Skip to content

Commit

Permalink
Update README
Browse files Browse the repository at this point in the history
  • Loading branch information
Pierre Delaunay committed Nov 21, 2024
1 parent a8415d3 commit d5cbbf5
Show file tree
Hide file tree
Showing 6 changed files with 90 additions and 1,279 deletions.
143 changes: 84 additions & 59 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -20,62 +20,23 @@ evaluating current and future hardware in a research environment.
* Focussed on training
* Ease of use
* Pytorch focused
* ROCm & NVIDIA
* ROCm, NVIDIA, Intel OneAPI, Habana Gaudi (Synapse)
* Independent

## Getting Started

The easiest way to run milabbench is to run it with one of its docker image.
It will include all of the necessary data


# Choose the image you want to use
export MILABENCH_IMAGE=ghcr.io/mila-iqia/milabench:cuda-nightly

# Pull the image we are going to run
docker pull $MILABENCH_IMAGE

# Run milabench
docker run -it --rm --ipc=host --gpus=all \
-v $(pwd)/results:/milabench/envs/runs \
$MILABENCH_IMAGE \
bash -c "milabench prepare && milabench run"

=================
Benchmark results
=================
fail n perf sem% std% peak_memory score weight
bert-fp16 0 8 155.08 0.3% 4.3% 24552 1241.260310 0.00
bert-fp32 0 8 29.52 0.0% 0.5% 31524 236.337218 0.00
bert-tf32 0 8 120.46 0.4% 6.1% 31524 964.713297 0.00
bert-tf32-fp16 0 8 154.76 0.3% 4.1% 24552 1238.477257 3.00
convnext_large-fp16 0 8 337.48 0.9% 14.0% 27658 2741.604444 0.00
convnext_large-fp32 0 8 44.61 0.8% 12.6% 49786 354.207225 0.00
convnext_large-tf32 0 8 135.99 0.7% 11.2% 49786 1089.394916 0.00
convnext_large-tf32-fp16 0 8 338.58 0.8% 13.0% 27658 2744.325170 3.00
davit_large 0 8 312.79 0.3% 6.7% 35058 2515.326450 1.00
davit_large-multi 0 1 2401.65 1.0% 7.7% 42232 2401.651720 5.00
dlrm 0 1 188777.20 1.8% 14.0% 3194 188777.203190 1.00
focalnet 0 8 400.47 0.2% 5.4% 26604 3215.431924 2.00
opt-1_3b 0 1 26.71 0.1% 0.4% 44116 26.714365 5.00
opt-1_3b-multinode 0 2 34.62 0.2% 1.0% 43552 34.618292 10.00
opt-6_7b 0 1 14.32 0.0% 0.1% 55750 14.319587 5.00
opt-6_7b-multinode 0 2 10.79 0.1% 0.7% 49380 10.792595 10.00
reformer 0 8 61.70 0.0% 0.9% 25376 494.110834 1.00
regnet_y_128gf 0 8 99.96 0.2% 5.0% 31840 803.012507 2.00
resnet152 0 8 710.18 0.3% 6.2% 36732 5710.828608 1.00
resnet152-multi 0 1 5367.34 1.0% 8.1% 38638 5367.338469 5.00
resnet50 0 8 984.43 0.9% 19.1% 5026 7927.257351 1.00
rwkv 0 8 428.65 0.2% 3.8% 5546 3435.097716 1.00
stargan 0 8 51.32 1.8% 40.8% 37848 413.238870 1.00
super-slomo 0 8 41.63 0.1% 2.3% 34082 332.395065 1.00
t5 0 8 48.05 0.2% 3.9% 35466 384.317023 2.00
whisper 0 8 248.16 0.0% 0.6% 37006 1985.861017 1.00

Scores
------
Failure rate: 0.00% (PASS)
Score: 219.06

git clone https://github.com/mila-iqia/milabench.git

pip install -e milabench

export MILABENCH_GPU_ARCH=cuda

milabench install --base workspace --config milabench/config/standard.yaml --select fp32

milabench prepare --base workspace --config milabench/config/standard.yaml --select fp32

milabench run --base workspace --config milabench/config/standard.yaml --select fp32


## Details
Expand All @@ -84,13 +45,77 @@ The benchmark suite has been validated on the following configurations:

| Python version | GPU | Configuration file |
| - | - | - |
| 3.10 (conda) | 2 node x 8xNVIDIA A100 80GB | config/standard.yaml |
| 3.9.12 (conda) | 8x NVIDIA RTX8000 48GB | config/standard.yaml |
| 3.9.16 (conda) | 2x NVIDIA K80 | config/ci.yaml |
| 3.9.16 (conda) | 2x AMD MI100 | config/ci.yaml |
| 3.9.16 (conda) | 4x AMD MI250 | config/standard.yaml |
| 3.10 | 2 node x 8xNVIDIA A100 80GB | config/standard.yaml |
| 3.10 | 2 node x 8xMI300X | config/standard.yaml |
| 3.10 | 1 node x 8xGaudi2 | config/standard.yaml |

We are working on validating it on more configurations and will update the above table as we do.



## Report

=================
Benchmark results
=================

System
------
cpu: AMD EPYC 7742 64-Core Processor
n_cpu: 128
product: NVIDIA A100-SXM4-80GB
n_gpu: 8
memory: 81920.0

Breakdown
---------
bench | fail | n | ngpu | perf | sem% | std% | peak_memory | score | weight
brax | 0 | 1 | 8 | 730035.71 | 0.1% | 0.4% | 2670 | 730035.71 | 1.00
diffusion-gpus | 0 | 1 | 8 | 117.67 | 1.5% | 11.7% | 59944 | 117.67 | 1.00
diffusion-single | 0 | 8 | 1 | 25.02 | 0.8% | 17.9% | 53994 | 202.10 | 1.00
dimenet | 0 | 8 | 1 | 366.85 | 0.7% | 16.2% | 2302 | 2973.32 | 1.00
dinov2-giant-gpus | 0 | 1 | 8 | 445.68 | 0.4% | 3.0% | 69614 | 445.68 | 1.00
dinov2-giant-single | 0 | 8 | 1 | 53.54 | 0.4% | 9.5% | 74646 | 432.65 | 1.00
dqn | 0 | 8 | 1 | 23089954554.91 | 1.1% | 89.9% | 62106 | 184480810548.20 | 1.00
bf16 | 0 | 8 | 1 | 293.43 | 0.2% | 6.3% | 1788 | 2361.16 | 0.00
fp16 | 0 | 8 | 1 | 289.26 | 0.1% | 3.6% | 1788 | 2321.65 | 0.00
fp32 | 0 | 8 | 1 | 19.14 | 0.0% | 0.7% | 2166 | 153.21 | 0.00
tf32 | 0 | 8 | 1 | 146.63 | 0.1% | 3.6% | 2166 | 1177.04 | 0.00
bert-fp16 | 0 | 8 | 1 | 263.73 | 1.1% | 16.7% | nan | 2165.37 | 0.00
bert-fp32 | 0 | 8 | 1 | 44.84 | 0.6% | 9.6% | 21170 | 364.52 | 0.00
bert-tf32 | 0 | 8 | 1 | 141.95 | 0.9% | 14.1% | 1764 | 1162.94 | 0.00
bert-tf32-fp16 | 0 | 8 | 1 | 265.04 | 1.0% | 15.6% | nan | 2175.59 | 3.00
reformer | 0 | 8 | 1 | 62.29 | 0.3% | 6.0% | 25404 | 501.89 | 1.00
t5 | 0 | 8 | 1 | 51.40 | 0.5% | 9.9% | 34390 | 416.14 | 2.00
whisper | 0 | 8 | 1 | 481.95 | 1.0% | 21.4% | 8520 | 3897.53 | 1.00
lightning | 0 | 8 | 1 | 680.22 | 1.0% | 22.7% | 27360 | 5506.90 | 1.00
lightning-gpus | 0 | 1 | 8 | 3504.74 | 7.9% | 62.9% | 28184 | 3504.74 | 1.00
llava-single | 1 | 8 | 1 | 2.28 | 0.4% | 9.6% | 72556 | 14.12 | 1.00
llama | 0 | 8 | 1 | 484.86 | 4.4% | 80.0% | 27820 | 3680.86 | 1.00
llm-full-mp-gpus | 0 | 1 | 8 | 193.92 | 3.1% | 16.2% | 48470 | 193.92 | 1.00
llm-lora-ddp-gpus | 0 | 1 | 8 | 16738.58 | 0.4% | 2.0% | 36988 | 16738.58 | 1.00
llm-lora-mp-gpus | 0 | 1 | 8 | 1980.63 | 2.2% | 11.8% | 55972 | 1980.63 | 1.00
llm-lora-single | 0 | 8 | 1 | 2724.95 | 0.2% | 3.0% | 49926 | 21861.99 | 1.00
ppo | 0 | 8 | 1 | 3114264.32 | 1.6% | 57.2% | 62206 | 24915954.98 | 1.00
recursiongfn | 0 | 8 | 1 | 7080.67 | 1.2% | 27.1% | 10292 | 57038.34 | 1.00
rlhf-gpus | 0 | 1 | 8 | 6314.94 | 2.1% | 11.2% | 21730 | 6314.94 | 1.00
rlhf-single | 0 | 8 | 1 | 1143.72 | 0.4% | 8.4% | 19566 | 9174.52 | 1.00
focalnet | 0 | 8 | 1 | 375.07 | 0.7% | 14.9% | 23536 | 3038.83 | 2.00
torchatari | 0 | 8 | 1 | 5848.88 | 0.6% | 12.7% | 3834 | 46613.34 | 1.00
convnext_large-fp16 | 0 | 8 | 1 | 330.93 | 1.5% | 22.9% | 27376 | 2711.46 | 0.00
convnext_large-fp32 | 0 | 8 | 1 | 59.49 | 0.6% | 9.8% | 55950 | 483.84 | 0.00
convnext_large-tf32 | 0 | 8 | 1 | 155.41 | 0.9% | 14.3% | 49650 | 1273.31 | 0.00
convnext_large-tf32-fp16 | 0 | 8 | 1 | 322.28 | 1.6% | 24.5% | 27376 | 2637.88 | 3.00
regnet_y_128gf | 0 | 8 | 1 | 119.46 | 0.5% | 10.0% | 29762 | 966.96 | 2.00
resnet152-ddp-gpus | 0 | 1 | 8 | 3843.06 | 5.2% | 39.3% | 27980 | 3843.06 | 0.00
resnet50 | 0 | 8 | 1 | 932.95 | 2.4% | 52.2% | 14848 | 7524.25 | 1.00
resnet50-noio | 0 | 8 | 1 | 1163.88 | 0.3% | 6.7% | 27480 | 9385.35 | 0.00
vjepa-gpus | 0 | 1 | 8 | 130.13 | 5.9% | 46.8% | 64244 | 130.13 | 1.00
vjepa-single | 0 | 8 | 1 | 21.29 | 1.0% | 22.4% | 58552 | 172.11 | 1.00

Scores
------
Failure rate: 0.38% (PASS)
Score: 4175.57

Errors
------
1 errors, details in HTML report.
6 changes: 3 additions & 3 deletions milabench/_version.py
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
"""This file is generated, do not modify"""

__tag__ = "v1.0.0_RC1-18-g784b38e"
__commit__ = "784b38e77b90116047e3de893c22c2f7d3225179"
__date__ = "2024-10-18 15:58:46 +0000"
__tag__ = "v0.1.0-146-ga8415d3"
__commit__ = "a8415d3da9f91aa1ac23d932dff2c70fe580e556"
__date__ = "2024-11-21 14:35:55 -0500"
9 changes: 3 additions & 6 deletions scripts/article/run_cuda.sh
View file
Original file line number Diff line number Diff line change
Expand Up @@ -84,15 +84,12 @@ if [ "$MILABENCH_PREPARE" -eq 0 ]; then

. $MILABENCH_WORDIR/env/bin/activate



# pip install torch
# milabench pin --variant cuda --from-scratch
# rm -rf $MILABENCH_WORDIR/results/venv/
rm -rf $MILABENCH_WORDIR/results/extra

milabench install --system $MILABENCH_WORDIR/system.yaml
milabench prepare --system $MILABENCH_WORDIR/system.yaml $ARGS
# rm -rf $MILABENCH_WORDIR/results/extra
# milabench install --system $MILABENCH_WORDIR/system.yaml
# milabench prepare --system $MILABENCH_WORDIR/system.yaml $ARGS

(
. $BENCHMARK_VENV/bin/activate
Expand Down
Loading

0 comments on commit d5cbbf5

Please sign in to comment.