> For the complete documentation index, see [llms.txt](https://slm-lab.gitbook.io/slm-lab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slm-lab.gitbook.io/slm-lab/v4.2.0/analyzing-results/analytics.md).

# Data Locations

## :open\_file\_folder: Folders and Files Generated by SLM Lab

Now that we know how to run SLM Lab, let's see how to interpret the data it produces.

As a run (Session, Trial, or Experiment) completes, its data is saved to the `data/` folder with an identifying **spec name** appended with a timestamp, e.g. `ppo_lunar_2019_11_30_002958`. The folder is also automatically zipped for convenient file transfer. A lab data folder is organized as follows:

```
slm_lab/data/
  ppo_lunar_2019_11_30_002958/
    graph/    # all the plotted graphs for Session, Trial, Experiment
    info/     # all the data: metrics and evaluation dataframes, etc.
    log/      # log files
    model/    # PyTorch model weights
    *graphs   # important graphs from graph/ for easy access
    *metrics  # important metrics from info/ for easy access
    *specs    # important specs from info/ for easy access
```

The data files are generated mostly by the lab's [analysis module](https://github.com/kengz/SLM-Lab/blob/master/slm_lab/experiment/analysis.py), with self-explanatory names. For most purposes, we are interested in the graphs and metrics files saved directly under the data folder produced by a run. Next, we will look at how to make use of these data files to interpret the lab results corresponding to the [Lab Organization](/slm-lab/v4.2.0/using-slm-lab/lab-organization.md).


---

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