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--- # ---------------------------
# `sample` can be
# - `uniform` (np.random.uniform(*from))
# - `range` (np.choice(np.arange(*from)))
# - `list` (np.choice(from))
# - `cartesian` special case where a cartesian product of all keys with the `cartesian` sampling scheme
# is created and iterated over in order. `from` MUST be a list
# As we iterate over the cartesian product of all
# such keys, others are sampled as usual. If n_search is larger than the size of the cartesian
# product, it will cycle again through the product in the same order
# example with A being `cartesian` from [1, 2] and B from [y, z] and 5 searches:
# => {A:1, B: y}, {A:1, B: z}, {A:2, B: y}, {A:2, B: z}, {A:1, B: y}
# - `sequential` samples will loop through the values in `from`. `from` MUST be a list
# ---------------------------
# ----- SBATCH config -----
cpus: 8
partition: long
mem: 32G
gres: "gpu:rtx8000:1"
codeloc: $HOME/ccai/climategan
modules: "module load anaconda/3 && module load pytorch"
conda: "conda activate climatenv && conda deactivate && conda activate climatenv"
n_search: -1
# ------------------------
# ----- Train Args -----
# ------------------------
"args.note": "Hyper Parameter search #1"
"args.comet_tags": ["masker_search", "v1"]
"args.config": "config/trainer/my_config.yaml"
# --------------------------
# ----- Model config -----
# --------------------------
"gen.opt.lr":
sample: list
from: [0.01, 0.001, 0.0001, 0.00001]
"dis.opt.lr":
sample: uniform
from: [0.01, 0.001]
"dis.opt.optimizer":
sample: cartesian
from:
- ExtraAdam
- Adam
"gen.opt.optimizer":
sample: cartesian
from:
- ExtraAdam
- Adam
"gen.lambdas.C":
sample: cartesian
from:
- 0.1
- 0.5
- 1
"data.loaders.batch_size":
sample: sequential
from:
- 2
- 4
- 6
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