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

# Unload modules that may interfere
module unload mpi/openmpi/5.0
module unload devel/cuda/12.4

# Activate virtual environment
source .venv/bin/activate

# Set workspace and checkpoint directory (update slurm_job_id as needed)
ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared'
# Replace the following with the actual job id/checkpoint you want to sample from
slurm_job_id=3301029

# job_name=train_dynamics_minecraft_overfit_sample_tiny
CHECKPOINT_DIR=$ws_dir/checkpoints/${slurm_job_id}

# Example: If you want to use a specific checkpoint, set it here
# CHECKPOINT_PATH=$ws_dir/checkpoints/3299272/dynamics-tiny-overfit-big-lr-3299272_50000/
# Or use the latest in the directory
# CHECKPOINT_PATH=$(ls -d $CHECKPOINT_DIR/*/ | sort | tail -n 1)
CHECKPOINT_PATH=$CHECKPOINT_DIR/genie_1751067601_200000/
# CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/0000/genie_1751301068_2000/
# CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/../checkpoints/3307618/genie_1751322003_15500/
# CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3307619/genie_1751322003_200000/
CHECKPOINT_PATH=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3309699/genie_1751384516_74500/


echo "Sampling from checkpoint: $CHECKPOINT_PATH"

python sample.py \
    --checkpoint "$CHECKPOINT_PATH" \
    --tokenizer_dim=384 \
    --latent_patch_dim=32 \
    --num_patch_latents=1024 \
    --patch_size=4 \
    --tokenizer_num_blocks=8 \
    --tokenizer_num_heads=8 \
    --lam_dim=384 \
    --latent_action_dim=32 \
    --lam_patch_size=16 \
    --lam_num_blocks=8 \
    --lam_num_heads=8 \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4 \
    --maskgit_steps=1 \
    --num_latent_actions=6 \
    --seq_len=6 \
    --start_frame=0

# python sample.py \
    # --checkpoint "$CHECKPOINT_PATH" \
    # --data_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/coinrun_episodes
[content] genie.py (python)
        curr_masked_frame = jnp.where(
            jnp.expand_dims(mask, -1), # (B, N, 1)
            self.dynamics.mask_token[0], # (B, 1, D)
            vid_embed[:, -1], # (B, N, D)
        ) # (B, N, D)
[content] genie.py (python)

            # begin potential forloop (from T to S)
            print("token_idxs shape:", token_idxs.shape)
            pad_shape = (B, S - T, N)
            pad = jnp.zeros(pad_shape, dtype=token_idxs.dtype)
            token_idxs = jnp.concatenate([token_idxs, pad], axis=1) # shape (B, S, N)
[tab] scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_1_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=15:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node

# 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=hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord
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_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --min_lr=3e-4 \
    --max_lr=3e-4 \
    --log_image_interval=250 \
    --log \
    --name=tokenizer-batch-size-scaling-1-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 1-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir
[tab] scripts_horeka/batchsize_scaling/adjusted_lr/train_tokenizer_2_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=logs/logs_training/%x_%j.log
#SBATCH --error=logs/logs_training/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
#SBATCH --mem=100G

# 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=hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord
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_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=4.24e-4 \
    --max_lr=4.24e-4 \
    --log_image_interval=250 \
    --log \
    --name=tokenizer-batch-size-scaling-2-node-$slurm_job_id \
    --tags tokenizer batch-size-scaling 2-node \
    --entity instant-uv \
    --project jafar \
    --data_dir $tf_records_dir