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Logging/Saving Settings and Instructions for Inference Jobs #1646

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mohammedouhibi opened this issue May 26, 2024 · 6 comments
Closed

Logging/Saving Settings and Instructions for Inference Jobs #1646

mohammedouhibi opened this issue May 26, 2024 · 6 comments

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@mohammedouhibi
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There needs to be a way to log or save all settings and instructions provided for every inference job that the vLLM inference server receives. This would be useful for debugging purposes, as it would allow us to track and analyze the input data and configurations used for each job.

Proposed solution :

Implement a logging mechanism that captures and stores the following information for each inference job:

  • Model parameters (e.g., model name, version, quantization settings)
  • Input data (e.g., prompt, context, or other input files)
  • Inference settings (e.g., temperature, top-k, top-p, number of tokens, etc.)
  • Timestamp and other relevant metadata
@pseudotensor
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Alot of information is stored in the save directory in history.json for every inference job. It has everything you mentioned, except not perhaps as much detail -- e.g.no input files, but just whether there are input files.

In addition, vLLM or TGI can also change its logging.

@mohammedouhibi
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Alot of information is stored in the save directory in history.json for every inference job. It has everything you mentioned, except not perhaps as much detail -- e.g.no input files, but just whether there are input files.

I cant seem to find history.json under /save, is there perhaps a run option that I'm missing?

@pseudotensor
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--save_dir=foo will place it in foo directory relative to startup in repo.

@mohammedouhibi
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mohammedouhibi commented May 28, 2024

--save_dir=foo will place it in foo directory relative to startup in repo.

Great! It works now.

though I'm missing the docs/chunks used in the job. I really think it needs to be added.
I have noticed that the docs being loaded when using the h2oGPT interface is different than those used when requesting the same instruction through a gradio client(i inferred this from the fact that the "num_prompt_tokens" value is higher when handling gradio client requests), it's causing my model to behave differently (worse in my case)

@pseudotensor
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Hi, the API call and history.json does contain 'save_dict['sources']` as the list of sources used.

@mohammedouhibi
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I see, thanks!

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