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