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This PR adds a user guide to AIR for using Ray Train. It provides a high level overview of the trainers and removes redundant sections. The main file to review is here: doc/source/ray-air/trainer.rst. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com> Signed-off-by: Richard Liaw <rliaw@berkeley.edu> Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Kai Fricke <kai@anyscale.com>
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9.8 KiB
ReStructuredText
262 lines
9.8 KiB
ReStructuredText
Benchmarks
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==========
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Below we document key performance benchmarks for common AIR tasks and workflows.
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Bulk Ingest
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-----------
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This task uses the DummyTrainer module to ingest 200GiB of synthetic data.
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We test out the performance across different cluster sizes.
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- `Bulk Ingest Script`_
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- `Bulk Ingest Cluster Configuration`_
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For this benchmark, we configured the nodes to have reasonable disk size and throughput to account for object spilling.
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.. code-block:: yaml
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aws:
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BlockDeviceMappings:
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- DeviceName: /dev/sda1
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Ebs:
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Iops: 5000
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Throughput: 1000
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VolumeSize: 1000
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VolumeType: gp3
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.. list-table::
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* - **Cluster Setup**
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- **Performance**
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- **Disk Spill**
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- **Command**
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* - 1 m5.4xlarge node (1 actor)
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- 390 s (0.51 GiB/s)
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- 205 GiB
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- `python data_benchmark.py --dataset-size-gb=200 --num-workers=1`
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* - 5 m5.4xlarge nodes (5 actors)
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- 70 s (2.85 GiB/S)
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- 206 GiB
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- `python data_benchmark.py --dataset-size-gb=200 --num-workers=5`
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* - 20 m5.4xlarge nodes (20 actors)
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- 3.8 s (52.6 GiB/s)
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- 0 GiB
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- `python data_benchmark.py --dataset-size-gb=200 --num-workers=20`
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XGBoost Batch Prediction
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------------------------
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This task uses the BatchPredictor module to process different amounts of data
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using an XGBoost model.
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We test out the performance across different cluster sizes and data sizes.
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- `XGBoost Prediction Script`_
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- `XGBoost Cluster Configuration`_
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.. TODO: Add script for generating data and running the benchmark.
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.. list-table::
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* - **Cluster Setup**
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- **Data Size**
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- **Performance**
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- **Command**
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* - 1 m5.4xlarge node (1 actor)
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- 10 GB (26M rows)
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- 275 s (94.5k rows/s)
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- `python xgboost_benchmark.py --size 10GB`
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* - 10 m5.4xlarge nodes (10 actors)
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- 100 GB (260M rows)
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- 331 s (786k rows/s)
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- `python xgboost_benchmark.py --size 100GB`
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.. _xgboost-benchmark:
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XGBoost training
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----------------
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This task uses the XGBoostTrainer module to train on different sizes of data
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with different amounts of parallelism.
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XGBoost parameters were kept as defaults for xgboost==1.6.1 this task.
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- `XGBoost Training Script`_
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- `XGBoost Cluster Configuration`_
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.. list-table::
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* - **Cluster Setup**
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- **Data Size**
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- **Performance**
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- **Command**
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* - 1 m5.4xlarge node (1 actor)
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- 10 GB (26M rows)
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- 692 s
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- `python xgboost_benchmark.py --size 10GB`
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* - 10 m5.4xlarge nodes (10 actors)
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- 100 GB (260M rows)
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- 693 s
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- `python xgboost_benchmark.py --size 100GB`
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GPU image batch prediction
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--------------------------
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This task uses the BatchPredictor module to process different amounts of data
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using a Pytorch pre-trained ResNet model.
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We test out the performance across different cluster sizes and data sizes.
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- `GPU image batch prediction script`_
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- `GPU training small cluster configuration`_
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- `GPU training large cluster configuration`_
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.. list-table::
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* - **Cluster Setup**
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- **Data Size**
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- **Performance**
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- **Command**
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* - 1 g3.8xlarge node
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- 1 GB (1623 images)
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- 72.59 s (22.3 images/sec)
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- `python gpu_batch_prediction.py --data-size-gb=1`
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* - 1 g3.8xlarge node
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- 20 GB (32460 images)
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- 1213.48 s (26.76 images/sec)
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- `python gpu_batch_prediction.py --data-size-gb=20`
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* - 4 g3.16xlarge nodes
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- 100 GB (162300 images)
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- 885.98 s (183.19 images/sec)
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- `python gpu_batch_prediction.py --data-size-gb=100`
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GPU image training
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------------------
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This task uses the TorchTrainer module to train different amounts of data
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using an Pytorch ResNet model.
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We test out the performance across different cluster sizes and data sizes.
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- `GPU image training script`_
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- `GPU training small cluster configuration`_
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- `GPU training large cluster configuration`_
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.. note::
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For multi-host distributed training, on AWS we need to ensure ec2 instances are in the same VPC and
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all ports are open in the secure group.
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.. list-table::
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* - **Cluster Setup**
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- **Data Size**
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- **Performance**
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- **Command**
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* - 1 g3.8xlarge node (1 worker)
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- 1 GB (1623 images)
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- 79.76 s (2 epochs, 40.7 images/sec)
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- `python pytorch_training_e2e.py --data-size-gb=1`
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* - 1 g3.8xlarge node (1 worker)
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- 20 GB (32460 images)
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- 1388.33 s (2 epochs, 46.76 images/sec)
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- `python pytorch_training_e2e.py --data-size-gb=20`
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* - 4 g3.16xlarge nodes (16 workers)
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- 100 GB (162300 images)
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- 434.95 s (2 epochs, 746.29 images/sec)
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- `python pytorch_training_e2e.py --data-size-gb=100 --num-workers=16`
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.. _pytorch-training-parity:
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Pytorch Training Parity
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-----------------------
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This task checks the performance parity between native Pytorch Distributed and
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Ray Train's distributed TorchTrainer.
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We demonstrate that the performance is similar (within 10\%) between the two frameworks.
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Performance may vary greatly across different model, hardware, and cluster configurations.
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- `Pytorch comparison training script`_
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- `Pytorch comparison CPU cluster configuration`_
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- `Pytorch comparison GPU cluster configuration`_
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.. list-table::
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* - **Cluster Setup**
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- **Dataset**
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- **Performance**
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- **Command**
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* - 4 m5.2xlarge nodes (4 workers)
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- FashionMNIST
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- 201.17 s (vs 195.90 s Pytorch)
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- `python workloads/torch_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 4 --cpus-per-worker 8`
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* - 4 m5.2xlarge nodes (16 workers)
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- FashionMNIST
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- 447.14 s (vs 461.75 s Pytorch)
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- `python workloads/torch_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 16 --cpus-per-worker 2`
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* - 4 g4dn.12xlarge node (16 workers)
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- FashionMNIST
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- 236.61 s (vs 220.97 s Pytorch)
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- `python workloads/torch_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 16 --cpus-per-worker 4 --use-gpu`
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.. _tf-training-parity:
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Tensorflow Training Parity
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--------------------------
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This task checks the performance parity between native Tensorflow Distributed and
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Ray Train's distributed TensorflowTrainer.
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We demonstrate that the performance is similar (within 10\%) between the two frameworks.
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Performance may vary greatly across different model, hardware, and cluster configurations.
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.. note:: The batch size and number of epochs is different for the GPU benchmark, resulting in a longer runtime.
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- `Tensorflow comparison training script`_
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- `Tensorflow comparison CPU cluster configuration`_
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- `Tensorflow comparison GPU cluster configuration`_
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.. list-table::
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* - **Cluster Setup**
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- **Dataset**
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- **Performance**
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- **Command**
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* - 4 m5.2xlarge nodes (4 workers)
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- FashionMNIST
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- 90.61 s (vs 81.26 s Tensorflow)
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- `python workloads/tensorflow_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 4 --cpus-per-worker 8`
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* - 4 m5.2xlarge nodes (16 workers)
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- FashionMNIST
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- 75.34 s (vs 69.51 s Tensorflow)
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- `python workloads/tensorflow_benchmark.py run --num-runs 3 --num-epochs 20 --num-workers 16 --cpus-per-worker 2`
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* - 4 g4dn.12xlarge node (16 workers)
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- FashionMNIST
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- 495.85 s (vs 479.28 s Tensorflow)
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- `python workloads/tensorflow_benchmark.py run --num-runs 3 --num-epochs 200 --num-workers 16 --cpus-per-worker 4 --batch-size 64 --use-gpu`
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.. _`Bulk Ingest Script`: https://github.com/ray-project/ray/blob/a30bdf9ef34a45f973b589993f7707a763df6ebf/release/air_tests/air_benchmarks/workloads/data_benchmark.py#L25-L40
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.. _`Bulk Ingest Cluster Configuration`: https://github.com/ray-project/ray/blob/a30bdf9ef34a45f973b589993f7707a763df6ebf/release/air_tests/air_benchmarks/data_20_nodes.yaml#L6-L15
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.. _`XGBoost Training Script`: https://github.com/ray-project/ray/blob/a241e6a0f5a630d6ed5b84cce30c51963834d15b/release/air_tests/air_benchmarks/workloads/xgboost_benchmark.py#L40-L58
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.. _`XGBoost Prediction Script`: https://github.com/ray-project/ray/blob/a241e6a0f5a630d6ed5b84cce30c51963834d15b/release/air_tests/air_benchmarks/workloads/xgboost_benchmark.py#L63-L71
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.. _`XGBoost Cluster Configuration`: https://github.com/ray-project/ray/blob/a241e6a0f5a630d6ed5b84cce30c51963834d15b/release/air_tests/air_benchmarks/xgboost_compute_tpl.yaml#L6-L24
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.. _`GPU image batch prediction script`: https://github.com/ray-project/ray/blob/cec82a1ced631525a4d115e4dc0c283fa4275a7f/release/air_tests/air_benchmarks/workloads/gpu_batch_prediction.py#L18-L49
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.. _`GPU image training script`: https://github.com/ray-project/ray/blob/cec82a1ced631525a4d115e4dc0c283fa4275a7f/release/air_tests/air_benchmarks/workloads/pytorch_training_e2e.py#L95-L106
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.. _`GPU training small cluster configuration`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/compute_gpu_1.yaml#L6-L24
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.. _`GPU training large cluster configuration`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/compute_gpu_16.yaml#L5-L25
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.. _`Pytorch comparison training script`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/workloads/torch_benchmark.py
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.. _`Pytorch comparison CPU cluster configuration`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/compute_cpu_4.yaml
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.. _`Pytorch comparison GPU cluster configuration`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/compute_gpu_4x4.yaml
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.. _`Tensorflow comparison training script`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/workloads/tensorflow_benchmark.py
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.. _`Tensorflow comparison CPU cluster configuration`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/compute_cpu_4.yaml
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.. _`Tensorflow comparison GPU cluster configuration`: https://github.com/ray-project/ray/blob/master/release/air_tests/air_benchmarks/compute_gpu_4x4.yaml
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