job updates

newsearch.py

@@ -5,7 +5,7 @@ from random import sample
 import numpy as np  # noqa: F401
 from lightning_sdk import Machine, Studio  # noqa: F401
 
-NUM_JOBS = 100
+NUM_JOBS = 500
 
 # reference to the current studio
 # if you run outside of Lightning, you can pass the Studio name
@@ -23,6 +23,7 @@ NUM_JOBS = 100
 learning_rate_values = [1e-2]
 alpha_values = [0]
 widths = [2**k for k in range(4, 15)]
+depths = [1, 2, 4, 8, 16]
 # learning_rate_values = [5e-4]
 batch_size_values = [256]
 max_epochs_values = [100]
@@ -35,18 +36,18 @@ optimizers = [
     "LBFGS",
     "RAdam",
     "RMSprop",
-    "SparseAdam",
     "Adadelta",
 ]
 # Generate all possible combinations of hyperparameters
 all_params = [
-    (alpha, lr, bs, me, s, w, opt)
+    (alpha, lr, bs, me, s, w, d, opt)
     for alpha in alpha_values
     for lr in learning_rate_values
     for bs in batch_size_values
     for me in max_epochs_values
     for s in seeds
     for w in widths
+    for d in depths
     for opt in optimizers
 ]
 
@@ -55,7 +56,7 @@ all_params = [
 search_params = sample(all_params, min(NUM_JOBS, len(all_params)))
 
 for idx, params in enumerate(search_params):
-    a, lr, bs, me, s, w, opt = params
+    a, lr, bs, me, s, w, d, opt = params
     # cmd = f"cd ~/colors && python main.py --alpha {a} --lr {lr} --bs {bs} --max_epochs {me} --seed {s} --width {w}"
     cmd = f"""
 python newmain.py fit \
@@ -65,7 +66,7 @@ python newmain.py fit \
 --data.val_size 100000 \
 --model.alpha {a} \
 --model.width {w} \
---model.depth 1 \
+--model.depth {d} \
 --trainer.min_epochs 10 \
 --trainer.max_epochs {me} \
 --trainer.log_every_n_steps 3 \
@@ -96,3 +97,5 @@ python newmain.py fit \
     except KeyboardInterrupt:
         print("Interrupted by user")
         sys.exit(1)
+    # except subprocess.CalledProcessError:
+    #     pass