Better logging power example (#167)

* better logging for the power example

* better optuna loggin

* make tpe sampler use 1 job explicitly

* update learning reate

* log interval for learning rate

* fixed random seed bug

* make all range calls intervals in power

* fix database overwriting each other in power example

examples/power/conf/torch_cifar10.yaml

@@ -28,7 +28,7 @@ hydra:
     dir: ${files.directory}/logs/
     subdir : ${hydra.sweeper.study_name}/${hydra.job.num}
   sweeper:
-    study_name: ${device_id}_${stage}
+    study_name: ${stage}
     sampler:
       _target_: optuna.samplers.TPESampler
       consider_prior: true

examples/power/conf/torch_cifar100.yaml

@@ -28,7 +28,7 @@ hydra:
     dir: ${files.directory}/logs/
     subdir : ${hydra.sweeper.study_name}/${hydra.job.num}
   sweeper:
-    study_name: ${device_id}_${stage}
+    study_name: ${stage}
     sampler:
       _target_: optuna.samplers.TPESampler
       consider_prior: true

examples/power/conf/torch_mnist.yaml

@@ -25,10 +25,11 @@ hydra:
   run:
     dir: ${files.directory}/${files.reports}/logs/${stage}/
   sweep:
-    dir: ${files.directory}/logs/
+    dir: ${files.directory}/logs/${hydra.sweeper.storage}
     subdir : ${hydra.sweeper.study_name}/${hydra.job.num}
   sweeper:
-    study_name: ${device_id}_${stage}
+    study_name: ${stage}
+    storage: sqlite:///model.db
     sampler:
       _target_: optuna.samplers.TPESampler
       consider_prior: true

examples/power/dvc.yaml

@@ -68,7 +68,7 @@ stages:
       - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file}:
           cache: false
   attacks:
-    cmd: python -m deckard.layers.optimise  --multirun ++attack.attack_size=100 stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${data.generate.name}.db  --config-name ${data.generate.name}.yaml
+    cmd: python -m deckard.layers.optimise  --multirun ++attack.attack_size=100 stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${device_id}.db  --config-name ${data.generate.name}.yaml
     deps:
       - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage
     outs:

examples/pytorch/cifar100/conf/cifar100.yaml

@@ -24,7 +24,7 @@ hydra:
     direction: ${direction}
     study_name: control
     storage: sqlite:///model.db
-    n_jobs: 8
+    n_jobs: 4
     n_trials : 32
     params:
       ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)