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112 lines
3.3 KiB
112 lines
3.3 KiB
import subprocess
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import sys
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from random import sample, seed
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import numpy as np # noqa: F401
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from lightning_sdk import Machine, Studio # noqa: F401
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# consistency of randomly sampled experiments.
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seed(19920921)
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NUM_JOBS = 100
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# reference to the current studio
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# if you run outside of Lightning, you can pass the Studio name
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# studio = Studio()
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# use the jobs plugin
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# studio.install_plugin("jobs")
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# job_plugin = studio.installed_plugins["jobs"]
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# do a sweep over learning rates
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# Define the ranges or sets of values for each hyperparameter
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# alpha_values = list(np.round(np.linspace(2, 4, 21), 4))
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# learning_rate_values = list(np.round(np.logspace(-5, -3, 21), 5))
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learning_rate_values = [1e-3]
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# learning_rate_values = [5e-4]
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# alpha_values = [0, .25, 0.5, 0.75, 1] # alpha = 0 is unsupervised. alpha = 1 is supervised.
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alpha_values = [1]
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# widths = [2**k for k in range(4, 13)]
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# depths = [1, 2, 4, 8, 16]
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widths, depths = [512], [4]
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batch_size_values = [256]
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max_epochs_values = [100]
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seeds = list(range(21, 1992))
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optimizers = [
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# "Adagrad",
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"Adam",
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# "SGD",
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# "AdamW",
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# "LBFGS",
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# "RAdam",
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# "RMSprop",
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# "Adadelta",
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]
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# Generate all possible combinations of hyperparameters
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all_params = [
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(alpha, lr, bs, me, s, w, d, opt)
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for alpha in alpha_values
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for lr in learning_rate_values
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for bs in batch_size_values
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for me in max_epochs_values
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for s in seeds
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for w in widths
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for d in depths
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for opt in optimizers
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]
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# perform random search with a limit
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search_params = sample(all_params, min(NUM_JOBS, len(all_params)))
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# --trainer.callbacks+ lightning.pytorch.callbacks.EarlyStopping \
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# --trainer.callbacks.init_args.monitor hp_metric \
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for idx, params in enumerate(search_params):
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a, lr, bs, me, s, w, d, opt = params
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# cmd = f"cd ~/colors && python main.py --alpha {a} --lr {lr} --bs {bs} --max_epochs {me} --seed {s} --width {w}"
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cmd = f"""
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python newmain.py fit \
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--seed_everything {s} \
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--data.batch_size {bs} \
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--data.train_size 0 \
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--data.val_size 10000 \
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--model.alpha {a} \
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--model.width {w} \
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--model.depth {d} \
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--model.bias true \
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--model.transform tanh \
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--trainer.min_epochs 10 \
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--trainer.max_epochs {me} \
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--trainer.log_every_n_steps 3 \
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--trainer.check_val_every_n_epoch 1 \
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--trainer.limit_val_batches 50 \
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--trainer.callbacks callbacks.SaveImageCallback \
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--trainer.callbacks.init_args.final_dir out \
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--trainer.callbacks.init_args.save_interval 0 \
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--optimizer torch.optim.{opt} \
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--optimizer.init_args.lr {lr} \
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--trainer.callbacks+ lightning.pytorch.callbacks.LearningRateFinder
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# --lr_scheduler lightning.pytorch.cli.ReduceLROnPlateau \
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# --lr_scheduler.init_args.monitor hp_metric \
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# --lr_scheduler.init_args.factor 0.05 \
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# --lr_scheduler.init_args.patience 5 \
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# --lr_scheduler.init_args.cooldown 10 \
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# --lr_scheduler.init_args.verbose true
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"""
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# job_name = f"color2_{bs}_{a}_{lr:2.2e}"
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# job_plugin.run(cmd, machine=Machine.T4, name=job_name)
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print(f"Running {params}: {cmd}")
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try:
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# Run the command and wait for it to complete
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# subprocess.run(test_cmd, shell=True, check=True)
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subprocess.run(cmd, shell=True, check=True)
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except KeyboardInterrupt:
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print("Interrupted by user")
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sys.exit(1)
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# except subprocess.CalledProcessError:
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# pass
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