From cd9c48def2bc0505c3426a55e23a6b7cf17a3428 Mon Sep 17 00:00:00 2001
From: pollfly <75068813+pollfly@users.noreply.github.com>
Date: Sun, 18 Sep 2022 10:23:17 +0300
Subject: [PATCH] Update FAQ about parameter logging (#333)
---
docs/faq.md | 11 ++++++++---
1 file changed, 8 insertions(+), 3 deletions(-)
diff --git a/docs/faq.md b/docs/faq.md
index 75658b0e..ebe3e5d2 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -283,10 +283,13 @@ to reproduce. You can see uncommitted changes in the ClearML Web UI, in the EXEC
**I do not use argparse for hyperparameters. Do you have a solution?**
-Yes! ClearML supports connecting hyperparameter dictionaries to experiments, using the [Task.connect](references/sdk/task.md#connect) method.
+Yes! ClearML also automatically captures and tracks command line parameters created using [click](https://click.palletsprojects.com/),
+[Python Fire](https://github.com/google/python-fire), and/or [LightningCLI](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.utilities.cli.html).
-For example, to log the hyperparameters `learning_rate`, `batch_size`, `display_step`,
-`model_path`, `n_hidden_1`, and `n_hidden_2`:
+Additionally, ClearML supports connecting hyperparameter dictionaries to experiments, using the [Task.connect](references/sdk/task.md#connect) method.
+
+For example, the code below connects hyperparameters (`learning_rate`, `batch_size`, `display_step`,
+`model_path`, `n_hidden_1`, and `n_hidden_2`) to a task:
```python
# Create a dictionary of parameters
@@ -296,6 +299,8 @@ parameters_dict = { 'learning_rate': 0.001, 'batch_size': 100, 'display_step': 1
# Connect the dictionary to your CLEARML Task
parameters_dict = Task.current_task().connect(parameters_dict)
```
+
+See more information about logging of hyperparameters [here](fundamentals/hyperparameters.md).