configure s3 bucket with dvc remote

.dvc/config

@@ -1,6 +1,4 @@
 [core]
     remote = s3bucket
-['remote "remote"']
-    url = ../temp
 ['remote "s3bucket"']
-    url = s3://dvcs3bucket
+    url = s3://dvcs3bucket/dvcArtifacts

dvc.lock

@@ -5,8 +5,8 @@ stages:
     deps:
     - path: ./src/data/load_dataset.py
       hash: md5
-      md5: d58bb455b8d0d6a6760e593e2297d2c7
-      size: 2114
+      md5: 66e46ce7e93b2c343cb1fff2b8ec22de
+      size: 2120
     params:
       params.yaml:
         load_dataset.drive_link: 
@@ -87,8 +87,8 @@ stages:
       size: 601982
     - path: ./src/models/train_model.py
       hash: md5
-      md5: 5e294cb2c3d36529e7c479180f25efe5
-      size: 5844
+      md5: a74f763ded5dd5b54217be40162cea3f
+      size: 5884
     params:
       params.yaml:
         base.target: quality
@@ -120,18 +120,18 @@ stages:
       size: 601982
     - path: ./src/models/tune_model.py
       hash: md5
-      md5: 1aa831ddf9b7ee87cf1eac33ee2e057d
-      size: 7892
+      md5: b82ee90750a841620d81bb9e97e02796
+      size: 8258
     params:
       params.yaml:
         base.target: quality
         build_features.extended_data: /data/extended
-        hyperopt.max_eval: 2
+        hyperopt.max_eval: 1
         hyperopt.model_name: best_model
         mlflow_config.remote_server_uri: http://localhost:5000
         mlflow_config.tunningExpName: modeltunning
     outs:
     - path: ./models/best_model.joblib
       hash: md5
-      md5: 2e6ecc02267e8f6506646e8ba6e7f6cd
-      size: 538265
+      md5: da0e91c46198ff9229b07fb8e3302c34
+      size: 164697