job looks good to go

newsearch.py

@@ -8,8 +8,7 @@ from lightning_sdk import Machine, Studio  # noqa: F401
 # consistency of randomly sampled experiments.
 seed(19920921)
 
-NUM_JOBS = 1
-
+NUM_JOBS = 10
 
 # Define the ranges or sets of values for each hyperparameter
 # alpha_values = list(np.round(np.linspace(2, 4, 21), 4))
@@ -21,11 +20,12 @@ learning_rate_values = [1e-3]
 alpha_values = [0, 0.1]
 widths = [2**k for k in range(4, 13)]
 depths = [1, 2, 4, 8, 16]
+dropouts = [0, 0.25, 0.5]
 # widths, depths = [512], [4]
 
 batch_size_values = [256]
 max_epochs_values = [420]  # at 12 fps, around 35s
-seeds = list(range(20, 1992))
+seeds = list(range(21, 1992))
 optimizers = [
     # "Adagrad",
     "Adam",
@@ -39,7 +39,7 @@ optimizers = [
 
 # Generate all possible combinations of hyperparameters
 all_params = [
-    (alpha, lr, bs, me, s, w, d, opt)
+    (alpha, lr, bs, me, s, w, d, opt, dr)
     for alpha in alpha_values
     for lr in learning_rate_values
     for bs in batch_size_values
@@ -48,6 +48,7 @@ all_params = [
     for w in widths
     for d in depths
     for opt in optimizers
+    for dr in dropouts
 ]
 
 
@@ -58,7 +59,7 @@ search_params = sample(all_params, min(NUM_JOBS, len(all_params)))
 # --trainer.callbacks.init_args.monitor hp_metric \
 
 for idx, params in enumerate(search_params):
-    a, lr, bs, me, s, w, d, opt = params
+    a, lr, bs, me, s, w, d, opt, dr = params
     # cmd = f"cd ~/colors && python main.py --alpha {a} --lr {lr} --bs {bs} --max_epochs {me} --seed {s} --width {w}"
     cmd = f"""
 cd ~/colors && python newmain.py fit \
@@ -72,7 +73,7 @@ cd ~/colors && python newmain.py fit \
 --model.bias true \
 --model.loop true \
 --model.transform tanh \
---model.dropout 0.5 \
+--model.dropout {dr} \
 --trainer.min_epochs 10 \
 --trainer.max_epochs {me} \
 --trainer.log_every_n_steps 3 \
@@ -84,19 +85,19 @@ cd ~/colors && python newmain.py fit \
 --optimizer torch.optim.{opt} \
 --optimizer.init_args.lr {lr} \
 --trainer.callbacks+ lightning.pytorch.callbacks.LearningRateFinder
-# --lr_scheduler lightning.pytorch.cli.ReduceLROnPlateau \
-# --lr_scheduler.init_args.monitor hp_metric \
-# --lr_scheduler.init_args.factor 0.05 \
-# --lr_scheduler.init_args.patience 5 \
-# --lr_scheduler.init_args.cooldown 10 \
-# --lr_scheduler.init_args.verbose true
 """
+    # --lr_scheduler lightning.pytorch.cli.ReduceLROnPlateau \
+    # --lr_scheduler.init_args.monitor hp_metric \
+    # --lr_scheduler.init_args.factor 0.05 \
+    # --lr_scheduler.init_args.patience 5 \
+    # --lr_scheduler.init_args.cooldown 10 \
+    # --lr_scheduler.init_args.verbose true
     print(f"Running {params}: {cmd}")
     try:
         studio = Studio("colors-animate-jobs")
         studio.install_plugin("jobs")
         job_plugin = studio.installed_plugins["jobs"]
-        job_name = f"color-animate-{idx}-{s}"
+        job_name = f"colors-animate-20240303-{idx}"
         job_plugin.run(cmd, machine=Machine.T4, name=job_name)
 
         # Run the command and wait for it to complete