Below we document key performance benchmarks for common AIR tasks and workflows.
This task uses the DummyTrainer module to ingest 200GiB of synthetic data.
We test out the performance across different cluster sizes.
For this benchmark, we configured the nodes to have reasonable disk size and throughput to account for object spilling.
aws:
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
Iops: 5000
Throughput: 1000
VolumeSize: 1000
VolumeType: gp3| Cluster Setup | Performance | Disk Spill | Command |
| 1 m5.4xlarge node (1 actor) | 390 s (0.51 GiB/s) | 205 GiB | python data_benchmark.py --dataset-size-gb=200 --num-workers=1 |
| 5 m5.4xlarge nodes (5 actors) | 70 s (2.85 GiB/S) | 206 GiB | python data_benchmark.py --dataset-size-gb=200 --num-workers=5 |
| 20 m5.4xlarge nodes (20 actors) | 3.8 s (52.6 GiB/s) | 0 GiB | python data_benchmark.py --dataset-size-gb=200 --num-workers=20 |
This task uses the BatchPredictor module to process different amounts of data using an XGBoost model.
We test out the performance across different cluster sizes and data sizes.
| Cluster Setup | Data Size | Performance | Command |
| 1 m5.4xlarge node (1 actor) | 10 GB (26M rows) | 275 s (94.5k rows/s) | python xgboost_benchmark.py --size 10GB |
| 10 m5.4xlarge nodes (10 actors) | 100 GB (260M rows) | 331 s (786k rows/s) | python xgboost_benchmark.py --size 100GB |
This task uses the XGBoostTrainer module to train on different sizes of data with different amounts of parallelism.
XGBoost parameters were kept as defaults for xgboost==1.6.1 this task.
| Cluster Setup | Data Size | Performance | Command |
| 1 m5.4xlarge node (1 actor) | 10 GB (26M rows) | 692 s | python xgboost_benchmark.py --size 10GB |
| 10 m5.4xlarge nodes (10 actors) | 100 GB (260M rows) | 693 s | python xgboost_benchmark.py --size 100GB |
This task uses the BatchPredictor module to process different amounts of data using a Pytorch pre-trained ResNet model.
We test out the performance across different cluster sizes and data sizes.
- GPU image batch prediction script
- GPU training small cluster configuration
- GPU training large cluster configuration
| Cluster Setup | Data Size | Performance | Command |
| 1 g3.8xlarge node | 1 GB (1623 images) | 72.59 s (22.3 images/sec) | python gpu_batch_prediction.py --data-size-gb=1 |
| 1 g3.8xlarge node | 20 GB (32460 images) | 1213.48 s (26.76 images/sec) | python gpu_batch_prediction.py --data-size-gb=20 |
| 4 g3.16xlarge nodes | 100 GB (162300 images) | 885.98 s (183.19 images/sec) | python gpu_batch_prediction.py --data-size-gb=100 |
This task uses the TorchTrainer module to train different amounts of data using an Pytorch ResNet model.
We test out the performance across different cluster sizes and data sizes.
- GPU image training script
- GPU training small cluster configuration
- GPU training large cluster configuration
Note
For multi-host distributed training, on AWS we need to ensure ec2 instances are in the same VPC and all ports are open in the secure group.
| Cluster Setup | Data Size | Performance | Command |
| 1 g3.8xlarge node (1 worker) | 1 GB (1623 images) | 79.76 s (2 epochs, 40.7 images/sec) | python pytorch_training_e2e.py --data-size-gb=1 |
| 1 g3.8xlarge node (1 worker) | 20 GB (32460 images) | 1388.33 s (2 epochs, 46.76 images/sec) | python pytorch_training_e2e.py --data-size-gb=20 |
| 4 g3.16xlarge nodes (16 workers) | 100 GB (162300 images) | 434.95 s (2 epochs, 746.29 images/sec) | python pytorch_training_e2e.py --data-size-gb=100 --num-workers=16 |
This task checks the performance parity between native Pytorch Distributed and Ray Train's distributed TorchTrainer.
We demonstrate that the performance is similar (within 2.5%) between the two frameworks. Performance may vary greatly across different model, hardware, and cluster configurations.
The reported times are for the raw training times. There is an unreported constant setup overhead of a few seconds for both methods that is negligible for longer training runs.
- Pytorch comparison training script
- Pytorch comparison CPU cluster configuration
- Pytorch comparison GPU cluster configuration
| Cluster Setup | Dataset | Performance | Command |
| 4 m5.2xlarge nodes (4 workers) | FashionMNIST | 196.64 s (vs 194.90 s Pytorch) | python workloads/torch_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 4 --cpus-per-worker 8 |
| 4 m5.2xlarge nodes (16 workers) | FashionMNIST | 430.88 s (vs 475.97 s Pytorch) | python workloads/torch_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 16 --cpus-per-worker 2 |
| 4 g4dn.12xlarge node (16 workers) | FashionMNIST | 149.80 s (vs 146.46 s Pytorch) | python workloads/torch_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 16 --cpus-per-worker 4 --use-gpu |
This task checks the performance parity between native Tensorflow Distributed and Ray Train's distributed TensorflowTrainer.
We demonstrate that the performance is similar (within 1%) between the two frameworks. Performance may vary greatly across different model, hardware, and cluster configurations.
The reported times are for the raw training times. There is an unreported constant setup overhead of a few seconds for both methods that is negligible for longer training runs.
Note
The batch size and number of epochs is different for the GPU benchmark, resulting in a longer runtime.
- Tensorflow comparison training script
- Tensorflow comparison CPU cluster configuration
- Tensorflow comparison GPU cluster configuration
| Cluster Setup | Dataset | Performance | Command |
| 4 m5.2xlarge nodes (4 workers) | FashionMNIST | 78.81 s (vs 79.67 s Tensorflow) | python workloads/tensorflow_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 4 --cpus-per-worker 8 |
| 4 m5.2xlarge nodes (16 workers) | FashionMNIST | 64.57 s (vs 67.45 s Tensorflow) | python workloads/tensorflow_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 16 --cpus-per-worker 2 |
| 4 g4dn.12xlarge node (16 workers) | FashionMNIST | 465.16 s (vs 461.74 s Tensorflow) | python workloads/tensorflow_benchmark.py run --num-runs 3 --num-epochs 200 --num-workers 16 --cpus-per-worker 4 --batch-size 64 --use-gpu |