diff --git a/dreem/training/configs/base.yaml b/dreem/training/configs/base.yaml
index a93c21d..15d2898 100644
--- a/dreem/training/configs/base.yaml
+++ b/dreem/training/configs/base.yaml
@@ -114,7 +114,7 @@ logging:
   group: "example"
   save_dir: './logs'
   project: "GTR"
-  log_model: "all"
+  log_model: null
 
 early_stopping:
   monitor: "val_loss"
@@ -131,12 +131,12 @@ checkpointing:
   save_last: true
   dirpath: null
   auto_insert_metric_name: true
-  every_n_epochs: 10
+  every_n_epochs: 1
 
 trainer:
-  # only use mps and devices params for apple silicon runs
-  accelerator: "mps"
-  devices: 1
+  # only use this for local apple silicon runs; change for cluster runs
+  # accelerator: "mps"
+  # devices: 1
   check_val_every_n_epoch: 1
   enable_checkpointing: true
   gradient_clip_val: null
diff --git a/dreem/training/configs/run_batch_job.py b/dreem/training/configs/run_batch_job.py
deleted file mode 100644
index f5910c9..0000000
--- a/dreem/training/configs/run_batch_job.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import subprocess as sp
-import pandas as pd
-
-# to use this, just run python run_batch_job.py in cmd
-
-gpu = "0.1" # amount of GPU to use per task
-job_name = "mustafa-test-batch-job"
-
-base = "/home/runner/talmodata-smb/mustafa/dreem-experiments/run/mice-btc" #where to run the job from
-dreem_repo = "/home/runner/talmodata-smb/mustafa/dreem-experiments/src/dreem" #where the dreem repo is stored
-
-config_dir=os.path.join(base, "configs") #where to find the configs
-config_name= "base" #base config name
-params_cfg = os.path.join(config_dir, "override.yaml") #override config 
-
-# if running just 1 job, comment this line out and delete the ++batch_config command in the command below
-# each row in this file is a separate run with overrides
-# naming method: have the first column as logging.name (wandb logging); this creates the directory ./models/logging.name
-task_csv = os.path.join(config_dir, "demo_batch.csv") # csv for tasks - each pod is a task
-
-# number of VMs that are spun up (also the number of tasks that you are running)
-# note that the server must be mounted locally as a network location to use this if the csv is on the cluster
-pods = len(pd.read_csv(task_csv.replace("/home/runner/talmodata-smb", "/Volumes/talmodata")))
-par = min(int(1/float(gpu)), pods) #number of tasks that can be run in parallel (always smaller than pods)
-
-# enter your WANDB API KEY in the cmd section
-# mount both smb and vast volumes
-cmd = [
-    "runai",
-    "submit",
-    "--gpu",
-    gpu,
-    "--name",
-    job_name,
-    "--preemptible",
-    "-i",
-    "asheridan/biogtr",
-    "-v",
-    "/data/talmolab-smb:/home/runner/talmodata-smb", 
-    "-v",
-    "/talmo:/home/runner/vast"
-    "-e",
-    f"RUNNER_CMD=cp -r {dreem_repo} ~ && mamba env create -n dreem -f ~/dreem/environment.yml && export WANDB_API_KEY=6cc5012a6ecfb9cd970bd07686dbfcefd3190a04 && cd {base} && conda run -n dreem dreem-train --config-dir={config_dir} --config-name={config_name} ++params_config={params_cfg} ++batch_config={task_csv}",
-    "--parallelism",
-    str(par),
-    "--completions",
-    str(pods),
-]
-
-print(f"base directory: {base}")
-print(f"running with {pods} pods")
-print(f"max pods that can run concurrently: {par}")
-print(f"runner command: {cmd}")
-
-sp.run(cmd)
\ No newline at end of file