Docs - Update README and version for v0.2.0 release (#111)
Update README and version for v0.2 release.
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README.md
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README.md
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@ -1,17 +1,23 @@
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# SuperBench
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[](LICENSE)
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[](https://github.com/microsoft/superbenchmark/actions?query=workflow%3ALint)
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[](https://github.com/microsoft/superbenchmark/actions/workflows/build-image.yml)
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[](https://codecov.io/gh/microsoft/superbenchmark)
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[](https://aka.ms/superbench)
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[](https://github.com/microsoft/superbenchmark/releases/latest)
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[](https://hub.docker.com/r/superbench/superbench/tags)
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[](LICENSE)
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| Azure Pipelines | Build Status |
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| :---: | :---: |
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| cpu-unit-test | [](https://dev.azure.com/msrasrg/SuperBenchmark/_build/latest?definitionId=77&branchName=main) |
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| gpu-unit-test | [](https://dev.azure.com/msrasrg/SuperBenchmark/_build/latest?definitionId=80&branchName=main) |
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| cpu-unit-test | [](https://dev.azure.com/msrasrg/SuperBenchmark/_build/latest?definitionId=77&branchName=main) |
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| cuda-unit-test | [](https://dev.azure.com/msrasrg/SuperBenchmark/_build/latest?definitionId=80&branchName=main) |
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| ansible-integration-test | [](https://dev.azure.com/msrasrg/SuperBenchmark/_build/latest?definitionId=82&branchName=main) |
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__SuperBench__ is a validation and profiling tool for AI infrastructure.
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_Check SuperBench website for more details._
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📢 [v0.2.0](https://github.com/microsoft/superbenchmark/releases/tag/v0.2.0) has been released!
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## _Check [aka.ms/superbench](https://aka.ms/superbench) for more details._
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## Trademarks
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Двоичные данные
docs/assets/architecture.png
Двоичные данные
docs/assets/architecture.png
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До Ширина: | Высота: | Размер: 108 KiB |
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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После Ширина: | Высота: | Размер: 23 KiB |
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@ -80,14 +80,26 @@ superbench:
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parameters:
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<<: *common_model_config
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batch_size: 128
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cnn_models:
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resnet_models:
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<<: *default_pytorch_mode
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models:
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- resnet50
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- resnet101
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- resnet152
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parameters:
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<<: *common_model_config
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batch_size: 128
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densenet_models:
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<<: *default_pytorch_mode
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models:
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- densenet169
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- densenet201
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parameters:
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<<: *common_model_config
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batch_size: 128
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vgg_models:
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<<: *default_pytorch_mode
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models:
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- vgg11
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- vgg13
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- vgg16
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@ -99,26 +111,24 @@ superbench:
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By default, all benchmarks in default configuration will be run if you don't specify customized configuration.
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If you want to have a quick try, you can modify this config a little bit. For example, only run resnet models.
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If you want to have a quick try, you can modify this config a little bit. For example, only run resnet101 model.
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1. copy the default config to a file named `resnet.yaml` in current path.
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```bash
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cp superbench/config/default.yaml resnet.yaml
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```
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2. enable only `cnn_models` in the config and remove other models except resnet under `benchmarks.cnn_models.models`.
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```yaml {3,10-13} title="resnet.yaml"
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2. enable only `resnet_models` in the config and remove other models except resnet101 under `benchmarks.resnet_models.models`.
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```yaml {3,11} title="resnet.yaml"
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# SuperBench Config
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superbench:
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enable: ['cnn_models']
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enable: ['resnet_models']
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var:
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# ...
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# omit the middle part
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# ...
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cnn_models:
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resnet_models:
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<<: *default_pytorch_mode
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models:
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- resnet50
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- resnet101
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- resnet152
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parameters:
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<<: *common_model_config
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batch_size: 128
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@ -54,6 +54,12 @@ deactivate
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You can clone the source from GitHub and build it.
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:::note Note
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You should checkout corresponding tag to use release version, for example,
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`git clone -b v0.2.0 https://github.com/microsoft/superbenchmark`
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:::
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```bash
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git clone https://github.com/microsoft/superbenchmark
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cd superbenchmark
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@ -24,6 +24,12 @@ or your private key requires a passphase before use, you can do
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sb deploy -f remote.ini --host-password [password]
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```
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:::note Note
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You should deploy corresponding Docker image to use release version, for example,
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`sb deploy -f local.ini -i superbench/superbench:v0.2.0-cuda11.1.1`
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:::
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## Run
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After deployment, you can start to run the SuperBench benchmarks on all managed nodes using `sb run` command.
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@ -26,4 +26,4 @@ as well as model-benchmark to measure domain-aware end-to-end deep learning work
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The following figure shows the capabilities provided by SuperBench core framework and its extension.
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@ -6,5 +6,5 @@
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Provide hardware and software benchmarks for AI systems.
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"""
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__version__ = '0.0.0'
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__version__ = '0.2.0'
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__author__ = 'Microsoft'
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@ -1,4 +1,5 @@
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# SuperBench Config
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version: v0.2
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superbench:
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enable: null
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var:
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# SuperBench Config
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version: v0.2
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superbench:
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enable: null
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var:
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@ -15,11 +15,11 @@ This blog is to introduce [SuperBench](https://github.com/microsoft/superbenchma
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### Easy-to-use CLI
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In order to provide good user experience, SuperBench provides a command line interface to help users deploy and run benchmarks.
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Empowered by SuperBench CLI, user can deploy and run their benchmarks with only one command, which greatly shorten the learning curve of using tools,
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In order to provide good user experience, SuperBench provides a command line interface to help users deploy and run benchmarks.
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Empowered by SuperBench CLI, user can deploy and run their benchmarks with only one command, which greatly shorten the learning curve of using tools,
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to help user easily evaluate the performance of AI workload.
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Below is a simple example to show how to deploy and run benchmarks locally. For more information,
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Below is a simple example to show how to deploy and run benchmarks locally. For more information,
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please view [CLI Document](https://microsoft.github.io/superbenchmark/docs/cli)
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1. Deploy
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@ -48,9 +48,9 @@ For more information, please view [configuration](https://microsoft.github.io/su
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1. Executor Framework
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In order to facilitate the benchmarking and validation on large-scale clusters, we designed and implemented a modular and extensible framework.
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SuperBench framework includes a runner as control node, as well as multiple executors as worker nodes.
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A runner received commands from CLI and distribute to all nodes (worker nodes) in the cluster, collect data, and summarize the results.
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In order to facilitate the benchmarking and validation on large-scale clusters, we designed and implemented a modular and extensible framework.
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SuperBench framework includes a runner as control node, as well as multiple executors as worker nodes.
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A runner received commands from CLI and distribute to all nodes (worker nodes) in the cluster, collect data, and summarize the results.
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Each worker will run executor to execute the specified benchmark tasks.
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@ -88,7 +88,7 @@ SuperBench supports a set of benchmarks listed as below.
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* BERT models
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* GPT-2 models
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For the details of each benchmark, please view [micro-benchmarks](https://microsoft.github.io/superbenchmark/docs/benchmarks/micro-benchmarks.md)
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For the details of each benchmark, please view [micro-benchmarks](https://microsoft.github.io/superbenchmark/docs/benchmarks/micro-benchmarks.md)
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and [model-benchmarks](https://microsoft.github.io/superbenchmark/docs/benchmarks/model-benchmarks.md).
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@ -96,7 +96,7 @@ and [model-benchmarks](https://microsoft.github.io/superbenchmark/docs/benchmark
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We want to extend SuperBench capability to distributed validation and auto-diagnosis, to build a benchmarking eco-system.
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The following figure shows the whole picture.
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With SuperBench and its extensions, we can support:
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@ -111,4 +111,4 @@ With SuperBench and its extensions, we can support:
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## Call for Contributor
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This project welcomes contributions and suggestions.
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This project welcomes contributions and suggestions.
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@ -101,6 +101,7 @@ module.exports = {
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announcementBar: {
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id: 'supportus',
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content:
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'📢 <a href="https://microsoft.github.io/superbenchmark/blog/release-sb-v0.2">v0.2</a> has been released! ' +
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'⭐️ If you like SuperBench, give it a star on <a target="_blank" rel="noopener noreferrer" href="https://github.com/microsoft/superbenchmark">GitHub</a>! ⭐️',
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},
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prism: {
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@ -1,6 +1,6 @@
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{
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"name": "superbench-website",
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"version": "0.0.0",
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"version": "0.2.0",
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"lockfileVersion": 1,
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"requires": true,
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"dependencies": {
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@ -1,6 +1,6 @@
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{
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"name": "superbench-website",
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"version": "0.0.0",
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"version": "0.2.0",
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"private": true,
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"scripts": {
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"docusaurus": "docusaurus",
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