lucid.grad_enabled¶
- lucid.grad_enabled() bool¶
The grad_enabled function returns the current state of gradient tracking, which indicates whether gradients are being computed for tensor operations.
Function Signature¶
def grad_enabled() -> bool
Returns¶
- bool:
True if gradients are being tracked.
False if gradients are disabled.
Usage¶
The grad_enabled function is used to check if gradient computation is currently enabled.
This can be useful for debugging or ensuring that gradient tracking is appropriately enabled or disabled in certain parts of the code.
Example¶
>>> import lucid
>>> print(lucid.grad_enabled()) # Output: True (assuming gradients are enabled by default)
>>> with lucid.no_grad():
>>> print(lucid.grad_enabled()) # Output: False (inside the no_grad context manager)
In the example above, grad_enabled shows whether gradient tracking is on or off at any given point in the code.
How It Works¶
The function simply returns the value of the global _grad_enabled variable, which determines if gradient computation is enabled. When grad_enabled() is called, it reflects the state of the gradient tracking system, helping you determine if operations are being performed with or without gradient tracking.
Note
The global _grad_enabled variable is modified by context managers like no_grad() to disable gradients during specific operations.
Potential Pitfalls¶
Using grad_enabled to check the gradient state is straightforward, but consider the following:
Caution
The state returned by grad_enabled is global, which means if other parts of your code modify the gradient state, it will affect the result of grad_enabled.
Since grad_enabled only returns the current state, ensure that its use does not interfere with the dynamic behavior of gradient tracking in your program.
Conclusion¶
The grad_enabled function provides a simple and effective way to check the current gradient tracking state in your code. It is particularly useful for debugging and controlling the flow of operations that require or do not require gradients.