adam-atan2-pytorch
Adam-atan2 for Pytorch
Decision gist · record as of 2026-08-14
Yes, if you use PyTorch and want to experiment with a theoretically motivated Adam variant. Low install friction, permissive license, no known vulnerabilities, and active maintenance make it a low-risk addition. Best suited for researchers exploring optimizer variants or teams dealing with scale-invariance issues; standard Adam remains the safer default for most production training.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyPI installation of torch and einops; Python 3.9 or later.
- Low install friction with only two runtime dependencies (einops and torch).
- Active maintenance status with a recent release 28 days ago.
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and distribution with only attribution and liability disclaimer required—no restrictions on commercial or private use.
last release 2026-07-17 (28 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 270,109 downloads/mo, #8,242 on PyPI
Alternatives
Verify before relying
pip install adam-atan2-pytorch
import torch
from adam_atan2_pytorch import AdamAtan2
model = torch.nn.Linear(10, 1)
opt = AdamAtan2(model.parameters(), lr=1e-4)
loss = model(torch.randn(10))
loss.backward()
opt.step()
opt.zero_grad()- Whether plasticity features are enabled by default or require explicit configuration.
- Performance comparison with standard Adam on typical training tasks.
- Specific continual learning scenarios where plasticity features provide measurable benefit.
What it is and what it does
Adam-atan2-pytorch implements a modified Adam optimizer that replaces the epsilon term with atan2 to achieve numerical stability without requiring manual epsilon tuning. The core change is algorithmic—using atan2 in the update rule removes the need for epsilon altogether while maintaining scale invariance across different parameter ranges. This is based on research from DeepMind proposing the atan2 modification.
The package also includes features for improving plasticity in continual learning scenarios, where models must learn new tasks without catastrophic forgetting of old ones. It integrates with PyTorch's standard optimizer interface, so you instantiate it with model parameters and a learning rate, then call step() and zero_grad() in your training loop like any other optimizer. The implementation depends on einops for tensor operations and torch itself.
Use it for
- Replace standard Adam in deep learning models where epsilon tuning is problematic or parameter scales vary widely.
- Continual learning pipelines where maintaining plasticity across task sequences is critical.
- Training scenarios where numerical stability without manual epsilon adjustment is desired.
- Research comparing optimizer variants with scale-invariant update rules.
- Large-scale training where removing epsilon hyperparameter reduces tuning overhead.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use PyTorch and want to experiment with a theoretically motivated Adam variant.
Low install friction, permissive license, no known vulnerabilities, and active maintenance make it a low-risk addition. Best suited for researchers exploring optimizer variants or teams dealing with scale-invariance issues; standard Adam remains the safer default for most production training.
Install
adam-atan2-pytorch on PyPI
Before you install
Low install friction with only two runtime dependencies (einops and torch). Active maintenance status with a recent release 28 days ago.
Requires PyPI installation of torch and einops; Python 3.9 or later.
License in practice
MIT License permits unrestricted use, modification, and distribution with only attribution and liability disclaimer required—no restrictions on commercial or private use.
Quickstart
pip install adam-atan2-pytorch
import torch
from adam_atan2_pytorch import AdamAtan2
model = torch.nn.Linear(10, 1)
opt = AdamAtan2(model.parameters(), lr=1e-4)
loss = model(torch.randn(10))
loss.backward()
opt.step()
opt.zero_grad()Verify before relying
- Whether plasticity features are enabled by default or require explicit configuration.
- Performance comparison with standard Adam on typical training tasks.
- Specific continual learning scenarios where plasticity features provide measurable benefit.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageseinopstorch |
| Maintenance | Actively maintained 28 days since the last release |
| First released | |
| Downloads | 270,109 / month, #8,242 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: adam_atan2_pytorch-0.4.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “adam optimizer pytorch”
- adam-atan2-pytorchProvides an Adam optimizer variant using atan2 for numerical…
- lion-pytorchLion is a PyTorch optimizer that implements an evolved sign momentum…
- flashoptimFlashOptim provides drop-in replacements for PyTorch optimizers (SGD,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also hyper-connections · lion-pytorch · pytorch-ranger · flashoptim · schedulefree · ema-pytorch · pytorch_optimizer · opacus · tomesd · torchaudio