sweep.yml

'''
This is an example yaml file for conducting a sweep using wandb
For more info check out https://docs.wandb.ai/sweeps. In the main script you need to 
log at least one metric to wandb. I would also advise logging the configuration of the model.


not the below config would run:

python main.py --lr=0.00something --activation=somethingrelu etc


to set up the wandb sweep run:

wandb sweep sweep.yaml

copy the sweep-id then ssh into any gpu you have available and run:

wandb agent sweep-id

note that if you are using any of the gpuclusters using slurm you should add an additional
argument to the to the above command:

wandb agent --count 1 sweep-id

this then runs only one job. You can then queue up a number of jobs on the gpucluster. i.e. in the call_gpu.py
file use the above command.

cross validation note: some have asked about how to use a sweep with cross validaiton, as the sweep needs a single metric
to optimise, maybe you should return the final mean metric score across all folds? If anyone has a better suggestion feel 
free to add.
'''



program: main.py # program to run 
command:
  - ${interpreter} # this arguement ensures that 'python' preceeds the program command, in this case main.py
  - ${program} # call ths program arg --> main.py
  - ${args} # calls all the below parameter args, so lr, activation etc

method: random # This can be a 'grid' search or 'bayes' search
metric:
  name: Test F1 # The name of the metric you log to wandb (with wandb.log)that you want to max/min
  goal: maximize
parameters: # now the hyper-parameters to vary. not these need to be arguments in the script e.g. argparse
  lr:
    distribution: log_uniform
    min: 0.000001
    max: 0.0001
  activation:
    distribution: categorical
    values:
    - relu
    - elu
    - selu
    - prelu
    - randomrelu
  masking:
    values: [False, True]