Create README_nvidia.md

docs/mlperf/inference/gpt-j/README_nvidia.md

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+[ [Back to the common setup](README.md) ]
+
+## Build Nvidia Docker Container (from 3.1 Inference round)
+
+```
+cm docker script --tags=build,nvidia,inference,server
+```
+## Run this benchmark via CM
+
+
+### Do a test run to detect and record the system performance
+
+```
+cmr "generate-run-cmds inference _find-performance _all-scenarios" \
+--model=gptj-99 --implementation=nvidia-original --device=cuda --backend=tensorrt \
+--category=edge --division=open --quiet
+```
+* Use `--division=closed` to run all scenarios for the closed division.
+* Use `--category=datacenter` to run datacenter scenarios
+
+### Do full accuracy and performance runs for all the scenarios
+
+```
+cmr "generate-run-cmds inference _submission _all-scenarios" --model=gptj-99 \
+--device=cuda --implementation=nvidia-original --backend=tensorrt \
+--execution-mode=valid \
+--category=edge --division=open --quiet --skip_submission_generation=yes
+```
+
+* Use `--power=yes` for measuring power. It is ignored for accuracy and compliance runs
+* Use `--division=closed` to run all scenarios for the closed division. No compliance runs are there for gptj. 
+* `--offline_target_qps`, `--server_target_qps`, and `--singlestream_target_latency` can be used to override the determined performance numbers
+
+### Populate the README files describing your submission
+
+```
+cmr "generate-run-cmds inference _populate-readme _all-scenarios" \
+--model=resnet50 --device=cuda --implementation=nvidia-original --backend=tensorrt \
+--execution-mode=valid --results_dir=$HOME/results_dir \
+--category=edge --division=open --quiet
+```
+
+### Generate and upload MLPerf submission
+
+Follow [this guide](../Submission.md) to generate the submission tree and upload your results.
+
+### Run individual scenarios for testing and optimization
+
+TBD
+
+### Questions? Suggestions?
+
+Don't hesitate to get in touch via [public Discord server](https://discord.gg/JjWNWXKxwT).
+
+### Acknowledgments
+
+* CM automation for Nvidia's MLPerf inference implementation was developed by Arjun Suresh and Grigori Fursin.
+* Nvidia's MLPerf inference implementation was developed by Zhihan Jiang, Ethan Cheng, Yiheng Zhang and Jinho Suh.