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**Description**
Add stdout logging util module and enable real-time logging flushing in executor

**Major Revision**
- Add stdout logging util module to redirect stdout into file log
- enable stdout logging in executor to write benchmark output into both stdout and file `sb-bench.log`
- enable real-time log flushing in run_command of microbenchmarks through config `log_flushing`

**Minor Revision**
- add log_n_step args to enable regular step time log in model benchmarks 
- udpate related docs

**Description**

Cherry-pick  bug fixes from v0.5.0 to main.

**Major Revisions**

* Bug - Force to fix ort version as '1.10.0' (#343)
* Bug - Support no matching rules and unify the output name in result_summary (#345)
* Analyzer - Support regex in annotations of benchmark naming for metrics in rules (#344)
* Bug - Fix bugs in sync results on root rank for e2e model benchmarks (#342)
* Bug - Fix bug of duration feature for model benchmarks in distributed mode (#347)
* Docs - Upgrade version and release note (#348)
Co-authored-by: Yuting Jiang <v-yutjiang@microsoft.com>

**Description**
Remove fp16 samples type converting time for training cnn and lstm inference.

**Description**
Remove fp16  samples type converting time for cnn and lstm models.

**Description**
The BatchNorm operator is not numerically stable in fp16.  PyTorch documentation recommends to keep the BN op in fp32 for fp16 AMP models.  Refer to https://pytorch.org/docs/stable/amp.html#ops-that-can-autocast-to-float32.  Preserving BN in fp32 for superbench more accurately reflects real workloads.

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

* Benchmarks: Add Benchmark - Add CNN model benchmarks.

Benchmarks: Code Revision - Fix some issue for BERT benchmark. (#58)

Benchmarks: Add Benchmark - Add Pytorch BERT benchmarks, including bert-base and bert-large.   (#20)

* add pytorch bert benchmarks.

* revise code

* fix issue

* revise code.
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>