linting, moved env, updated contrib credit

environments/community/pytorch_optimizer_coding/FOB/baselines/README.md

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+here you can find the configs that were used to create the baseline.
+Make sure to adapt the paths to data and the experiments output!

environments/community/pytorch_optimizer_coding/FOB/baselines/classification.yaml

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+task:
+  name: classification
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-2, 1.e-3, 1.e-4]
+    weight_decay: [10, 1.e-0, 1.e-1, 1.e-2]
+  - name: adamcpr_fast
+    learning_rate: [1.e-2, 1.e-3, 1.e-4]
+    kappa_init_param: [0.125, 0.25, 0.5, 1]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.8  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/classification_small-adamcpr_paper.yaml

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+task:
+  name: classification_small
+  output_dir_name: classification_small_cpr_paper
+  label_smoothing: 0.1
+  train_transforms:
+    trivial_augment:
+      use: false
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-1, 1.e-2, 1.e-3, 1.e-4]
+    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
+    weight_decay: [1, 1.e-1, 1.e-2, 1.e-3, 1.e-4, 0]
+    warmup_factor: 0.025
+    eta_min_factor: 0.1
+  - name: adamcpr_fast
+    learning_rate: [1.e-1, 1.e-2, 1.e-3, 1.e-4]
+    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
+    kappa_init_param: [0.5, 1, 2, 4, 8, 16, 32]
+    warmup_factor: 0.025
+    eta_min_factor: 0.1
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.8  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/classification_small.yaml

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+task:
+  name: classification_small
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-1, 1.e-2, 1.e-3, 1.e-4]
+    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
+    weight_decay: [10, 1.e-0, 1.e-1, 1.e-2, 1.e-3]
+  - name: adamcpr_fast
+    learning_rate: [1.e-1, 1.e-2, 1.e-3, 1.e-4]
+    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
+    kappa_init_param: [1, 2, 4, 8, 16]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.8  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/graph.yaml

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+task:
+  name: graph
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-2, 1.e-3, 1.e-4]
+    weight_decay: [1.e-1, 1.e-2, 1.e-3, 0]
+  - name: adamcpr_fast
+    learning_rate: [1.e-2, 1.e-3, 1.e-4]
+    kappa_init_param: [0.5, 1, 2, 4]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.8  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/graph_tiny.yaml

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+task:
+  name: graph_tiny
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-0, 1.e-1, 1.e-2, 1.e-3]
+    weight_decay: [1, 1.e-1, 1.e-2, 1.e-3, 1.e-4, 0]
+  - name: adamcpr_fast
+    learning_rate: [1.e-0, 1.e-1, 1.e-2, 1.e-3]
+    kappa_init_param: [0.5, 1, 2, 4, 8, 16, 32]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/mnist.yaml

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+task:
+  name: mnist
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-1, 1.e-2, 1.e-3]
+    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
+    weight_decay: [1.e-0, 1.e-1, 1.e-2, 1.e-3]
+  - name: adamcpr_fast
+    learning_rate: [1.e-1, 1.e-2, 1.e-3]
+    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
+    kappa_init_param: [0.5, 1, 2, 4, 8, 16, 32]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.8  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/sbatch_template.sh

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+module load devel/miniconda
+
+nvidia-smi
+
+source ~/.bashrc
+# some users reported issues with stacked conda environments; see https://en.wikipedia.org/wiki/Rule_of_three_(writing)
+conda deactivate
+conda deactivate
+conda deactivate
+conda activate fob
+
+# Running the job
+
+start=$(date +%s)
+
+__FOB_COMMAND__
+
+finish=$(date +%s)
+
+runtime=$((finish-start))
+
+echo Job execution complete.
+echo Total job runtime: $runtime seconds

environments/community/pytorch_optimizer_coding/FOB/baselines/segmentation.yaml

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+task:
+  name: segmentation
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [3.16e-3, 1.e-3, 3.16e-4]
+    weight_decay: [1.e-1, 1.e-2, 1.e-3, 0]
+  - name: adamcpr_fast
+    learning_rate: [3.16e-3, 1.e-3, 3.16e-4]
+    kappa_init_param: [1, 4, 16, 64]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.5  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+  save_sbatch_scripts: slurm-scripts
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/tabular.yaml

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+task:
+  name: tabular
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [1.e-2, 1.e-3, 1.e-4]
+    weight_decay: [10, 1.e-0, 1.e-1, 1.e-2, 1.e-3]
+  - name: adamcpr_fast
+    learning_rate: [1.e-2, 1.e-3, 1.e-4]
+    kappa_init_param: [0.5, 1, 2, 4, 8]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 1.8  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay

environments/community/pytorch_optimizer_coding/FOB/baselines/translation.yaml

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+task:
+  name: translation
+optimizer:
+  - name: adamw_baseline
+    learning_rate: [3.16e-3, 1.e-3, 3.16e-4]
+    # learning_rate: [3.16e-3, 1.77e-3, 1.e-3, 5.16e-3, 3.16e-4]  # finer grid
+    weight_decay: [1.e-0, 1.e-1, 1.e-2]
+  - name: adamcpr_fast
+    learning_rate: [3.16e-3, 1.e-3, 3.16e-4]
+    # learning_rate: [3.16e-3, 1.77e-3, 1.e-3, 5.16e-3, 3.16e-4]  # finer grid
+    kappa_init_param: [0.5, 1, 2]
+engine:
+  seed: [1, 2, 3]
+  # data_dir: ./data
+  # output_dir: ./experiments
+  plot: false
+  silent: true
+  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
+  run_scheduler: slurm_array
+  sbatch_time_factor: 2.0  # increase this for slower machine
+  sbatch_args:
+    partition: single  # adapt to your cluster
+evaluation:
+  output_types: [pdf]
+  plot:
+    x_axis:
+      - optimizer.kappa_init_param
+      - optimizer.weight_decay