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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 | |