# Rank Recorder
This is a useful tool to get the records of certain functions in each rank. The records of each rank will dump into a json file after the end of multiple process program. You can parse and visualize the json file easily.
Before using the tool, you should ensure dist.is_initialized() return true before exit of program.
## Usage
Is very simple:
```python
from colossalai.utils.rank_recorder import recorder
...
...
with recorder(record_name, current_rank) as r:
"""procedure to record
"""
```
## Example
This is a demo to display kernel select in cuda and visualize the cost of several procedures in each rank.
```python
import time
import os
import logging
logging.disable(logging.INFO)
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from colossalai.utils.rank_recorder import recorder
WORLD_SIZE = 4
# config the export image here
# If you want to dive into the detail, format 'svg' is recommended
recorder.export_format = 'png'
recorder.export_name = 'kernel_select'
recorder.dpi = 500
def calc(x, y):
a = torch.randn(x, y).cuda()
b = torch.randn(x, y).cuda()
c = sum(a * b)
return c
def worker(rank):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29020'
dist.init_process_group(backend='nccl', world_size=WORLD_SIZE, rank=rank)
print(dist.get_rank(), "enter")
time.sleep(0.1 * rank)
with recorder("calc_1(x100)", rank) as r:
calc(100, 100)
with recorder("calc_2(x400)", rank) as r:
calc(400, 400)
with recorder("calc_2(x200)", rank) as r:
calc(200, 200)
if __name__ == "__main__":
mp.spawn(worker, nprocs=WORLD_SIZE)
```
run the script directly and you will get `kernel_select.json` and `kernel_select.png` in your current folder.
