p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] scripts_horeka/overfit_batch_tiny/train_lam_overfit_batch.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=15:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --mail-user=mihir.mahajan2002@gmail.com
#SBATCH --job-name=train_lam_minecraft_overfit_sample
#SBATCH --mem=50G
#SBATCH --mail-type=ALL

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

tf_records_dir=$ws_dir/knoms_tfrecords_500_shards
ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python train_lam.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=12 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=lam-tiny-overfit-batch-$slurm_job_id \
    --tags lam overfit-batch tiny \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir \
    --model_dim=192 \
    --latent_dim=8 \
    --num_latents=4 \
    --patch_size=16 \
    --num_blocks=3 \
    --num_heads=3 \
    --codebook_dropout=0.0