eta':0.001,
     'dense_log_frequency': 1, 
     'world_dense_log_frequency':1, 
     'energy_cost':0,
@@ -135,7 +137,7 @@ eval_env_config = {
     'flatten_masks': True,
 }
 
-num_frames=5
+num_frames=1
 
 class TensorboardCallback(BaseCallback):
     """
@@ -161,6 +163,23 @@ class TensorboardCallback(BaseCallback):
         
         return True
 
+min_at_target_basic=0.5
+min_lr_basic=5e-6
+start_lr_basic=9e-4
+
+min_at_target_trade=0.5
+min_lr_trade=5e-6
+start_lr_trade=9e-4
+
+def learning_rate_adj_basic(x) -> float:
+    diff=start_lr_basic-min_lr_basic
+    lr=min_lr_basic+x*diff
+    return lr
+
+def learning_rate_adj_trade(x) -> float:
+    diff=start_lr_trade-min_lr_trade
+    lr=min_lr_basic+x*diff
+    return lr
 
 def printMarket(market):
     for i in range(len(market)):
@@ -273,37 +292,15 @@ runname="run_{}".format(run_number)
 model_db=[None,None] # object for storing model
 
 
-model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=1e-5,env=stackenv_basic, seed=300,verbose=1,device="cuda",tensorboard_log="./log")
-model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=1e-5,env=stackenv_traid, seed=300,verbose=1,device="cuda",tensorboard_log="./log")
+model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_basic,env=stackenv_basic, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_trade,env=stackenv_traid, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+# Setup complete
 
-n_agents=econ.n_agents
-
-total_required_for_episode_basic=len(mobileRecieverEconWrapper.agnet_idx)*env_config['episode_length']
-total_required_for_episode_traid=len(tradeRecieverEconWrapper.agnet_idx)*env_config['episode_length']
-
-print("this is run {}".format(runname))
+# Load models
+model.load("basic.ai")
+model_trade.load("trade.ai")
 
 while True:
- 
-
-    #Train
-    runname="run_{}_{}".format(run_number,"basic")
-
-    thread_model=Thread(target=train,args=(model,total_required_for_episode_basic*50,econ,True,runname,model_db,0))
-    runname="run_{}_{}".format(run_number,"trader")
-    thread_model_traid=Thread(target=train,args=(model_trade,total_required_for_episode_traid*50,econ,False,runname,model_db,1))
-    
-    thread_model.start()
-    thread_model_traid.start()
-    thread_model.join()
-    thread_model_traid.join()
-    #normenv.save("temp-normalizer.ai")
-    model=model_db[0]
-    model_trade=model_db[1]
-    model.save("basic.ai")
-    model_trade.save("trade.ai")
-   
-    ## Run Eval
     print("### EVAL ###")
     obs_basic=stackenv_basic_eval.reset()
     obs_trade=stackenv_traid_eval.reset()
@@ -329,8 +326,8 @@ while True:
     craft=econ_eval.get_component("Craft")
    # trades=market.get_dense_log()
     build=craft.get_dense_log()
-    met=econ.previous_episode_metrics
-    printReplay(econ_eval,0)
+    met=econ_eval.previous_episode_metrics
+    printReplay(econ_eval,21)
    # printMarket(trades)
   #  printBuilds(builds=build)
     print("social/productivity: {}".format(met["social/productivity"]))

ui/.env

@@ -0,0 +1,3 @@
+MB=change_me
+POSTGRES_PASSWORD=change_me
+PGADMIN_PASSWORD=change_me

ui/docker-compose.yml

@@ -0,0 +1,41 @@
+version: "3.5"
+
+services:
+
+  postgres:
+    image: postgres:10
+    volumes:
+      - "./db_data:/var/lib/postgresql/data"
+    restart: always
+    environment:
+      POSTGRES_DB: metabase
+      POSTGRES_USER: metabase_usr
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
+
+  metabase:
+    depends_on:
+      - postgres
+    image: metabase/metabase
+    ports:
+      - "3000:3000"
+    restart: always
+    environment:
+      JAVA_TIMEZONE: Europe/Istanbul
+      MB_ENCRYPTION_SECRET_KEY: ${MB}
+      MB_DB_TYPE: postgres
+      MB_DB_DBNAME: metabase
+      MB_DB_PORT: 5432
+      MB_DB_USER: metabase_usr
+      MB_DB_PASS: ${POSTGRES_PASSWORD}
+      MB_DB_HOST: postgres
+ 
+  mongo:
+    image: mongo
+    restart: always
+    environment:
+      MONGO_INITDB_DATABASE: example
+      MONGO_INITDB_ROOT_USERNAME: root
+      MONGO_INITDB_ROOT_PASSWORD: example
+    volumes:
+      - /home/angelo/mongodata:/data/db
+