Benchmarking framework for Machine learning and Artificial Intelligence, geared toward evaluating current and future hardware in a research environment.
- Simple / Hands-off
- Wide selection of models on diverse applications
- Multi GPUs
- Multi node
- nlp / transformer / llm / rl / rnn
- vision / classification / convnet / resnet / transformer
- audio
- Docker Container
- Works on slurm
- Automatic batch resize
- Focussed on training
- Ease of use
- Pytorch focused
- ROCm, NVIDIA, Intel OneAPI, Habana Gaudi (Synapse)
- Independent
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
The benchmark suite has been validated on the following configurations:
| Python version | GPU | Configuration file |
|---|---|---|
| 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.
=================
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