timestamp format changed seconds -> miliseconds

deployment/nbeats/main.py

@@ -94,8 +94,8 @@ def main():
     # msg2 = Msg()
     # msg2.body = """{
     #     "metrics": ["cpu_usage"],
-    #     "timestamp": 0,
-    #     "epoch_start": 0,
+    #     "timestamp": 1623057648907,
+    #     "epoch_start": 1623057698298,
     #     "number_of_forward_predictions": 5,
     #     "prediction_horizon": 120}"""
 

deployment/nbeats/predict.py

@@ -48,7 +48,6 @@ class CustomListener(stomp.ConnectionListener):
             metrics = set(json.loads(frame.body)["metrics"])
 
             if self.start:
-                print("STARTTTTTT", self.start)
                 predicted_metrics = predicted_metrics.union(metrics)
                 for metric in metrics:
                     frame_body = json.loads(frame.body)
@@ -87,11 +86,13 @@ def main():
     influxdb_conn = InfluxdbPredictionsSender(METHOD, APP_NAME)
 
     logging.debug(
-        f"waiting {msg['epoch_start'] - int(time.time()) - prediction_cycle} seconds"
+        f"waiting {(msg['epoch_start'] - int(time.time()) * 1000 - prediction_cycle * 1000) // 1000} seconds"
     )
 
-    print("waiting: ", msg["epoch_start"] - int(time.time()) - prediction_cycle)
-    time.sleep(max(0, msg["epoch_start"] - int(time.time()) - prediction_cycle))
+    time.sleep(
+        max(0, (msg["epoch_start"] - int(time.time()) * 1000 - prediction_cycle * 1000))
+        // 1000
+    )
 
     tl = Timeloop()
 

deployment/nbeats/src/model_train.py

@@ -91,7 +91,7 @@ def train(target_column, yaml_file="nbeats.yaml"):
     msg = {
         "metrics": target_column,
         "forecasting_method": os.environ.get("METHOD", "nbetas"),
-        "timestamp": time.time(),
+        "timestamp": int(time.time()) * 1000,
     }
     print(msg)
     return msg

deployment/tft/predict.py

@@ -48,7 +48,6 @@ class CustomListener(stomp.ConnectionListener):
             metrics = set(json.loads(frame.body)["metrics"])
 
             if self.start:
-                print("STARTTTTTT", self.start)
                 predicted_metrics = predicted_metrics.union(metrics)
                 for metric in metrics:
                     frame_body = json.loads(frame.body)
@@ -87,11 +86,13 @@ def main():
     influxdb_conn = InfluxdbPredictionsSender(METHOD, APP_NAME)
 
     logging.debug(
-        f"waiting {msg['epoch_start'] - int(time.time()) - prediction_cycle} seconds"
+        f"waiting {(msg['epoch_start'] - int(time.time()) * 1000 - prediction_cycle * 1000) // 1000} seconds"
     )
 
-    print("waiting: ", msg["epoch_start"] - int(time.time()) - prediction_cycle)
-    time.sleep(max(0, msg["epoch_start"] - int(time.time()) - prediction_cycle))
+    time.sleep(
+        max(0, (msg["epoch_start"] - int(time.time()) * 1000 - prediction_cycle * 1000))
+        // 1000
+    )
 
     tl = Timeloop()
 

deployment/tft/src/model_train.py

@@ -95,7 +95,7 @@ def train(target_column, prediction_length, yaml_file="model.yaml"):
     msg = {
         "metrics": target_column,
         "forecasting_method": os.environ.get("METHOD", "tft"),
-        "timestamp": int(time.time()),
+        "timestamp": int(time.time()) * 1000,
     }
 
     return msg