Feature/sg 1053 add explanations on experiment managing

documentation/source/Checkpoints.md

@@ -1,5 +1,8 @@
 # Model Checkpoints
 
+*If you are not familiar on how experiments are managed, you can check [this tutorial](experiment_management.md)*
+
+
 The first question that arises is: what is a checkpoint?
 
 From the [Pytorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) documentation:

documentation/source/experiment_management.md

@@ -0,0 +1,76 @@
+# Experiment Management
+
+## Outline
+1. [Core Concepts](#core-concepts)
+   - [Checkpoint Root Directory](#checkpoint-root-directory-ckpt_root_dir)
+   - [Experiments](#experiments-experiment_name)
+   - [Runs](#runs-run_id)
+2. [File Structure of Experiments](#file-structure-of-experiments)
+3. [Utilities for Experiment Management](#utilities)
+   - [Get the Absolute Path of a Run Directory](#a-get-the-absolute-path-of-a-run-directory)
+   - [Retrieve the Latest Run ID](#b-get-the-latest-run-id)
+
+## Core Concepts
+
+### Checkpoint Root Directory (`ckpt_root_dir`)
+- The main directory where all experiment outputs are housed.
+
+### Experiments (`experiment_name`)
+- Symbolizes a distinct training recipe or configuration.
+- Alter the `experiment_name` for transparency when updating your training recipe.
+- Each training under the same `experiment_name` has its individual `run` directory, ensuring no overwrites.
+
+### Runs (`run_id`)
+- Every individual training session is termed as a `run`.
+- A unique `run_id` is generated for every training, regardless of identical parameters.
+- Different trainings under the same `experiment_name` maintain distinct logs and checkpoints, courtesy of their separate run directories.
+
+## File Structure of Experiments
+
+```
+<ckpt_root_dir>
+│
+├── <experiment_name>
+│   │
+│   ├─── <run_dir>
+│   │     ├─ ckpt_best.pth                   # Best performance during validation
+│   │     ├─ ckpt_latest.pth                 # End of the most recent epoch
+│   │     ├─ average_model.pth               # Averaged over specified epochs
+│   │     ├─ ckpt_epoch_*.pth                # Checkpoints from certain epochs (e.g., epoch 10, 15)
+│   │     ├─ events.out.tfevents.*           # Tensorflow run artifacts
+│   │     └─ log_<timestamp>.txt             # Trainer logs of that particular run
+│   │
+│   └─── <other_run_dir>
+│        └─ ...
+│
+└─── <other_experiment_name>
+    │
+    ├─── <run_dir>
+    │     └─ ...
+    │
+    └─── <another_run_dir>
+          └─ ...
+```
+
+## Utilities
+
+#### A. Get the absolute path of a run directory
+Manually navigate using `<ckpt_root_dir>/<experiment_name>/<run_dir>` or utilize the following programmatic approach:
+```python
+from super_gradients.common.environment.checkpoints_dir_utils import get_checkpoints_dir_path
+
+checkpoints_dir_path = get_checkpoints_dir_path(experiment_name="<experiment_name>", run_id="<run_id>")
+```
+
+#### B. Get the latest run id
+
+```python
+from super_gradients.common.environment.checkpoints_dir_utils import get_latest_run_id
+
+run_id = get_latest_run_id(experiment_name="<experiment_name>")
+```
+Combine with the above utility to fetch the path of the latest run directory.
+
+**Next Steps**:
+- Dive into the [checkpoints tutorial](Checkpoints.md) to grasp the essence of checkpoints, enabling you to resume trainings or access checkpoints from prior runs.
+- The [logs tutorial](logs.md) focuses on the log files stored in your run directories, offering insights into the training progression.

mkdocs.yml

@@ -30,6 +30,7 @@ nav:
     - Training: ./documentation/source/Recipes_Training.md
     - Factories: ./documentation/source/Recipes_Factories.md
     - Custom Recipes: ./documentation/source/Recipes_Custom.md
+  - Experiment Management: ./documentation/source/experiment_management.md
   - Checkpoints: ./documentation/source/Checkpoints.md
   - Docker: ./documentation/source/SGDocker.md
   - Output Adapter: ./documentation/source/DetectionOutputAdapter.md