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---
# sidebar: false
title: "`ezpz` π"
lightbox: true
higlight-style: atom-one
# callout-appearance: simple
title-block-banner: false
citeproc: true
citation:
author: Sam Foreman
type: webpage
title: "`ezpz`"
url: https://saforem2.github.io/ezpz
editor:
render-on-save: true
execute:
freeze: auto
# twitter-card:
# image: "./assets/thumbnail.png"
# creator: "@saforem2"
# site: "@saforem2"
# open-graph:
# image: "./assets/thumbnail.png"
format:
html: default
ipynb: default
revealjs:
scrollable: true
output-file: "slides.html"
# pdf: default
gfm:
author: Sam Foreman
output-file: "README.md"
---
## π Overview
::: {.callout-tip icon=false title="<code>ezpz</code> π" collapse=false style="text-align: left!important; width:100%; background-color: var(--code-bg); border:none!important; border-left: 2px solid #1ED760!important; opacity:100%; border-radius: 0pt!important; margin-bottom: 0em; padding-bottom: 0.0rem;"}
Launch and train across all your accelerators, using your favorite framework +
backend combo.
`ezpz` simplifies the process of:
- <details><summary>Setting up + launching distributed training:</summary>
- <details closed><summary><code>import ezpz as ez</code></summary>
- `RANK = `
[`ez.setup_torch(backend=backend)`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#L551)
[for `backend` $\in$ \{`DDP`, `deepspeed`, `horovod`}]{.dim-text}
- `RANK =`
[`ez.get_rank()`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#396)
- `LOCAL_RANK =`
[`ez.get_local_rank()`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#448)
- `WORLD_SIZE =`
[`ez.get_world_size()`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#L417)
[(see [`ezpz/dist.py`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py) for more details).]{.dim-text}
</details>
</details>
- <details closed><summary>Using your favorite framework:</summary>
- `framework=pytorch` + `backend={DDP, deepspeed, horovod}`
- `framework=tensorflow` + `backend=horovod`
- [`ez.get_torch_device()`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#L332): {`cuda`, `xpu`, `mps`, `cpu`}
- [`ez.get_torch_backend()`](https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#L348): {`nccl`, `ccl`, `gloo`}
_2ez_ π. (see [frameworks](#frameworks) for additional details)
</details>
- <details closed><summary>Writing device agnostic code:</summary>
- <details><summary><a href="https://github.com/saforem2/ezpz/blob/main/src/ezpz/dist.py#L332"><code>ezpz.get_torch_device()</code></a></summary>
```python
>>> 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)
```
</details>
</details>
- <details closed><summary>Using <code>wandb</code>:</summary>
- `ez.setup_wandb(project_name='ezpz')`
</details>
- **Full support** for any {`device` + `framework` + `backend`}:
- device: {`GPU`, `XPU`, `MPS`, `CPU`}
- framework: {`torch`, `deepspeed`, `horovod`, `tensorflow`}
- backend: {`DDP`, `deepspeed`, `horovod`}
:::
## π Example
We provide below a complete example that will launch
[`test_dist.py`](./src/ezpz/test_dist.py) (included below) across all GPUs in
your current {`PBS`, `slurm`} job and train a simple model using either `DDP` or
`deepspeed`
<details closed><summary><code>test_dist.py</code></summary>
<!-- <a href="https://github.com/saforem2/ezpz/blob/main/src/ezpz/test_dist.py"><code>test_dist.py</code></a>:</summary> -->
```python
{{< include ./src/ezpz/test_dist.py >}}
```
</details>
### ππ»ββοΈ Running
1. `git clone` + `pip install ezpz`:
```bash
$ git clone https://github.com/saforem2/ezpz
$ python3 -m pip install -e ezpz
```
2. [\[optional\]]{.dim-text} If using `PBS` or `slurm`:
- <details closed><summary>Save Job info:</summary>
- [`savejobenv`](./src/ezpz/bin/savejobenv):
```bash
$ source ezpz/src/ezpz/bin/savejobenv
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Writing PBS vars to /home/foremans/.pbsenv
β HOSTFILE: /var/spool/pbs/aux/8992614.amn-0001
β NHOSTS: 2
β NGPU_PER_HOST: 12 GPUs per host
β NGPUS: 24 GPUs total
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [DIST INFO]:
β β’ Writing Job info to /home/foremans/.pbsenv
β β’ HOSTFILE: /var/spool/pbs/aux/8992614.amn-0001
β β’ NHOSTS: 2
β β’ NGPU_PER_HOST: 12
β β’ NGPUS = (NHOSTS * NGPU_PER_HOST) = 24
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [Hosts]:
β β’ x1921c0s0b0n0.hostmgmt2000.cm.americas.sgi.com, x1921c0s2b0n0.hostmgmt2000.cm.americas.sgi.com
β β’ [host:0] - x1921c0s0b0n0.hostmgmt2000.cm.americas.sgi.com
β β’ [host:1] - x1921c0s2b0n0.hostmgmt2000.cm.americas.sgi.com
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β YOU ARE HERE: /home/foremans
β Run 'source ./bin/getjobenv' in a NEW SHELL to automatically set env vars
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [Launch]:
β β’ Use: 'launch' (=mpiexec --verbose --envall -n 24 -ppn 12 --hostfile /var/spool/pbs/aux/8992614.amn-0001)
β to launch job
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
```
this will automatically define a `launch` alias:
```bash
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [Launch]:
β β’ Use: 'launch' (=mpiexec --verbose --envall -n 24 -ppn 12 --hostfile /var/spool/pbs/aux/8992614.amn-0001)
β to launch job
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
```
</details>
3. Launch [`test_dist.py`](./src/ezpz/test_dist.py):
- DDP:
```bash
$ launch python3 -m ezpz.test_dist
```
- DeepSpeed:
```bash
$ BACKEND=deepspeed launch python3 -m ezpz.test_dist --deepspeed --deepspeed_config ezpz/src/ezpz/conf/ds_config.json
```
- Output:
- <details closed><summary><code>GPU</code></summary>
```bash
$ 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
```
</details>
- <details closed><summary><code>XPU</code></summary>
```bash
# [04:50:57 PM] [foremans@x1921c0s0b0n0] ~/q/llm.devkit/Megatron-DeepSpeed/dep/ezpz/s/ezpz ο 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
```
</details>
- <details closed><summary><code>CPU</code></summary>
```bash
$ 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
```
</details>
## π§° 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`](./src/ezpz/bin/savejobenv):
Shell script to save relevant job related environment variables to a file
which can be `sourced` from new login instances.
- <details closed><summary><b><code>savejobenv</code></b></summary>
- Launch a job, clone (or navigate into) `ezpz`, and `source` [`src/ezpz/bin/savejobenv`](./src/ezpz/bin/savejobenv):
```bash
(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
[...]
```
```bash
(thetagpu04) $ git clone https://github.com/saforem2/ezpz
(thetagpu04) $ source ezpz/src/ezpz/bin/savejobenv
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Writing COBALT vars to /home/foremans/.cobaltenv
β HOSTFILE: /var/tmp/cobalt.10155652
β NHOSTS: 2
β 8 GPUs per host
β 16 GPUs total
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [DIST INFO]:
β β’ Writing Job info to /home/foremans/.cobaltenv
β β’ HOSTFILE: /var/tmp/cobalt.10155652
β β’ NHOSTS: 2
β β’ NGPU_PER_HOST: 8
β β’ NGPUS = (NHOSTS * NGPU_PER_HOST) = 16
β [Hosts]:
β β’ thetagpu04 thetagpu19
β [Launch]:
β β’ Use: 'launch' (=mpirun -n -N --hostfile /var/tmp/cobalt.10155652 -x PATH -x LD_LIBRARY_PATH)
β to launch job
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β YOU ARE HERE: /home/foremans
β Run 'source ./bin/getjobenv' in a NEW SHELL to automatically set env vars
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
```
</details>
- [`src/ezpz/bin/getjobenv`](./src/ezpz/bin/getjobenv):
Shell script that, when sourced, will populate the current environment with
the necessary job-related variables.
- <details closed><summary><b><code>getjobenv</code></b></summary>
- Now, in a **NEW SHELL**
```bash
(localhost) $ ssh <user>@theta
```
```bash
(thetalogin4) $ ssh thetagpu19
```
```bash
(thetagpu19) $ module load conda/2023-01-11; conda activate base
(thetagpu19) $ cd ezpz
(thetagpu19) $ source ./src/ezpz/bin/getjobenv
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [Hosts]:
β β’ thetagpu04, thetagpu19
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β [DIST INFO]:
β β’ Loading job env from: /home/foremans/.cobaltenv
β β’ HOSTFILE: /var/tmp/cobalt.10155652
β β’ NHOSTS: 2
β β’ NGPU_PER_HOST: 8
β β’ NGPUS (NHOSTS x NGPU_PER_HOST): 16
β β’ DIST_LAUNCH: mpirun -n 16 -N 8 --hostfile /var/tmp/cobalt.10155652 -x PATH -x LD_LIBRARY_PATH
β β’ Defining alias: launch: aliased to mpirun -n 16 -N 8 --hostfile /var/tmp/cobalt.10155652 -x PATH -x LD_LIBRARY_PATH
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
(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}`.
</details>
::: {.callout-tip icon=false title='[β€οΈβπ©Ή Status]{style="color: var(--ansi-red);"}' collapse=false style="text-align: left!important; width:100%; background-color: var(--code-bg); border:none!important; border-left: 2px solid var(--ansi-red)!important; opacity:100%; border-radius: 0pt!important;"}
```{python code-line-numbers="true"}
#| echo: false
import datetime
from rich import print
now = datetime.datetime.now()
day = now.strftime('%m/%d/%Y')
time = now.strftime('%H:%M:%S')
print(' '.join([
"[dim italic]Last Updated[/]:",
f"[#F06292]{day}[/]",
f"[dim]@[/]",
f"[#1A8FFF]{time}[/]"
]))
```
[[](https://hits.seeyoufarm.com)]{.center style="text-align:center;"}
:::