[REQUEST] Some comments on using deepspeed to know about the distributed effect

Is your feature request related to a problem? Please describe.

If I want to experiment with zero-3 to train 345m GPT model, how to set the relevant configuration of zero-3? At present, I use the default configuration and find that the training speed is not very fast.

model link: https://github.com/microsoft/DeepSpeedExamples/tree/174ae3bc8dbb688cfaccb4afa15d6e2cdbe19ce5/Megatron-LM-v1.1.5-ZeRO3

#ZeRO Configs
stage=3
reduce_scatter=true
contigious_gradients=true
rbs=50000000
agbs=5000000000

what's that mean?

{
  "train_batch_size": 64,
  "gradient_accumulation_steps": 1,
  "steps_per_print": 1,
  "zero_optimization": {
    "stage": 3,
    "stage3_max_live_parameters": 1e9,
    "stage3_max_reuse_distance": 1e9,
    "stage3_prefetch_bucket_size": 1e7,
    "stage3_param_persitence_threshold": 1e5,
    "reduce_bucket_size": 1e7,
    "contiguous_gradients": true
  },
  "gradient_clipping": 1.0,
  "fp16": {
    "enabled": true,
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "wall_clock_breakdown": true,
  "zero_allow_untested_optimizer": false
}

what is stage3_max_live_parameters? Does it appear in the API introduction？

Describe the solution you'd like
I hope to give a classic configuration model, such as 345m GPT model of single 8-card, which can facilitate users to experience the algorithm effect of DeepSpeed.

Describe alternatives you've considered
In the released configuration information, you should give a classic configuration with the best performance, and then give your test performance, which can be used as a training reference.

Additional context
I hope to give the performance data of the classic 345m GPT model of single 8-card under zero-3 as a performance reference. For example, the GPT model can be adjusted under the default configuration of the GPT 345m model.

Start with the linked Megatron-LM-v1.1.5-ZeRO3 example and the ZeRO-3 configuration shown in the issue. Determine where configuration explanations and benchmark guidance belong, then document a 345M GPT single 8-card reference setup, explain settings such as stage3_max_live_parameters, and include measured performance data.