Can Atlas help me understand device placement issues in my PyTorch model?

Yes, Atlas can use `codebase_search` and `lsp` in plan mode to find all `model.to(device)` calls and analyze tensor device placement. It can help identify tensors still on CPU that might silently force device syncs inside your inner loop, a common source of slowness in PyTorch.

What PyTorch-specific tools does Atlas integrate with for planning?

While planning, Atlas uses its internal `codebase_search`, `grep`, `read`, and `lsp` tools to understand your PyTorch project. For implementation, it integrates with your actual toolchain, including `pytest` for testing, `uv` for package management, and `ruff format` for code formatting.

How do I get my multi-file PyTorch change plan reviewed before implementation?

You draft your detailed plan into a markdown file using Atlas's plan agent. This document can then be shared and reviewed by your team. Only after approval do you use the `plan_exit` tool to switch to the build agent and begin actual code modifications.

Can Atlas help me optimize my PyTorch training loop during the planning phase?

During planning, Atlas can research your training loop, `DataLoader` configurations, and `torch.no_grad()` usage. It can suggest optimizations like adding `torch.no_grad` around evaluation or wrapping the model with `torch.compile`, detailing these in your plan before any code is changed.

What happens if I decide not to proceed with the plan after drafting it?

If you decide not to proceed with the plan, you can answer 'No' when prompted by the `plan_exit` tool. Atlas will then raise a `Question.RejectedError` and keep you in the plan agent, allowing you to refine or discard the plan without any code changes being made.

Plan a Multi-File Change Before Editing in PyTorch with Atlas in 2026

Atlas empowers PyTorch developers in 2026 to design and review multi-file changes comprehensively before any code is modified, ensuring safety and alignment with the existing codebase. This approach integrates direct with your PyTorch toolchain, allowing Atlas to research your `nn.Module` definitions, `DataLoader` configurations, and `torch.to(device)` calls, then draft a plan that you can refine and get reviewed, all before running `pytest` or `ruff format` on actual edits.

How Atlas Plans Multi-File PyTorch Changes Safely

Atlas's dedicated plan agent provides a secure environment for designing multi-file PyTorch changes, ensuring no accidental edits occur. In 2026, this agent operates with a strict permission set, allowing write access only to a designated plan markdown file, typically located under `.atlas/plans/*.md`, while disallowing all other edit tools.

When you initiate Atlas in plan mode, it activates a specialized agent whose primary function is research and design. This agent is explicitly configured to deny all edit operations across your PyTorch project, except for writing to a specific plan markdown file. This means you can safely use Atlas's powerful research tools,`codebase_search`, `grep`, `read`, and `lsp`,to explore your `nn.Module` definitions, analyze `torch.to(device)` calls, or investigate `DataLoader` worker counts without any risk of unintended modifications. For instance, you might ask Atlas to `codebase_search` for all instances of `model.to(device)` to understand device placement patterns across your training loop and evaluation scripts. The plan agent's read-only nature for your source code guarantees that your PyTorch codebase remains untouched until you explicitly approve the transition to an implementation phase.

Researching PyTorch Codebase Details with Atlas's Plan Agent

To effectively plan a multi-file change in PyTorch, Atlas's plan agent provides robust research capabilities, allowing you to deeply understand your codebase without risk. You can use tools like `grep` and `lsp` to inspect `pyproject.toml` dependencies or analyze `nn.Module` definitions, ensuring your proposed changes are well-informed by 2026 standards.

Within the plan agent, Atlas provides a suite of tools to thoroughly research your PyTorch project. You can use `grep` to find specific patterns, such as all occurrences of `torch.no_grad()` or `torch.compile()` within your training and evaluation scripts. The `read` tool allows you to inspect the contents of files like `pyproject.toml` to understand pinned `torch` versions or `requirements.txt` for other dependencies managed by `uv`. Crucially, the `lsp` tool provides semantic understanding, letting Atlas analyze your `nn.Module` definitions, track tensor shapes, and identify where `DataLoader` instances are configured. For example, you could ask Atlas to use `lsp` to find all usages of a specific custom `nn.Module` subclass or to identify all `torch.optim` optimizers defined in your `train.py` file. This detailed research capability ensures that your multi-file plan accounts for the specific idioms and structures present in your PyTorch codebase, from device placement logic to autograd graph interactions.

Drafting and Refining Your PyTorch Change Plan

Once research is complete, Atlas's plan agent allows you to draft your multi-file PyTorch change plan directly into a markdown file, such as `.atlas/plans/refactor_device_placement.md`. This document serves as your blueprint, detailing proposed modifications to `nn.Module`s or `DataLoader` configurations, and can be refined iteratively before any actual code is touched in 2026.

The core output of the plan agent is a detailed markdown document outlining your proposed multi-file change. This is the only location where the plan agent has write permissions. You can instruct Atlas to write down its findings and proposed steps, for example, 'Add `torch.no_grad()` around evaluation loops in `model_eval.py` and `test_script.py`' or 'Modify `data_loader.py` to increase `num_workers` for `DataLoader` instances.' As you refine the plan, Atlas can update this markdown file, incorporating new research findings or adjusting the proposed changes based on your feedback. This iterative drafting process ensures that the final plan is comprehensive, addresses all identified issues (like tensors silently forcing device syncs), and is ready for review by your team. The plan markdown file becomes a living document, capturing the design rationale and specific file paths like `src/models/my_model.py` or `configs/training_config.yaml` that will be affected.

Review and Safety: Approving PyTorch Code Implementation

Atlas ensures a safe transition from planning to implementation for your PyTorch changes through a explicit approval step. After drafting your plan, you use the `plan_exit` tool, which prompts you to confirm the switch to the build agent, preventing accidental code modifications. This critical 2-step process guarantees that your team can review the plan before any `pytest` runs or `ruff format` operations are initiated.

The transition from planning to actual code modification is permission-gated and requires your explicit approval. Once your multi-file PyTorch change plan is complete and reviewed, you invoke the `plan_exit` tool. Atlas will then present a clear prompt: 'Plan at <path> is complete. Would you like to switch to the build agent and start implementing?' Answering 'Yes' signals your intent to proceed, handing control to the build agent which has the necessary permissions to modify files, run `pytest` for testing, and apply `ruff format` for code style. If you answer 'No', Atlas raises a `Question.RejectedError`, keeping you in the plan agent to further refine your design. This robust approval mechanism ensures that no changes are made to your `nn.Module` definitions, training loops, or `torch.to(device)` calls without a deliberate decision, providing a crucial safety net for complex PyTorch refactors.

Step by step

01Start Atlas in your PyTorch project, ensuring a `pyproject.toml` pins `torch` and Atlas can read your `nn.Module` definitions.
02Switch to the plan agent by typing a command like `atlas plan` or by selecting the plan agent from the agent menu. Its permissions will deny edits for `*` and allow only under `.atlas/plans/*.md`.
03Research your PyTorch codebase using `codebase_search` to find `DataLoader` configurations, `grep` for `torch.no_grad()` calls, `read` to inspect `train.py`, and `lsp` to analyze `nn.Module` definitions and `torch.to(device)` calls.
04Draft your multi-file change plan into the allowed markdown path, for example, `.atlas/plans/optimize_device_sync.md`, detailing proposed changes to `model.py` and `utils.py`.
05Iteratively refine the plan, asking Atlas to update the markdown with more specific details, such as 'Add `torch.compile` to `my_model.py`' or 'Adjust `num_workers` in `data_loader.py` based on throughput delta analysis.'
06Once the plan is complete and reviewed, call the `plan_exit` tool. Atlas will ask: 'Plan at .atlas/plans/optimize_device_sync.md is complete. Would you like to switch to the build agent and start implementing?'
07Answer 'Yes' to hand off to the build agent, which can then proceed with implementing the changes, running `pytest` for verification, and applying `ruff format` for consistency.

Frequently asked questions

How does Atlas prevent accidental edits to my PyTorch code during planning?: Atlas's plan agent operates with a strict permission set that denies all edit tools for your PyTorch source code, allowing write access only to a designated plan markdown file. This ensures your `nn.Module` definitions and training loops remain untouched until you explicitly approve implementation.
Can Atlas help me understand device placement issues in my PyTorch model?: Yes, Atlas can use `codebase_search` and `lsp` in plan mode to find all `model.to(device)` calls and analyze tensor device placement. It can help identify tensors still on CPU that might silently force device syncs inside your inner loop, a common source of slowness in PyTorch.
What PyTorch-specific tools does Atlas integrate with for planning?: While planning, Atlas uses its internal `codebase_search`, `grep`, `read`, and `lsp` tools to understand your PyTorch project. For implementation, it integrates with your actual toolchain, including `pytest` for testing, `uv` for package management, and `ruff format` for code formatting.
How do I get my multi-file PyTorch change plan reviewed before implementation?: You draft your detailed plan into a markdown file using Atlas's plan agent. This document can then be shared and reviewed by your team. Only after approval do you use the `plan_exit` tool to switch to the build agent and begin actual code modifications.
Can Atlas help me optimize my PyTorch training loop during the planning phase?: During planning, Atlas can research your training loop, `DataLoader` configurations, and `torch.no_grad()` usage. It can suggest optimizations like adding `torch.no_grad` around evaluation or wrapping the model with `torch.compile`, detailing these in your plan before any code is changed.
What happens if I decide not to proceed with the plan after drafting it?: If you decide not to proceed with the plan, you can answer 'No' when prompted by the `plan_exit` tool. Atlas will then raise a `Question.RejectedError` and keep you in the plan agent, allowing you to refine or discard the plan without any code changes being made.

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