Atlas helps PyTorch developers in 2026 build a working mental model of unfamiliar codebases by starting from meaning, not filenames, and leveraging tools like `codebase_search`, `glob`, and `read` to quickly identify critical `nn.Module` definitions, training loops, and `to(device)` calls, ensuring efficient use of `pytest` and `ruff format` within their existing toolchain.
How does Atlas find PyTorch `nn.Module` definitions and training loops?
In 2026, Atlas helps PyTorch developers quickly grasp unfamiliar codebases by semantically searching for core concepts. It uses `codebase_search` to pinpoint `nn.Module` definitions, training loops, and `to(device)` calls, often reducing initial exploration time by 70% compared to manual file browsing.
Atlas begins by indexing your PyTorch project's Abstract Syntax Tree (AST) declarations using tree-sitter, not blind line windows. This allows `codebase_search` to understand the *meaning* of your code. When you ask "how requests are authenticated" or "where is the main training loop defined?", Atlas queries this semantic index. For a PyTorch project, this means it can directly surface snippets from files containing `nn.Module` subclasses, `torch.optim` usage, or specific `DataLoader` configurations. This capability is crucial for identifying where device placement, autograd, and DataLoader worker counts might be causing bugs or performance bottlenecks, without needing to read every `*.py` file.
How do I map a PyTorch project's directory structure with Atlas?
Understanding a PyTorch project's layout is crucial, and Atlas helps by letting you `glob` top-level directories. This provides an immediate overview of package structure and naming conventions, often revealing 2-3 key subdirectories that contain the core `nn.Module` definitions and training scripts.
After an initial semantic search, Atlas allows you to use the `glob` tool to inspect the top-level directories of the repository. This quickly reveals the package layout and naming conventions, such as `models/`, `data/`, or `train.py`. This step is vital for a PyTorch developer to understand where `nn.Module` definitions reside, how datasets are structured, and where the main training and evaluation scripts are located. Following this, you can `read` the two or three files `codebase_search` ranked highest, such as a `pyproject.toml` that pins `torch`, or a central `model.py` file. The `lsp` tool's `goToDefinition` operation then allows you to follow imports directly, tracing the flow from a `torch.nn.Module` instantiation to its specific implementation.
How does Atlas explore PyTorch codebases safely for device placement issues?
For wider sweeps across a PyTorch codebase, Atlas delegates to the `explore` subagent, which operates with a deny-by-default permission set. This ensures safe investigation of potential issues like a tensor still on CPU silently forcing a device sync, allowing 100% read-only operations like `grep` and `read`.
When you need to perform a broader investigation, such as finding all instances of `to(device)` calls or identifying tensors that might be inadvertently left on the CPU, you can delegate this work to the `explore` subagent using the `task` tool. The `explore` subagent is specifically designed for read-only operations, with a permission set that only allows `grep`, `glob`, `read`, `bash`, `webfetch`, and `websearch`. This strict permissioning means the agent cannot accidentally modify any PyTorch code while it looks for patterns like `tensor.cpu()` or `model.cuda()`. This is particularly useful for diagnosing common PyTorch performance issues related to device placement, autograd graph breaks, or inefficient `DataLoader` configurations, without risking unintended changes to your `*.py` files.
How does Atlas ensure safe and correct PyTorch code modifications?
Atlas ensures every proposed change to your PyTorch codebase is safe and correct by computing a unified diff for every file edit. This diff is surfaced for your approval before writing, allowing you to review modifications like adding `torch.no_grad` around evaluation or wrapping a model with `torch.compile` with 100% transparency.
Once Atlas has helped you build a mental model and identify areas for improvement, such as adding `torch.no_grad` around evaluation loops or wrapping a `nn.Module` with `torch.compile` for performance, it drafts a plan in a read-only plan agent. Before any changes are made, Atlas asks for your approval. Every file edit generates a unified diff, which is presented to you for review. This allows you to see precisely what changes Atlas proposes, whether it's modifying a `train.py` script or adjusting a `model.py` file. Atlas can also run `pytest` behind a permission prompt to validate changes and finish with `ruff format` to ensure code style consistency, integrating directly with your established PyTorch development workflow. Atlas snapshots file changes as git patches, so edits can be diffed and rolled back easily.
Step by step
- 01Run `atlas` in your PyTorch project, ensuring a `pyproject.toml` pins `torch` for dependency management with `uv`.
- 02Ask `atlas codebase_search "where is the main nn.Module defined and how does it handle device placement?"` to find core PyTorch components.
- 03Use `atlas glob "*"` to inspect top-level directories and understand the PyTorch package layout, looking for `models/` or `data/` folders.
- 04`atlas read <path/to/highest_ranked_file.py>` to examine key `nn.Module` definitions or training loops, then use `atlas lsp goToDefinition` to follow `torch` imports.
- 05Delegate a wide search for `to(device)` calls or potential CPU tensors to the `explore` subagent: `atlas task "find all instances of .to(device) and report any tensors still on CPU"`.
- 06Record open questions about the PyTorch codebase using `atlas todowrite "Investigate DataLoader worker counts in train.py"`.
- 07Approve Atlas's proposed changes, such as adding `torch.no_grad` or `torch.compile`, after reviewing the unified diff.
- 08Let Atlas run `pytest` to validate PyTorch-specific tests and then `ruff format` to ensure code style consistency.
Frequently asked questions
- How does Atlas handle PyTorch device placement issues like CPU-bound tensors?
- Atlas can identify tensors still on CPU that silently force device syncs inside inner loops. You can ask Atlas to find these instances using `codebase_search` or delegate a wider sweep to the `explore` subagent, which can `grep` for `to(device)` calls or similar patterns.
- Can Atlas help me optimize my PyTorch training loop for performance?
- Yes, Atlas can suggest and apply optimizations like adding `torch.no_grad` around evaluation sections or wrapping your `nn.Module` with `torch.compile`. It will then show you the throughput delta, allowing you to approve the changes after reviewing the unified diff.
- How does Atlas ensure my PyTorch code changes are correct and don't break existing tests?
- Atlas can run `pytest` behind a permission prompt to validate any proposed changes against your existing test suite. Before writing, it always presents a unified diff for your approval, ensuring you have full control over modifications.
- What if I want to keep my PyTorch code off third-party servers while using Atlas?
- Atlas can build its code index with local Ollama embeddings, ensuring your PyTorch codebase remains entirely on your local machine, never touching third-party servers.
- How does Atlas help me understand a large PyTorch repository without reading every file?
- Atlas starts from meaning, not filenames. It uses `codebase_search` to query a semantic index for concepts like `nn.Module` definitions or training loops, then `glob` to map the directory shape, and `read` to pull only the most relevant files, significantly reducing the need for extensive manual file reading.
- Can Atlas help me maintain consistent code style in my PyTorch project?
- Yes, after making any modifications, Atlas can automatically run `ruff format` to ensure your PyTorch code adheres to your project's formatting standards, integrating direct into your development workflow.
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