diff --git a/examples/power/conf/attack/default.yaml b/examples/power/conf/attack/default.yaml
index 6cc5ec60..7d528793 100644
--- a/examples/power/conf/attack/default.yaml
+++ b/examples/power/conf/attack/default.yaml
@@ -6,6 +6,9 @@ init:
     _target_: deckard.base.attack.AttackInitializer
     name: art.attacks.evasion.FastGradientMethod
     eps: .99
-    eps_step : ${eval:'(.1)*${.eps}'}
+    # eps_step : ${eval:'(.1)*${.eps}'}
+    batch_size : ${model.trainer.batch_size}
+    targeted : false
+    minimal : true
 attack_size : 10
 method : evasion
diff --git a/examples/power/conf/torch_cifar10.yaml b/examples/power/conf/torch_cifar10.yaml
index 6e69ab4a..1b862654 100644
--- a/examples/power/conf/torch_cifar10.yaml
+++ b/examples/power/conf/torch_cifar10.yaml
@@ -7,28 +7,25 @@ defaults:
     - scorers: default
     - override hydra/sweeper : optuna
     - override hydra/sweeper/sampler : tpe
+    # - override hydra/launcher : rq
 device_id : ${oc.env:DECKARD_DEVICE,cpu}
 stage : '???'
 direction : 
   - maximize
   - minimize
-  # - minimize
   - maximize
   - minimize
-  # - minimize
 optimizers: 
    - accuracy
    - train_time
-  #  - predict_proba_time
    - adv_accuracy
    - adv_fit_time
-  #  - adv_predict_time
 _target_ : deckard.base.experiment.Experiment
 hydra:
   run:
-    dir: ${files.directory}/${files.reports}/${stage}/logs/${data.generate.name}
+    dir: ${files.directory}/${files.reports}/logs/${stage}/${data.generate.name}
   sweep:
-    dir: ${files.directory}/${stage}/${data.generate.name}
+    dir: ${files.directory}/logs/${stage}/${data.generate.name}/
     subdir : ${hydra.sweeper.study_name}/${hydra.job.num}
   sweeper:
     study_name: ${model.init.name}
@@ -50,7 +47,28 @@ hydra:
       ++model.trainer.nb_epoch: int(range(1, 50))
       ++model.trainer.batch_size: int(range(10, 10000))
       ++attack.init.eps : range(0.01, 1.0)
-      ++attack.init.eps_step : ${eval:'(.01)*${attack.init.eps}'}
-      ++attack.init.batch_size: ${model.trainer.batch_size}
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
     direction: ${direction}
+#   launcher: REDIS QUEUE LAUNCHER
+#     # @package hydra.launcher
+#     _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher
+#     enqueue:
+#       job_timeout: 3600s
+#       ttl: null
+#       result_ttl: 1s
+#       failure_ttl: 1h
+#       at_front: false
+#       job_id: null
+#       description: null
+#     queue: default 
+#     redis:
+#       host: localhost
+#       port: ${oc.env:REDIS_PORT,6379}
+#       db: ${oc.env:REDIS_DB,0}
+#       password: ${oc.env:REDIS_PASSWORD,D3vAQY9y4YdwhV}
+#       ssl: ${oc.env:REDIS_SSL,False}
+#       ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null}
+#       mock: ${oc.env:REDIS_MOCK,False}
+#     stop_after_enqueue: false
+#     wait_polling: 1.0
+# raise_exception: True
diff --git a/examples/power/conf/torch_cifar100.yaml b/examples/power/conf/torch_cifar100.yaml
index a249b2af..07ef3690 100644
--- a/examples/power/conf/torch_cifar100.yaml
+++ b/examples/power/conf/torch_cifar100.yaml
@@ -13,23 +13,19 @@ stage : '???'
 direction : 
   - maximize
   - minimize
-  # - minimize
   - maximize
   - minimize
-  # - minimize
 optimizers: 
    - accuracy
    - train_time
-  #  - predict_proba_time
    - adv_accuracy
    - adv_fit_time
-  #  - adv_predict_time
 _target_ : deckard.base.experiment.Experiment
 hydra:
   run:
-    dir: ${files.directory}/${files.reports}/${stage}/logs/${data.generate.name}
+    dir: ${files.directory}/${files.reports}/logs/${stage}/${data.generate.name}
   sweep:
-    dir: ${files.directory}/${stage}//${data.generate.name}
+    dir: ${files.directory}/logs/${stage}/${data.generate.name}/
     subdir : ${hydra.sweeper.study_name}/${hydra.job.num}
   sweeper:
     study_name: ${model.init.name}
@@ -44,14 +40,35 @@ hydra:
       n_ei_candidates: 24 # default: 24
       multivariate: true
       warn_independent_sampling: true
-    n_trials : 100    
+    n_trials : 100
     params:
       ++data.sample.random_state: int(range(0, 1))
       ++model.art.initialize.optimizer.lr: range(0.0001, 1)
       ++model.trainer.nb_epoch: int(range(1, 50))
       ++model.trainer.batch_size: int(range(10, 10000))
       ++attack.init.eps : range(0.01, 1.0)
-      ++attack.init.eps_step : ${eval:'(.01)*${attack.init.eps}'}
-      ++attack.init.batch_size: ${model.trainer.batch_size}
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
     direction: ${direction}
+#   launcher: REDIS QUEUE LAUNCHER
+#     # @package hydra.launcher
+#     _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher
+#     enqueue:
+#       job_timeout: 3600s
+#       ttl: null
+#       result_ttl: 1s
+#       failure_ttl: 1h
+#       at_front: false
+#       job_id: null
+#       description: null
+#     queue: default 
+#     redis:
+#       host: localhost
+#       port: ${oc.env:REDIS_PORT,6379}
+#       db: ${oc.env:REDIS_DB,0}
+#       password: ${oc.env:REDIS_PASSWORD,D3vAQY9y4YdwhV}
+#       ssl: ${oc.env:REDIS_SSL,False}
+#       ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null}
+#       mock: ${oc.env:REDIS_MOCK,False}
+#     stop_after_enqueue: false
+#     wait_polling: 1.0
+# raise_exception: True
diff --git a/examples/power/conf/torch_mnist.yaml b/examples/power/conf/torch_mnist.yaml
index a2a34c5b..3fae4e57 100644
--- a/examples/power/conf/torch_mnist.yaml
+++ b/examples/power/conf/torch_mnist.yaml
@@ -13,23 +13,19 @@ stage : '???'
 direction : 
   - maximize
   - minimize
-  # - minimize
   - maximize
   - minimize
-  # - minimize
 optimizers: 
    - accuracy
    - train_time
-  #  - predict_proba_time
    - adv_accuracy
    - adv_fit_time
-  #  - adv_predict_time
 _target_ : deckard.base.experiment.Experiment
 hydra:
   run:
-    dir: ${files.directory}/${files.reports}/${stage}/logs/${data.generate.name}
+    dir: ${files.directory}/${files.reports}/logs/${stage}/${data.generate.name}
   sweep:
-    dir: ${files.directory}/${stage}/${data.generate.name}
+    dir: ${files.directory}/logs/${stage}/${data.generate.name}/
     subdir : ${hydra.sweeper.study_name}/${hydra.job.num}
   sweeper:
     study_name: ${model.init.name}
@@ -51,8 +47,6 @@ hydra:
       ++model.trainer.nb_epoch: int(range(1, 50))
       ++model.trainer.batch_size: int(range(10, 10000))
       ++attack.init.eps : range(0.01, 1.0)
-      ++attack.init.eps_step : ${eval:'(.01)*${attack.init.eps}'}
-      ++attack.init.batch_size: ${model.trainer.batch_size}
     _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
     direction: ${direction}
 #   launcher: REDIS QUEUE LAUNCHER
diff --git a/examples/pytorch/cifar100/conf/cifar100.yaml b/examples/pytorch/cifar100/conf/cifar100.yaml
index a0727d43..1cfee53b 100644
--- a/examples/pytorch/cifar100/conf/cifar100.yaml
+++ b/examples/pytorch/cifar100/conf/cifar100.yaml
@@ -24,7 +24,7 @@ hydra:
     direction: ${direction}
     study_name: control
     storage: sqlite:///model.db
-    n_jobs: 4
+    n_jobs: 8
     n_trials : 32
     params:
       ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
diff --git a/examples/pytorch/cifar100/dvc.lock b/examples/pytorch/cifar100/dvc.lock
index ebda37d0..3a6310c7 100644
--- a/examples/pytorch/cifar100/dvc.lock
+++ b/examples/pytorch/cifar100/dvc.lock
@@ -100,11 +100,11 @@ stages:
       md5: 1070854e6c00fc787bc0fdfc82792fd6
       size: 761280311
     - path: cifar100/reports/train/default/predictions.json
-      md5: 406b5670c6fc9741adf82a3922f5d5f2
-      size: 24401460
+      md5: 59e3f3a1d965ce83bce8b10bfb568142
+      size: 24418063
     - path: cifar100/reports/train/default/score_dict.json
-      md5: 39cfcc1fc0d5bef2dafef2eb0295d934
-      size: 885
+      md5: 9cb64ff83dede1bd4adb4fbef7bb0d9c
+      size: 900
   attack:
     cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml
     deps:
@@ -326,14 +326,14 @@ stages:
             name: sklearn.metrics.log_loss
     outs:
     - path: cifar100/attacks/attack.pkl
-      md5: a80777574854b5cefe05d26ca2a8ba7c
+      md5: 408c383e6708d42e2271cc33956af948
       size: 123046
     - path: cifar100/reports/attack/default/adv_predictions.json
-      md5: 321ea49f7061832db3c5142f0ad7b042
-      size: 21395
+      md5: f4effd039275cf68f3978bd17091b8dd
+      size: 21441
     - path: cifar100/reports/attack/default/score_dict.json
-      md5: e0afe623a549e81ca7d44a65ce362ee2
-      size: 1356
+      md5: 5571912697a68b76528e517c58159245
+      size: 1359
   attacks@ResNet18:
     cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18
       stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet18.db