Gradient checkpointing technique for Model Agnostic Meta Learning
Project description
PyTorch implementation of Model Agnostic Meta Learning with gradient checkpointing. Allows you to perform way (~10-100x) more MAML steps with the same GPU memory budget.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
torch_maml-1.0.tar.gz
(9.7 kB
view details)
File details
Details for the file torch_maml-1.0.tar.gz.
File metadata
- Download URL: torch_maml-1.0.tar.gz
- Upload date:
- Size: 9.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: Python-urllib/3.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9fc7d91852514dc29ddd2838ac348f833eb81e778ef07b074b815d7a41ee50e8
|
|
| MD5 |
d65f34ca273c62f7eca3e92851b87cd9
|
|
| BLAKE2b-256 |
be4ca37a23fe88d41a47589e7653b398762a71d98d7dff8b2111759cc1a173e0
|
