# 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 visualise 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 visualise 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.