ICL_code/ICL_Jay_final/script/data_generate_test.sh

#!/bin/bash
#SBATCH --partition=scavenger-gpu
#SBATCH --exclude=dcc-youlab-gpu-28,dcc-gehmlab-gpu-56
#SBATCH --array=0-999  # 6 configurations to test
#SBATCH --ntasks=1
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=8G
#SBATCH --gres=gpu:1
#SBATCH --time=8:00:00
#SBATCH --output=/work/jf381/code/code/ICL_LOG/data_generation/%A_%a_%j.out
#SBATCH --error=/work/jf381/code/code/ICL_LOG/data_generation/%A_%a_%j.err
#SBATCH --requeue
set -e


# load_pretrain_model 0 
# rbf 1 2 4 971 067 971 

# 30,31,32 softmax use_cf
# 18,19,20 softmax use_ff
# exp 9 10 11
# 678 softmax
# nvidia-smi | grep 'python' | awk '{ print $5 }' | xargs -n1 kill -9

# SLURM_ARRAY_TASK_ID=3

# conda activate /work/jf381/cache/conda_envs/paul3100
# source activate /work/jf381/cache/conda_envs/paul31
# use SLURM_ARRAY_TASK_ID to control 
# cd /work/jf381/code/code/ICL_Jay


## pretrain useful for pe+lap
## add noise add performance
## test context size
## test model
# distribute 1000 tasks on 

# python /work/jf381/code/code/ICL_Jay/train_end2end_new.py --test_mode $SLURM_ARRAY_TASK_ID --n_epochs 3001 --data_type sphere --data_generation --slurm_id  --slurm_tasks

# n_epochs_stride
# SLURM_ARRAY_TASK_ID=0

#!/bin/bash

# 定义噪声水平列表

#!/bin/bash

# 获取SLURM任务ID

# SLURM_ARRAY_TASK_ID=34

# SLURM_ARRAY_TASK_ID=15
# SLURM_ID=${SLURM_ARRAY_TASK_ID:-0}
# SLURM_ARRAY_TASK_ID=15
SLURM_ID=15

# 设置数据类型

# 设置基础噪声水平（或从列表中选择）
add_noises=0  # 也可以从一个列表中基于SLURM_ID选择

# 使用SLURM_ID来确定模型配置
# 假设我们有以下变量组合:
# 1. n_layer_icl: 1, 2, 3, 4
# 2. kernel: 'linear', 'rbf', 'softmax', 'exp'
# 3. 使用选项: 无特殊选项, use_ff, use_ct

# /work/jf381/code/code/ICL_LOG/ICL_MODEL_TEST_TYPE2_Finetune_Pretrain_ENDEND
# NOW


# data_type="cylinder"
# test_data_type="cylinder"

# data_type="cylinder"
# test_data_type="cylinder"

# data_type="cylinder"
# test_data_type="cylinder"

# 计算层数 (1, 2, 4)
layer_options=(1 2 4)
layer_idx=$(( SLURM_ID % 3 ))
n_layer_icl=${layer_options[$layer_idx]}

# 计算kernel类型 (0-3)
kernel_idx=$(( (SLURM_ID / 3) % 4 ))
kernels=("linear" "rbf" "softmax" "exp")
kernel=${kernels[$kernel_idx]}

# 计算使用选项 (0-2)
option_idx=$(( (SLURM_ID / 12) % 3 ))
options=("" "--use_ff" "--use_cf")
option=${options[$option_idx]}

icl_type=2
echo "Running with configuration:"
echo "SLURM_ID = $SLURM_ID"
echo "n_layer_icl = $n_layer_icl"
echo "kernel = $kernel" 
echo "option = $option"
echo "icl_type = $icl_type"
batch_size=200

data_type="combine-5"
test_data_type=$data_type


SLURM_ARRAY_TASK_ID=0
# echo $SLURM_ARRAY_TASK_ID/
load_pretrain_model=0
test_mode=$load_pretrain_model
seed=2
context_size=40
query_size=60
n_samples=$((context_size+query_size))

# SLURM_ARRAY_TASK_ID=0
python /work/jf381/code/code/ICL_Jay/train_end2end_new.py \
    --test_mode $test_mode \
    --n_epochs 30001 \
    --data_type $data_type \
    --n_layer_icl $n_layer_icl \
    --test_data_type $test_data_type \
    --add_noises $add_noises \
    --kernel $kernel \
    --load_pretrain_model -1 \
    --fine_tune_n_epochs 31 \
    --icl_type $icl_type \
    --suffix n_samples_${n_samples}_context_size_${context_size} \
    --train_context_size $context_size \
    --test_context_size $context_size \
    --n_samples $n_samples \
    --seed $seed \
    --batch_size $batch_size \
    --data_generation \
    --slurm_id $SLURM_ARRAY_TASK_ID  \
    --slurm_tasks 1 \
    --batch_size $batch_size
# distribute 1000 tasks to generate 
# python /work/jf381/code/code/ICL_Jay/train_end2end_new.py --n_epochs 3001 --data_type swiss_roll --data_generation --slurm_id $SLURM_ARRAY_TASK_ID  --slurm_tasks 100 --batch_size 200