GitLab

**Description**
This commit adds readwrite I/O pattern for FIO benchmark. Read/write ratio is fixed at 4:1.

**Description**
Generate the summarized output files from all nodes. For each metric, do the reduce operation according to the `reduce_op`

**Major Revision**
- Generate the summarized json file per node:
For microbenchmark, the format is `{benchmark_name}/[{run_count}/]{metric_name}[:rank]`
For modelbenchmark, the format is `{benchmark_name}/{sub_benchmark_name}/[{run_count}/]{metric_name}`
`[]` means optional.
```
{
  "kernel-launch/overhead_event:0": 0.00583,
  "kernel-launch/overhead_event:1": 0.00545,
  "kernel-launch/overhead_event:2": 0.00581,
  "kernel-launch/overhead_event:3": 0.00572,
  "kernel-launch/overhead_event:4": 0.00559,
  "kernel-launch/overhead_event:5": 0.00591,
  "kernel-launch/overhead_event:6": 0.00562,
  "kernel-launch/overhead_event:7": 0.00586,
  "resnet_models/pytorch-resnet50/steptime-train-float32": 544.0827468410134,
  "resnet_models/pytorch-resnet50/throughput-train-float32": 353.7607016465773,
  "resnet_models/pytorch-resnet50/steptime-train-float16": 425.40482617914677,
  "resnet_models/pytorch-resnet50/throughput-train-float16": 454.0142363793973,
  "pytorch-sharding-matmul/0/allreduce": 10.561786651611328,
  "pytorch-sharding-matmul/1/allreduce": 10.561786651611328,
  "pytorch-sharding-matmul/0/allgather": 10.088025093078613,
  "pytorch-sharding-matmul/1/allgather": 10.088025093078613
}
```
- Generate the summarized jsonl file for all nodes, each line is the result from one node in json format.

Support mpi mode in runner:
* concate mpirun command
* support mca and env config
* prepare hostfile and update Ansible host pattern
Co-authored-by: Peng Cheng <chengpeng5555@outlook.com>

**Description**
Change the field name `reduce` to `reduce_op`.

**Description**
Set reduce type for current benchmarks' metrics, including model benchmarks and ShardingMatmul.

**Description**
Replace `sum(results) / len(results)` with `statistics.mean(results)`

**Description**
Add reduce function support for output summary.

**Major Revision**
- Add reducer class to maintain all reduce functions.
- Save reduce type of each metric into `BenchmarkResult`
- Fix UT.

**Description**
Add rccl bandwidth microbenchmark for rocm.

**Major Revision**
- Register rccl-bw benchmark.

__Description__
Cherry-pick bug fixes from v0.2.1 to main.

__Major Revisions__
* Fix bug of VGG models failed on A100 GPU with batch_size=128.
* Fix Ansible connection issue when running in localhost.
* Update version in packages and docs.

**Description**
Add the source code of rocm kernel launch overhead benchmark. 

**Major Revision**
- Revise cmake build logic to support both cuda and rocm

**Description**
Support rocm cmake build. 

**Major Revision**
- Add  some envs in rocm_common.cmake to support rocm cmake build.

**Description**
Add NCCL performance microbenchmark.

**Major Revision**
- Add microbenchmark, example, test, config for NCCL

**Description**
Add RDMA Loopback performance microbenchmark.

**Major Revision**
- Add microbenchmark, example, test, config for RDMA Loopback

**Description**
Add disk performance microbenchmark.

**Major Revision**
- Add microbenchmark, example, test, config for disk performance.

**Minor Revision**
- Fix bugs in executor unit test related to default enabled tests.

Add microbenchmark, example, test, config for cuda memory performance and Add cuda-samples(tag with cuda version) as git submodule and update related makefile

Update network common utils. Add get_ib_devices in network common utils and move get_free_port from test utils to network common utils

* Bug Fix - Fix race condition issue for multi ranks (#117)

Fix race condition issue when multi ranks rotating the same directory.

* Update pipeline for release branch (#122)

* Bug Fix - Fix bug when convert bool config to store_true argument. (#120)
Co-authored-by: Yifan Xiong <yifan.xiong@microsoft.com>

* add validation config file for AMD MI100

Support both NVIDIA and AMD GPU and check GPU vendor during deployment and execution.

* Add GPU environment check in sb deploy.
* Check GPU vendor in executor.

Update README and version for v0.2 release.

Fetch benchmarks results on all nodes, will rsync after each benchmark.
The results directory structure on control node is as follows:

```
outputs/
└── datetime
    ├── nodes
    │   └── node-0
    │       ├── benchmarks
    │       │   ├── benchmark-0
    │       │   │   ├── rank-0
    │       │   │   │   └── results.json
    │       └── sb-exec.log
    ├── sb-run.log
    └── sb.config.yaml
```

Support `--host-list` for deploy and run commands.

Before this change, an inventory file is needed to use `sb deploy/run`.
Now, `--host-list localhost` or `-l localhost` is sufficient for quick try.

* Support custom output directory.
* Update document.

* fix bug for nvidia v100
* hard code the supported dict for different arch.

* add config file for ndv4.

* replace torch.optim.AdamW with transformers.AdamW.

* Add `sb deploy` command content.
* Fix inline if-expression syntax in playbook.
* Fix quote escape issue in bash command.
* Add custom env in config.
* Update default config for multi GPU benchmarks.
* Update MANIFEST.in to include jinja2 template.
* Require jinja2 minimum version.
* Fix occasional duplicate output in Ansible runner.
* Fix mixed color from Ansible and Python colorlog.
* Update according to comments.
* Change superbench.env from list to dict in config file.

Benchmarks: Add Feature - Add DistributedImpl and DistributedBackend arguments for micro benchmark. (#100)

rename bin name and result metric of cublas and cudnn microbenchmark

Fix bugs and refine log in single GPU benchmarks:

* Fix none framework issue
* Fix empty parameter bug
* Remove missed mobilenet_v3 models
* Change benchmark registration log to debug level
* Add pid in logging
* Add missing benchmarks in default config
* Fix deprecated logging warn

* Clean up the cache.

* fix return code reset issue

* Support local mode in runner.

* Change default shape of sharding-matmul.

* add cuda flops performance benchmark.

* add python related cudnn microbenchmark

* add error return code for cublas micro benchmark