This README provides an abbreviated documentation of the DLIO code. Please refer to https://dlio-benchmark.readthedocs.io for full user documentation.
DLIO is an I/O benchmark for Deep Learning. DLIO is aimed at emulating the I/O behavior of various deep learning applications. The benchmark is delivered as an executable that can be configured for various I/O patterns. It uses a modular design to incorporate more data loaders, data formats, datasets, and configuration parameters. It emulates modern deep learning applications using Benchmark Runner, Data Generator, Format Handler, and I/O Profiler modules.
DLIO supports multiple storage backends out of the box:
- Local filesystem — the default, for NFS, Lustre, GPFS, and local NVMe
- AWS S3 / S3-compatible object storage — via s3dlio, s3torchconnector, or the MinIO Python SDK
- AIStore — via the native AIStore Python SDK
Object storage backends are configured through the storage: block in the workload YAML file (see Object Storage Configuration below).
git clone https://github.com/argonne-lcf/dlio_benchmark
cd dlio_benchmark/
pip install .
dlio_benchmark ++workload.workflow.generate_data=Truegit clone https://github.com/argonne-lcf/dlio_benchmark
cd dlio_benchmark/
pip install .[aistore]For S3-compatible object storage (AWS S3, MinIO, Vast Data, etc.) install one or more of the supported storage libraries alongside DLIO:
git clone https://github.com/argonne-lcf/dlio_benchmark
cd dlio_benchmark/
pip install .
# Choose one (or more) S3 client libraries:
pip install s3dlio # recommended — high-performance Rust-backed S3 client
pip install s3torchconnector # AWS S3 Connector for PyTorch (PyTorch only)
pip install minio # MinIO Python SDKThe storage library to use is selected per-workload via storage.storage_options.storage_library in the YAML config (see Object Storage Configuration).
git clone https://github.com/argonne-lcf/dlio_benchmark
cd dlio_benchmark/
pip install .[pydftracer]git clone https://github.com/argonne-lcf/dlio_benchmark
cd dlio_benchmark/
docker build -t dlio .
docker run -t dlio dlio_benchmark ++workload.workflow.generate_data=TrueYou can also pull rebuilt container from docker hub (might not reflect the most recent change of the code):
docker pull docker.io/zhenghh04/dlio:latest
docker run -t docker.io/zhenghh04/dlio:latest dlio_benchmark ++workload.workflow.generate_data=TrueIf your running on a different architecture, refer to the Dockerfile to build the dlio_benchmark container from scratch.
One can also run interactively inside the container
docker run -t docker.io/zhenghh04/dlio:latest /bin/bash
root@30358dd47935:/workspace/dlio$ dlio_benchmark ++workload.workflow.generate_data=TruePowerPC requires installation through anaconda.
# Setup required channels
conda config --prepend channels https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/
# create and activate environment
conda env create --prefix ./dlio_env_ppc --file environment-ppc.yaml --force
conda activate ./dlio_env_ppc
# install other dependencies
python -m pip install .For specific instructions on how to install and run the benchmark on Lassen please refer to: Install Lassen
A DLIO run is split in 3 phases:
- Generate synthetic data that DLIO will use
- Run the benchmark using the previously generated data
- Post-process the results to generate a report
The configurations of a workload can be specified through a yaml file. Examples of yaml files can be found in dlio_benchmark/configs/workload/.
One can specify the workload through the workload= option on the command line. Specific configuration fields can then be overridden following the hydra framework convention (e.g. ++workload.framework=tensorflow).
First, generate the data
mpirun -np 8 dlio_benchmark workload=unet3d ++workload.workflow.generate_data=True ++workload.workflow.train=FalseIf possible, one can flush the filesystem caches in order to properly capture device I/O
sudo sync && echo 3 | sudo tee /proc/sys/vm/drop_cachesFinally, run the benchmark
mpirun -np 8 dlio_benchmark workload=unet3dFinally, run the benchmark with Tracer
export DFTRACER_ENABLE=1
export DFTRACER_INC_METADATA=1
mpirun -np 8 dlio_benchmark workload=unet3dAll the outputs will be stored in hydra_log/unet3d/$DATE-$TIME folder. To post process the data, one can do
dlio_postprocessor --output-folder hydra_log/unet3d/$DATE-$TIMEThis will generate DLIO_$model_report.txt in the output folder.
Workload characteristics are specified by a YAML configuration file. Below is an example of a YAML file for the UNet3D workload which is used for 3D image segmentation.
# contents of unet3d.yaml
model:
name: unet3d
model_size: 499153191
framework: pytorch
workflow:
generate_data: False
train: True
checkpoint: True
dataset:
data_folder: data/unet3d/
format: npz
num_files_train: 168
num_samples_per_file: 1
record_length_bytes: 146600628
record_length_bytes_stdev: 68341808
record_length_bytes_resize: 2097152
reader:
data_loader: pytorch
batch_size: 4
read_threads: 4
file_shuffle: seed
sample_shuffle: seed
train:
epochs: 5
computation_time: 1.3604
checkpoint:
checkpoint_folder: checkpoints/unet3d
checkpoint_after_epoch: 5
epochs_between_checkpoints: 2
The full list of configurations can be found in: https://argonne-lcf.github.io/dlio_benchmark/config.html
Object storage is enabled by adding a storage: block to the workload YAML. The storage_type: s3 value activates the S3 backend; a storage_library field selects the underlying client library.
storage_library |
Description | Framework support |
|---|---|---|
s3dlio |
High-performance Rust-backed client via s3dlio. Parallel GET, range optimization, multi-endpoint load balancing. | PyTorch + TensorFlow |
s3torchconnector |
AWS S3 Connector for PyTorch — streaming single-file GET. | PyTorch only |
minio |
MinIO Python SDK via ThreadPoolExecutor. |
PyTorch + TensorFlow |
# contents of unet3d_s3.yaml
model:
name: unet3d
model_size: 499153191
framework: pytorch
workflow:
generate_data: False
train: True
checkpoint: False
dataset:
data_folder: my-bucket/unet3d # path within the bucket
format: npz
num_files_train: 168
num_samples_per_file: 1
record_length_bytes: 146600628
record_length_bytes_stdev: 68341808
record_length_bytes_resize: 2097152
storage:
storage_type: s3
storage_root: my-bucket # S3 bucket name
storage_library: s3dlio # client library (s3dlio | s3torchconnector | minio)
storage_options:
endpoint_url: http://your-s3-host:9000 # omit for AWS; required for MinIO etc.
region: us-east-1
# Credentials come from environment variables:
# export AWS_ACCESS_KEY_ID=...
# export AWS_SECRET_ACCESS_KEY=...
reader:
data_loader: pytorch
batch_size: 7
read_threads: 4
file_shuffle: seed
sample_shuffle: seed
# Required when using s3dlio with PyTorch multiprocessing:
multiprocessing_context: spawn
train:
epochs: 5
computation_time: 0.323Set credentials via environment variables before running:
export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key
export AWS_ENDPOINT_URL=http://your-s3-host:9000 # for non-AWS endpoints
# Generate data into S3
mpirun -np 8 dlio_benchmark workload=unet3d_s3 ++workload.workflow.generate_data=True ++workload.workflow.train=False
# Run benchmark from S3
mpirun -np 8 dlio_benchmark workload=unet3d_s3Pre-built S3 workload configs matching MLPerf Storage GPU profiles are available in dlio_benchmark/configs/workload/ (e.g. unet3d_h100_s3.yaml, unet3d_a100_s3.yaml, unet3d_v100_s3.yaml).
The training loop timing is not affected by switching to object storage. The measurement sequence (start_loading → batch delivery → batch_loaded → GPU sleep → batch_processed) is identical to local filesystem runs. Object storage I/O happens inside PyTorch DataLoader worker processes during the GPU computation sleep, exactly as local file reads do. See docs/DLIO-Object-Storage_Analysis.md for a detailed analysis.
The YAML file is loaded through hydra (https://hydra.cc/). The default setting are overridden by the configurations loaded from the YAML file. One can override the configuration through command line (https://hydra.cc/docs/advanced/override_grammar/basic/).
-
DLIO currently assumes the samples to always be 2D images, even though one can set the size of each sample through
--record_length. We expect the shape of the sample to have minimal impact to the I/O itself. This yet to be validated for case by case perspective. We plan to add option to allow specifying the shape of the sample. -
We assume the data/label pairs are stored in the same file. Storing data and labels in separate files will be supported in future.
-
File format support: we only support tfrecord, hdf5, npz, csv, jpg, jpeg formats. Other data formats can be extended.
-
Storage backend support: we support local filesystem (
local_fs), AWS S3 and S3-compatible object stores (s3), and AIStore (aistore). For S3 storage, three client libraries are available: s3dlio (recommended), s3torchconnector (PyTorch only), and the MinIO SDK. Other storage backends can be extended. -
Data Loader support: we support reading datasets using TensorFlow tf.data data loader, PyTorch DataLoader, and a set of custom data readers implemented in ./reader. For TensorFlow tf.data data loader, PyTorch DataLoader
- We have complete support for tfrecord format in TensorFlow data loader.
- For npz, jpg, jpeg, hdf5, we currently only support one sample per file case. In other words, each sample is stored in an independent file. Multiple samples per file case will be supported in future.
We welcome contributions from the community to the benchmark code. Specifically, we welcome contribution in the following aspects: General new features needed including:
- support for new workloads: if you think that your workload(s) would be interested to the public, and would like to provide the yaml file to be included in the repo, please submit an issue.
- support for new data loaders, such as DALI loader, MxNet loader, etc
- support for new frameworks, such as MxNet
- support for novel file systems or storage, such as AWS S3, AIStore, etc.
- support for loading new data formats.
If you would like to contribute, please submit an issue to https://github.com/argonne-lcf/dlio_benchmark/issues, and contact ALCF DLIO team, Huihuo Zheng at huihuo.zheng@anl.gov
The original CCGrid'21 paper describes the design and implementation of DLIO code. Please cite this paper if you use DLIO for your research.
@article{devarajan2021dlio,
title={DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications},
author={H. Devarajan and H. Zheng and A. Kougkas and X.-H. Sun and V. Vishwanath},
booktitle={IEEE/ACM International Symposium in Cluster, Cloud, and Internet Computing (CCGrid'21)},
year={2021},
volume={},
number={81--91},
pages={},
publisher={IEEE/ACM}
}
We also encourage people to take a look at a relevant work from MLPerf Storage working group.
@article{balmau2022mlperfstorage,
title={Characterizing I/O in Machine Learning with MLPerf Storage},
author={O. Balmau},
booktitle={SIGMOD Record DBrainstorming},
year={2022},
volume={51},
number={3},
publisher={ACM}
}
This work used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility under Contract DE-AC02-06CH11357 and is supported in part by National Science Foundation under NSF, OCI-1835764 and NSF, CSR-1814872.
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