Open shravya312 opened 1 month ago
please assign me the task @Devanik21
if u have any suggestions u can surely let me know @Devanik21
A dynamic computation graph is a key feature of PyTorch that allows the framework to create a map of calculations while the program is running.
Key Points: Built While Running: The graph is formed as you run your code, not before. This means it can change every time you execute it.
Flexible and Adaptive: You can easily change how your model behaves based on the data you provide. For example, if the input data is different, the graph adjusts accordingly.
Easy to Debug: Since the graph is created as the code runs, you can use regular Python tools to check for errors right where they happen, making it easier to fix problems.
Alright, try!🫡
Thanks a lot for giving me this opportunity @Devanik21 I will try my best!
Thanks a lot for giving me this opportunity @Devanik21 I will try my best!
hello, are u working on it? @shravya312
I am working on it,its taking time I will submit pr as soon its done can u please add labels to pull request accepted the leader board is live the pr is not reflecting @Devanik21
I am working on it,its taking time I will submit pr as soon its done can u please add labels to pull request accepted the leader board is live the pr is not reflecting @Devanik21
I have updated the labels to official ones, now it will be counted.
No not in the issue in the pr @Devanik21
in the right side labels is none yet
Please wait
haa sure 😇
the pr should be assigned to me and labels should be added
the pr should be assigned to me and labels should be added
plz check if its updated
Thanks a lot It is updated !!😇
By today it will be done @Devanik21
A dynamic computation graph is a key feature of PyTorch that allows the framework to create a map of calculations while the program is running.
Key Points: Built While Running: The graph is formed as you run your code, not before. This means it can change every time you execute it.
Flexible and Adaptive: You can easily change how your model behaves based on the data you provide. For example, if the input data is different, the graph adjusts accordingly.
Easy to Debug: Since the graph is created as the code runs, you can use regular Python tools to check for errors right where they happen, making it easier to fix problems.