`ezpz` 🍋

Sam Foreman
2024-05-14

👀 Overview

ezpz 🍋

Launch, train and communicate across all your accelerators, ezpz.

Full support for your favorite framework + backend combo ❤️.

ezpz simplifies the process of:

Setting up + launching distributed training:

import ezpz as ez

RANK = ez.setup_torch(backend=backend) for backend $\in$ {DDP, deepspeed, horovod}

RANK = ez.get_rank()

LOCAL_RANK = ez.get_local_rank()

WORLD_SIZE = ez.get_world_size()

(see ezpz/dist.py for more details).

Using your favorite framework:

framework=pytorch + backend={DDP, deepspeed, horovod}

framework=tensorflow + backend=horovod

ez.get_torch_device(): {cuda, xpu, mps, cpu}

ez.get_torch_backend(): {nccl, ccl, gloo}

2ez 😎. (see frameworks for additional details)
Writing device agnostic code:
ezpz.get_torch_device()
>>> import ezpz as ez
>>> DEVICE = ez.get_torch_device()
>>> model = torch.nn.Linear(10, 10)
>>> model.to(DEVICE)
>>> x = torch.randn((10, 10), device=DEVICE)
>>> y = model(x)
>>> y.device
device(type='mps', index=0)
Using wandb:

ez.setup_wandb(project_name='ezpz')

Full support for any {device + framework + backend}:

device: {GPU, XPU, MPS, CPU}

framework: {torch, deepspeed, horovod, tensorflow}

backend: {DDP, deepspeed, horovod}

📝 Example

Important

We walk through a complete example below that will:

Install ezpz
launch test_dist.py across all the GPUs in your active {PBS, slurm} job

test_dist.py

import os
import logging
import time
from typing import Optional
import torch
import ezpz as ez

# backend can be any of DDP, deespepeed, horovod
RANK = ez.setup_torch(
  backend=(
      backend := os.environ.get('BACKEND', 'DDP')
  ),
  port=(
      port := os.environ.get("MASTER_PORT", "29500")
  )
)
# RANK = DIST_INIT['rank']
# WORLD_SIZE = DIST_INIT['world_size']
# LOCAL_RANK = DIST_INIT['local_rank']
# if DEVICE == "cuda" and torch.cuda.is_available():
#     torch.cuda.set_device(LOCAL_RANK)
DEVICE = ez.get_torch_device()
WORLD_SIZE = ez.get_world_size()
LOCAL_RANK = ez.get_local_rank()
DEVICE_ID = f"{DEVICE}:{LOCAL_RANK}"


# log only from RANK == 0
logger = logging.getLogger(__name__)
logger.setLevel("INFO") if RANK == 0 else logger.setLevel("CRITICAL")

BATCH_SIZE = int(os.environ.get("BATCH_SIZE", 64))  # 64
INPUT_SIZE = int(os.environ.get("INPUT_SIZE", 128))  # 128
OUTPUT_SIZE = int(os.environ.get("OUTPUT_SIZE", 128))  # 128
DTYPE = os.environ.get("DTYPE", torch.get_default_dtype())
TRAIN_ITERS = int(os.environ.get("TRAIN_ITERS", 50))

# logger.info(f"{DIST_INIT=}")


class Network(torch.nn.Module):
  def __init__(
          self,
          input_dim: int = 128,
          output_dim: int = 128,
          sizes: Optional[list[int]] = None,
  ):
      super(Network, self).__init__()
      if sizes is None:
          self.layers = torch.nn.Linear(input_dim, output_dim)
      elif len(sizes) > 0:
          layers = [torch.nn.Linear(input_dim, sizes[0])]
          for idx, size in enumerate(sizes[1:]):
              layers.append(
                  torch.nn.Linear(sizes[idx], size)
              )
          layers.append(torch.nn.Linear(sizes[-1], output_dim))
          self.layers = torch.nn.Sequential(*layers)

  def forward(self, x: torch.Tensor) -> torch.Tensor:
      return self.layers(x)


def calc_loss(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
  return (y - x).pow(2).sum()


def plot_losses(losses: dict) -> None:
  import plotext as pltx
  # y = list(losses.values())
  pltx.theme('clear')
  pltx.scatter(list(losses.values()))
  pltx.show()
  pltx.save_fig("test_dist_losses.txt")
  pltx.ylabel("loss")
  pltx.xlabel("iteration")


def main():
  model = Network(
      input_dim=INPUT_SIZE,
      output_dim=OUTPUT_SIZE,
      sizes=[1024, 512, 256, 128]
  )
  model.to(DEVICE)
  model.to(DEVICE_ID)
  logger.info(f'{model=}')
  optimizer = torch.optim.Adam(model.parameters())
  if backend.lower() == 'ddp':
      if WORLD_SIZE > 1:
          from torch.nn.parallel import DistributedDataParallel as DDP
          model = DDP(
              model,
              device_ids=[]
          )
  elif backend.lower() in ('ds', 'deepspeed'):
      import deepspeed
      # config = ez.load_ds_config().update(
      #     {"train_micro_batch_size_per_gpu": BATCH_SIZE}
      # )
      import argparse
      parser = argparse.ArgumentParser(
          description='My training script.'
      )
      parser.add_argument(
          '--local_rank',
          required=False,
          type=int,
          default=-1,
          # default=ez.get_local_rank()),
          help='local rank passed from distributed launcher',
      )
      # Include DeepSpeed configuration arguments
      parser = deepspeed.add_config_arguments(parser)
      cmd_args = parser.parse_args()
      logger.info(f'{cmd_args=}')
      model, optimizer, *_ = deepspeed.initialize(
          args=cmd_args,
          model=model,
          optimizer=optimizer,
      )

  losses = {}
  for iter in range(TRAIN_ITERS):
      t0 = time.perf_counter()
      x = torch.rand((BATCH_SIZE, INPUT_SIZE), dtype=DTYPE).to(DEVICE)
      y = model(x)
      loss = calc_loss(x, y)
      losses[iter] = loss
      dtf = ((t1 := time.perf_counter()) - t0)
      if backend == 'deepspeed':
          model.backward(loss)
          model.step(loss)
      else:
          loss.backward()
          optimizer.step()
      optimizer.zero_grad()
      dtb = time.perf_counter() - t1
      logger.info(
          ', '.join([
              f'{iter=}',
              f'loss={loss.item():.5f}',
              f'dt={dtf+dtb:.3f}',
              f'{dtf=:.3f}',
              f'{dtb=:.3f}'
          ])
      )
  if RANK == 0:
      plot_losses(losses)


if __name__ == '__main__':
  main()

Running 🏃🏻‍♂️

git clone + pip install ezpz:

$ git clone https://github.com/saforem2/ezpz
$ python3 -m pip install -e ezpz

[optional] If using PBS or slurm:

Save Job info:

savejobenv:

$ source ezpz/src/ezpz/bin/savejobenv
┌───────────────────────────────────────────────────────────────────
│ Writing PBS vars to /home/foremans/.pbsenv
│ HOSTFILE: /var/spool/pbs/aux/8992614.amn-0001
│ NHOSTS: 2
│ NGPU_PER_HOST: 12 GPUs per host
│ NGPUS: 24 GPUs total
└───────────────────────────────────────────────────────────────────
┌───────────────────────────────────────────────────────────────────
│ [DIST INFO]:
│   • Writing Job info to /home/foremans/.pbsenv
│     • HOSTFILE: /var/spool/pbs/aux/8992614.amn-0001
│     • NHOSTS: 2
│     • NGPU_PER_HOST: 12
│     • NGPUS = (NHOSTS * NGPU_PER_HOST) = 24
└──────────────────────────────────────────────────────────────────
┌──────────────────────────────────────────────────────────────────
│ [Hosts]:
│       • x1921c0s0b0n0.hostmgmt2000.cm.americas.sgi.com, x1921c0s2b0n0.hostmgmt2000.cm.americas.sgi.com
│     • [host:0] - x1921c0s0b0n0.hostmgmt2000.cm.americas.sgi.com
│     • [host:1] - x1921c0s2b0n0.hostmgmt2000.cm.americas.sgi.com
└──────────────────────────────────────────────────────────────────
┌────────────────────────────────────────────────────────────────────────────────
│ YOU ARE HERE: /home/foremans
│ Run 'source ./bin/getjobenv' in a NEW SHELL to automatically set env vars
└────────────────────────────────────────────────────────────────────────────────
┌──────────────────────────────────────────────────────────────────
│ [Launch]:
│     • Use: 'launch' (=mpiexec --verbose --envall -n 24 -ppn 12 --hostfile /var/spool/pbs/aux/8992614.amn-0001)
│       to launch job
└───────────────────────────────────────────────────────────────────

this will automatically define a launch alias:

┌──────────────────────────────────────────────────────────────────
│ [Launch]:
│     • Use: 'launch' (=mpiexec --verbose --envall -n 24 -ppn 12 --hostfile /var/spool/pbs/aux/8992614.amn-0001)
│       to launch job
└───────────────────────────────────────────────────────────────────

Launch test_dist.py:

DDP:
```
$ launch python3 -m ezpz.test_dist
```

DeepSpeed:

$ BACKEND=deepspeed launch python3 -m ezpz.test_dist --deepspeed --deepspeed_config ezpz/src/ezpz/conf/ds_config.json

Output:

GPU

$ launch python3 -m ezpz.test_dist |& tee ezpz-test-dist.log

Connected to tcp://x3005c0s13b0n0.hsn.cm.polaris.alcf.anl.gov:7919
Found executable /lus/eagle/projects/datascience/foremans/miniconda3/envs/2024-04-20/bin/python3
Launching application 9e4c8311-1729-4385-b1d2-d4cd6006ac1d
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=1/7][local_rank=1/3][node=1/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=5/7][local_rank=1/3][node=1/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=3/7][local_rank=3/3][node=1/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=7/7][local_rank=3/3][node=1/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=4/7][local_rank=0/3][node=0/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=6/7][local_rank=2/3][node=0/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=2/7][local_rank=2/3][node=0/1]
[2024-04-20 19:26:22][INFO][dist:290] - [device='cuda'][rank=0/7][local_rank=0/3][node=0/1]
[2024-04-20 19:26:22][WARNING][dist:296] - Using [8 / 8] available "cuda" devices !!
[2024-04-20 19:26:22][INFO][test_dist:46] - DIST_INIT={'world_size': 8, 'rank': 0, 'local_rank': 0}
[2024-04-20 19:26:24][INFO][test_dist:84] - model=Network(
  (layers): Sequential(
    (0): Linear(in_features=128, out_features=1024, bias=True)
    (1): Linear(in_features=1024, out_features=512, bias=True)
    (2): Linear(in_features=512, out_features=256, bias=True)
    (3): Linear(in_features=256, out_features=128, bias=True)
    (4): Linear(in_features=128, out_features=128, bias=True)
  )
)
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=0, loss=2789.99072, dt=0.664, dtf=0.659, dtb=0.005
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=1, loss=1961.33459, dt=0.002, dtf=0.001, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=2, loss=1450.47461, dt=0.002, dtf=0.000, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=3, loss=1088.81958, dt=0.002, dtf=0.000, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=4, loss=945.28839, dt=0.002, dtf=0.000, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=5, loss=906.78857, dt=0.002, dtf=0.000, dtb=0.001
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=6, loss=789.18243, dt=0.002, dtf=0.000, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=7, loss=751.63477, dt=0.002, dtf=0.000, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=8, loss=735.62915, dt=0.002, dtf=0.000, dtb=0.002
[2024-04-20 19:26:28][INFO][test_dist:126] - iter=9, loss=732.12775, dt=0.002, dtf=0.000, dtb=0.001

XPU

# [04:50:57 PM] [foremans@x1921c0s0b0n0] ~/q/llm.devkit/Megatron-DeepSpeed/dep/ezpz/s/ezpz  main q4-drop 32s
$ launch python3 -Wignore test_dist.py
Connected to tcp://x1921c0s0b0n0.hostmgmt2000.cm.americas.sgi.com:7919
Found executable /home/foremans/miniconda3/envs/q4-drop/bin/python3
Launching application 5bf3e9e8-89fb-412a-a49e-3c81601436b7
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=9/23][local_rank=9/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=14/23][local_rank=2/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=3/23][local_rank=3/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=17/23][local_rank=5/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=6/23][local_rank=6/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=13/23][local_rank=1/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=7/23][local_rank=7/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=19/23][local_rank=7/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=8/23][local_rank=8/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=21/23][local_rank=9/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=10/23][local_rank=10/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=22/23][local_rank=10/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=11/23][local_rank=11/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=23/23][local_rank=11/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=2/23][local_rank=2/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=20/23][local_rank=8/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=4/23][local_rank=4/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=15/23][local_rank=3/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=18/23][local_rank=6/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=12/23][local_rank=0/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=1/23][local_rank=1/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=16/23][local_rank=4/11][node=0/1]
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=5/23][local_rank=5/11][node=1/1]
[2024-04-19 16:51:06][INFO][dist:239] - DistInfo={
    "DEVICE": "xpu",
    "DEVICE_ID": "xpu:0",
    "DISTRIBUTED_BACKEND": "ccl",
    "GPUS_PER_NODE": 12,
    "HOSTFILE": "/var/spool/pbs/aux/8992337.amn-0001",
    "HOSTNAME": "x1921c0s0b0n0.hostmgmt2000.cm.americas.sgi.com",
    "HOSTS": "['x1921c0s0b0n0', 'x1921c0s5b0n0']",
    "LOCAL_RANK": 0,
    "MACHINE": "SunSpot",
    "NGPUS": 24,
    "NODE_ID": 0,
    "NUM_NODES": 2,
    "RANK": 0,
    "SCHEDULER": "PBS",
    "WORLD_SIZE_IN_USE": 24,
    "WORLD_SIZE_TOTAL": 24
}
[2024-04-19 16:51:06][INFO][dist:602] - Using oneccl_bindings from: /lus/gila/projects/Aurora_deployment/foremans/q4-drop_sunspot/llm.devkit/torch-ccl/oneccl_bindings_for_pytorch/__init__.py
[2024-04-19 16:51:06][INFO][dist:604] - Using ipex from: /home/foremans/miniconda3/envs/q4-drop/lib/python3.9/site-packages/intel_extension_for_pytorch/__init__.py
[2024-04-19 16:51:06][INFO][dist:605] - [0/24] Using device='xpu' with backend='DDP' + 'ccl' for distributed training.
[2024-04-19 16:51:06][INFO][dist:290] - [device='xpu'][rank=0/23][local_rank=0/11][node=0/1]
[2024-04-19 16:51:06][WARNING][dist:296] - Using [24 / 24] available "xpu" devices !!
2024:04:19-16:51:06:(16909) |CCL_WARN| MPI was initialized externally, CCL-MPI specific environment is ignored
[2024-04-19 16:51:06][INFO][test_dist:71] - model=Network(
  (layers): Sequential(
    (0): Linear(in_features=128, out_features=1024, bias=True)
    (1): Linear(in_features=1024, out_features=512, bias=True)
    (2): Linear(in_features=512, out_features=256, bias=True)
    (3): Linear(in_features=256, out_features=128, bias=True)
    (4): Linear(in_features=128, out_features=128, bias=True)
  )
)
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=0, loss=2709.53418, dt=1.380, dtf=0.950, dtb=0.430
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=1, loss=2058.49805, dt=0.133, dtf=0.002, dtb=0.131
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=2, loss=1507.91187, dt=0.004, dtf=0.001, dtb=0.004
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=3, loss=1181.78577, dt=0.004, dtf=0.001, dtb=0.003
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=4, loss=949.43561, dt=0.004, dtf=0.001, dtb=0.003
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=5, loss=848.14905, dt=0.004, dtf=0.001, dtb=0.003
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=6, loss=788.76123, dt=0.004, dtf=0.001, dtb=0.003
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=7, loss=753.59509, dt=0.004, dtf=0.001, dtb=0.003
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=8, loss=750.62225, dt=0.004, dtf=0.001, dtb=0.003
[2024-04-19 16:51:18][INFO][test_dist:101] - iter=9, loss=740.23474, dt=0.004, dtf=0.001, dtb=0.003
Application 5bf3e9e8 resources: utime=621s stime=111s maxrss=1746816KB inblock=192 oublock=16 minflt=10719359 majflt=7493 nvcsw=169332 nivcsw=77546

CPU

$ TORCH_DEVICE=cpu mpirun -np 12 python3 test_dist.py
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=1/11][local_rank=1/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=3/11][local_rank=3/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=6/11][local_rank=6/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=5/11][local_rank=5/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=2/11][local_rank=2/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=10/11][local_rank=10/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=4/11][local_rank=4/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=7/11][local_rank=7/11][node=0/0]
[2024-04-19 14:44:12][INFO][dist:290] - [device='cpu'][rank=9/11][local_rank=9/11][node=0/0]
[2024-04-19 14:44:13][INFO][dist:290] - [device='cpu'][rank=11/11][local_rank=11/11][node=0/0]
[2024-04-19 14:44:13][INFO][dist:290] - [device='cpu'][rank=8/11][local_rank=8/11][node=0/0]
[2024-04-19 14:44:13][INFO][dist:239] - DistInfo={
    "DEVICE": "cpu",
    "DEVICE_ID": "cpu:0",
    "DISTRIBUTED_BACKEND": "gloo",
    "GPUS_PER_NODE": 12,
    "HOSTFILE": "/Users/samforeman/projects/saforem2/ezpz/src/ezpz/hostfile",
    "HOSTNAME": "Sams-MacBook-Pro.local",
    "HOSTS": "['Sams-MacBook-Pro']",
    "LOCAL_RANK": 0,
    "MACHINE": "Sams-MacBook-Pro.local",
    "NGPUS": 12,
    "NODE_ID": 0,
    "NUM_NODES": 1,
    "RANK": 0,
    "SCHEDULER": "LOCAL",
    "WORLD_SIZE_IN_USE": 12,
    "WORLD_SIZE_TOTAL": 12
}
[2024-04-19 14:44:13][INFO][dist:605] - [0/12] Using device='cpu' with backend='DDP' + 'gloo' for distributed training.
[2024-04-19 14:44:13][INFO][dist:290] - [device='cpu'][rank=0/11][local_rank=0/11][node=0/0]
[2024-04-19 14:44:13][WARNING][dist:296] - Using [12 / 12] available "cpu" devices !!
[2024-04-19 14:44:13][INFO][test_dist:72] - model=Network(
  (layers): Sequential(
    (0): Linear(in_features=128, out_features=1024, bias=True)
    (1): Linear(in_features=1024, out_features=512, bias=True)
    (2): Linear(in_features=512, out_features=256, bias=True)
    (3): Linear(in_features=256, out_features=128, bias=True)
    (4): Linear(in_features=128, out_features=128, bias=True)
  )
)
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=0, loss=2801.62549, dt=0.389, dtf=0.042, dtb=0.348
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=1, loss=2092.84692, dt=0.051, dtf=0.010, dtb=0.041
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=2, loss=1482.45520, dt=0.037, dtf=0.004, dtb=0.033
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=3, loss=1174.38037, dt=0.033, dtf=0.002, dtb=0.031
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=4, loss=938.39917, dt=0.032, dtf=0.003, dtb=0.030
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=5, loss=888.37390, dt=0.035, dtf=0.001, dtb=0.033
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=6, loss=784.63470, dt=0.036, dtf=0.003, dtb=0.032
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=7, loss=749.53839, dt=0.033, dtf=0.002, dtb=0.031
[2024-04-19 14:44:14][INFO][test_dist:102] - iter=8, loss=732.22656, dt=0.036, dtf=0.003, dtb=0.034
[2024-04-19 14:44:15][INFO][test_dist:102] - iter=9, loss=730.63776, dt=0.034, dtf=0.001, dtb=0.033
35.68s user 17.20s system 546% cpu 9.681s total

🧰 Helper Utilities

We provide some shell scripts that are useful when working with a job scheduler (e.g. PBS Pro @ ALCF or slurm elsewhere).

src/ezpz/bin/savejobenv:

Shell script to save relevant job related environment variables to a file which can be sourced from new login instances.

savejobenv

Launch a job, clone (or navigate into) ezpz, and source src/ezpz/bin/savejobenv:

(thetalogin4) $ qsub-gpu -A datascience -n 2 -q full-node --attrs="filesystems=home,grand,eagle,theta-fs0:ssds=required" -t 06:00 -I
Job routed to queue "full-node".
Wait for job 10155652 to start...
Opening interactive session to thetagpu04
[...]

(thetagpu04) $ git clone https://github.com/saforem2/ezpz
(thetagpu04) $ source ezpz/src/ezpz/bin/savejobenv
┌───────────────────────────────────────────────────────────────────
│ Writing COBALT vars to /home/foremans/.cobaltenv
│ HOSTFILE: /var/tmp/cobalt.10155652
│ NHOSTS: 2
│ 8 GPUs per host
│ 16 GPUs total
└───────────────────────────────────────────────────────────────────
┌───────────────────────────────────────────────────────────────────
│ [DIST INFO]:
│   • Writing Job info to /home/foremans/.cobaltenv
│     • HOSTFILE: /var/tmp/cobalt.10155652
│     • NHOSTS: 2
│     • NGPU_PER_HOST: 8
│     • NGPUS = (NHOSTS * NGPU_PER_HOST) = 16
│ [Hosts]:
│       • thetagpu04 thetagpu19
│ [Launch]:
│     • Use: 'launch' (=mpirun -n  -N  --hostfile /var/tmp/cobalt.10155652 -x PATH -x LD_LIBRARY_PATH)
│       to launch job
└───────────────────────────────────────────────────────────────────
┌────────────────────────────────────────────────────────────────────────────────
│ YOU ARE HERE: /home/foremans
│ Run 'source ./bin/getjobenv' in a NEW SHELL to automatically set env vars
└────────────────────────────────────────────────────────────────────────────────

src/ezpz/bin/getjobenv:

Shell script that, when sourced, will populate the current environment with the necessary job-related variables.

getjobenv

Now, in a NEW SHELL

(localhost)   $ ssh <user>@theta

(thetalogin4) $ ssh thetagpu19

(thetagpu19)  $ module load conda/2023-01-11; conda activate base
(thetagpu19)  $ cd ezpz
(thetagpu19)  $ source ./src/ezpz/bin/getjobenv
┌──────────────────────────────────────────────────────────────────
│ [Hosts]: 
│     • thetagpu04, thetagpu19
└──────────────────────────────────────────────────────────────────
┌──────────────────────────────────────────────────────────────────
│ [DIST INFO]: 
│     • Loading job env from: /home/foremans/.cobaltenv
│     • HOSTFILE: /var/tmp/cobalt.10155652
│     • NHOSTS: 2
│     • NGPU_PER_HOST: 8
│     • NGPUS (NHOSTS x NGPU_PER_HOST): 16
│     • DIST_LAUNCH: mpirun -n 16 -N 8 --hostfile /var/tmp/cobalt.10155652 -x PATH -x LD_LIBRARY_PATH
│     • Defining alias: launch: aliased to mpirun -n 16 -N 8 --hostfile /var/tmp/cobalt.10155652 -x PATH -x LD_LIBRARY_PATH
└──────────────────────────────────────────────────────────────────
(thetagpu19) $ mkdir -p venvs/thetaGPU/2023-01-11
(thetagpu19) $ python3 -m venv venvs/thetaGPU/2023-01-11 --system-site-packages
(thetagpu19) $ source venvs/thetaGPU/2023-01-11/bin/activate
(thetagpu19) $ python3 -m pip install -e . --require-virtualenv
(thetagpu19) $ launch python3 -m ezpz framework=pytorch backend=DDP
[2023-10-26 12:21:26,716][ezpz.dist][INFO] - Using DDP for distributed training
[2023-10-26 12:21:26,787][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 13
[2023-10-26 12:21:26,787][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 14
[2023-10-26 12:21:26,787][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 8
[2023-10-26 12:21:26,787][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 12
[2023-10-26 12:21:26,787][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 6
[2023-10-26 12:21:26,788][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 9
[2023-10-26 12:21:26,787][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 10
[2023-10-26 12:21:26,788][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 15
[2023-10-26 12:21:26,788][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 11
[2023-10-26 12:21:26,789][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 7
[2023-10-26 12:21:26,789][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 3
[2023-10-26 12:21:26,789][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 1
[2023-10-26 12:21:26,789][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 4
[2023-10-26 12:21:26,789][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 5
[2023-10-26 12:21:26,789][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 2
[2023-10-26 12:21:26,798][torch.distributed.distributed_c10d][INFO] - Added key: store_based_barrier_key:1 to store for rank: 0
[2023-10-26 12:21:26,811][torch.distributed.distributed_c10d][INFO] - Rank 14: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,812][torch.distributed.distributed_c10d][INFO] - Rank 6: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,814][torch.distributed.distributed_c10d][INFO] - Rank 13: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,815][torch.distributed.distributed_c10d][INFO] - Rank 7: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,816][torch.distributed.distributed_c10d][INFO] - Rank 8: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,817][torch.distributed.distributed_c10d][INFO] - Rank 3: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,819][torch.distributed.distributed_c10d][INFO] - Rank 12: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,820][torch.distributed.distributed_c10d][INFO] - Rank 1: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,821][torch.distributed.distributed_c10d][INFO] - Rank 10: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,823][torch.distributed.distributed_c10d][INFO] - Rank 4: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,825][torch.distributed.distributed_c10d][INFO] - Rank 9: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,825][torch.distributed.distributed_c10d][INFO] - Rank 5: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,827][torch.distributed.distributed_c10d][INFO] - Rank 15: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,828][torch.distributed.distributed_c10d][INFO] - Rank 2: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,830][torch.distributed.distributed_c10d][INFO] - Rank 11: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:26,831][torch.distributed.distributed_c10d][INFO] - Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 16 nodes.
[2023-10-26 12:21:27,035][ezpz.dist][INFO] - RANK: 0 / 15
{
  "framework": "pytorch",
  "backend": "DDP",
  "use_wandb": false,
  "seed": null,
  "port": null,
  "ds_config_path": null,
  "wandb_project_name": null,
  "precision": null,
  "ngpus": null
}
[2023-10-26 12:21:27,038][__main__][INFO] - Output dir: /lus/grand/projects/datascience/foremans/locations/thetaGPU/projects/saforem2/ezpz/outputs/runs/pytorch/DDP/2023-10-26/12-21-25
[2023-10-26 12:21:27,097][ezpz.dist][INFO] - RANK: 8 / 15
[2023-10-26 12:21:27,103][ezpz.dist][INFO] - RANK: 6 / 15
[2023-10-26 12:21:27,104][ezpz.dist][INFO] - RANK: 14 / 15
[2023-10-26 12:21:27,111][ezpz.dist][INFO] - RANK: 13 / 15
[2023-10-26 12:21:27,116][ezpz.dist][INFO] - RANK: 1 / 15
[2023-10-26 12:21:27,126][ezpz.dist][INFO] - RANK: 7 / 15
[2023-10-26 12:21:27,135][ezpz.dist][INFO] - RANK: 10 / 15
[2023-10-26 12:21:27,139][ezpz.dist][INFO] - RANK: 12 / 15
[2023-10-26 12:21:27,141][ezpz.dist][INFO] - RANK: 9 / 15
[2023-10-26 12:21:27,141][ezpz.dist][INFO] - RANK: 15 / 15
[2023-10-26 12:21:27,141][ezpz.dist][INFO] - RANK: 11 / 15
[2023-10-26 12:21:27,141][ezpz.dist][INFO] - RANK: 5 / 15
[2023-10-26 12:21:27,144][ezpz.dist][INFO] - RANK: 2 / 15
[2023-10-26 12:21:27,145][ezpz.dist][INFO] - RANK: 4 / 15
[2023-10-26 12:21:27,145][ezpz.dist][INFO] - RANK: 3 / 15
16.56s user 30.05s system 706% cpu 6.595s total

while this example looked at ThetaGPU, the exact same process will work on any of {ThetaGPU, Polaris, Perlmutter}.

❤️‍🩹 Status
Last Updated: 05/13/2024 @ 22:04:56

Name		Name	Last commit message	Last commit date
Latest commit History 428 Commits
assets		assets
docs		docs
quarto		quarto
src/ezpz		src/ezpz
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
_quarto.yml		_quarto.yml
favicon.svg		favicon.svg
index.qmd		index.qmd
notes.md		notes.md
pyproject.toml		pyproject.toml
references.bib		references.bib
setup.cfg		setup.cfg

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