💡 [REQUEST] - New recipe tutorial on accessing model parameters
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- Dominant language
- Python
- Stars
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Description
🚀 Describe the improvement or the new tutorial
This tutorial will help begginers understand how to access and make sense of model parameters, collect trainable parameters, and use torchinfo.summary().
Learning objectives:
- How to inspect a model's parameters using
.parameters()and.named_parameters() - How to collect the trainable parameters of a model
- How to use the
torchinfopackage (formerlytorch-summary) to print a model summary
Existing tutorials on this topic
No response
Additional context
I created this draft (https://github.com/pytorch/tutorials/pull/2914) as a part of the PyTorch Docathon H1 2024 effort. I did not realize new tutorials weren't being accepted as part of the sprint and was asked to fill out an issue and convert the PR to a draft.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the draft tutorial in pull request #2914 and compare it with the issue's learning objectives. The work is done when the tutorial explains .parameters(), .named_parameters(), collecting trainable parameters, and using torchinfo.summary() for model summaries.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100