sb3_train.submit_file

#---------------------------------------------
# Name your batch so it's easy to distinguish in the q.
JobBatchName = "PBN RL PPO"

# ---------------------------------------------------
# Universe (vanilla, docker)
universe     = docker
docker_image = registry.eps.surrey.ac.uk/pbn-rl:ed941756

# -------------------------------------------------
# Event, out and error logs
log    = logs/$(exp).$(steps).$(seed).log
output = outs/$(exp).$(steps).$(seed).log
error  = logs/error/$(exp).$(steps).$(seed).log

# -----------------------------------
# File Transfer, Input, Output
# should_transfer_files = YES

environment = "mount=$ENV(PWD) WANDB_API_KEY=$ENV(WANDB_API_KEY) WANDB_RUN_GROUP=$(group)"
requirements = (CUDAGlobalMemoryMb > 3000) && \
               (HasStornext) && \
               (CUDACapability > 7.0)


# --------------------------------------
# Resources
request_CPUs   = 1
request_memory = 3G
request_GPUs   = 1
+GPUMem        = 4000

# -----------------------------------
# Queue commands. We can use variables and flags to launch our command with multiple options (as you would from the command line)
arguments = $(script) --seed $(seed) --time-steps $(steps) --env $(env) --exp-name $(exp) --resume-training --checkpoint-dir $ENV(PWD)/models --log-dir $ENV(PWD)/logs 

script = $ENV(PWD)/train_sb3.py
steps = 150000

env_n = 28
env = gym-PBN/Bittner-$(env_n)-v0
exp = ppo-default-$(env_n)
group = ppo-default-$(env_n)

+CanCheckpoint = true
+JobRunTime = 1

# Make the checkpoint location depend on the variables we use to run the model
queue 1 seed in 42 727 420 69 1337