p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] plot.ipynb (python)
import wandb
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

# --- 1. SET YOUR PROJECT DETAILS ---
# Replace with your entity (username or team name) and project name
ENTITY = "instant-uv"
PROJECT = "jafar"

# --- 2. DEFINE METRIC AND CONFIG KEYS ---
# The metric you want to plot (e.g., 'train/loss', 'validation_loss')
METRIC_NAME = "loss" 

RUN_TAGS = ["batch-size-scaling", "dynamics"]

# --- 3. INITIALIZE WANDB API ---
api = wandb.Api()

# --- 4. FETCH RUNS ---
# Construct a filter to get only the runs you care about
filters = {"$and": [{"tags": tag} for tag in RUN_TAGS]}

runs = api.runs(f"{ENTITY}/{PROJECT}", filters=filters)

print(f"Found {len(runs)} runs to process...")
[tab] experiments/train_dynamics_lr_1e-4_larger_ffn.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/%x_%j.log
#SBATCH --job-name=train_dynamics_lr_1e-4_larger_ffn

# Log the sbatch script
cat $0

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

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --tokenizer_ffn_dim=512 \
    --tokenizer_num_blocks=8 \
    --lam_ffn_dim=2048 \
    --lam_num_blocks=4 \
    --dyna_ffn_dim=2048 \
    --dyna_num_blocks=6 \
    --init_lr=0 \
    --max_lr=1.5e-4 \
    --log_image_interval=1000 \
    --log \
    --log_checkpoint_interval=1000 \
    --name=dynamics-lr-1e-4-larger-ffn \
    --tags dynamics lr-1e-4 larger-ffn mixed-precision flash-attention \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \