# Upgrade a Dependency and Fix Breakage in TensorFlow with Atlas in 2026

> Atlas helps TensorFlow developers in 2026 upgrade dependencies and fix all resulting compile and test failures by directly interacting with uv, pytest, and black.

Atlas empowers TensorFlow developers in 2026 to confidently upgrade critical dependencies, such as bumping Keras 3 to a new major version, by automating the detection and repair of compile and test failures. It directly interacts with your TensorFlow project's pyproject.toml, drives the uv package manager, interprets pytest output, and ensures code quality with black formatting, streamlining a complex migration process.

## Key takeaways

- Atlas drives `uv` and `pytest` directly for TensorFlow dependency upgrades.
- `webfetch` provides real changelogs for accurate `tf.function` and `tf.data` fixes.
- `lsp`'s `goToDefinition` helps Atlas inspect new Keras 3 API signatures.
- All Atlas edits are permission-gated and presented as unified diffs for review.
- Atlas understands `pyproject.toml`, Keras models, and `tf.data` pipelines for context.

## How Atlas Manages TensorFlow Dependency Upgrades and Breakage in 2026

In 2026, Atlas streamlines TensorFlow dependency upgrades by driving your project's uv package manager through bash to bump versions, then systematically addressing all resulting compile and test failures. It captures the full output of uv and subsequent build commands, saving large logs to a file for your review if they exceed 100KB.

Atlas initiates dependency upgrades by executing real `uv` commands via its `bash` tool. For instance, to upgrade Keras, Atlas might run `uv update keras` within your TensorFlow project. It captures the complete output of this operation, and if the output exceeds internal limits, it automatically saves the full log to a file for your inspection. Following the upgrade, Atlas uses `webfetch` to retrieve the library's release notes and changelog. This crucial step ensures that Atlas understands the actual breaking changes, rather than guessing. This context is vital for accurately addressing issues such as altered `tf.function` signatures or modifications to `tf.data` pipeline stages, ensuring that fixes align precisely with the new API specifications documented in the release notes. Atlas operates within your project's `pyproject.toml` context, respecting pinned versions and existing configurations.

## Identifying and Fixing TensorFlow Compile and Test Failures with Atlas

Atlas identifies and fixes TensorFlow compile and test failures in 2026 by running pytest and build commands via bash, then using its edit tool to repair code. It leverages the lsp tool's goToDefinition to inspect new signatures in upgraded packages, ensuring precise fixes for issues like tf.function retracing warnings or tf.data pipeline changes.

Once a dependency like Keras 3 is upgraded, Atlas systematically identifies all resulting compile and test failures. It achieves this by executing your project's `pytest` suite and any relevant build or typecheck commands through its `bash` tool. Atlas reads and interprets the real compiler and test runner output, enumerating each error. For every identified breakage, Atlas employs its `edit` tool to propose and apply fixes directly to your TensorFlow codebase. When an API signature has changed, such as a method within `tf.keras.layers` or a parameter for `tf.function`, Atlas uses the `lsp` tool's `goToDefinition` operation. This allows it to inspect the new signatures and types within the upgraded package, ensuring that its `edit` operations are precise and correct. This iterative process of running `pytest`, fixing errors with `edit`, and re-running `pytest` continues until all tests pass and the project compiles cleanly, effectively resolving issues like `tf.function` retracing warnings or necessary adjustments to `tf.data` pipeline stages.

## Ensuring Code Quality and Safety in TensorFlow Migrations with Atlas

Atlas ensures code quality and safety during TensorFlow dependency migrations in 2026 by integrating black formatting, presenting unified diffs for every change, and enforcing permission-gated tool calls. Before any file is written or command executed, Atlas asks for approval, providing a robust safety net for your tf.data pipelines and Keras 3 models.

Safety and code quality are paramount during any dependency migration, especially in complex TensorFlow projects. Atlas integrates `black` formatting into its workflow, automatically applying it to any modified files to maintain consistent code style. Crucially, Atlas computes a unified diff for every single file edit it proposes, presenting these changes for your explicit approval before writing them to disk. This allows you to review every modification to your `tf.keras` models, `tf.data` pipelines, or `tf.function` decorated methods. All Atlas tool calls, including `bash` commands, `webfetch` requests, and `edit` operations, are permission-gated, operating under `allow`, `ask`, or `deny` rules. Atlas first drafts a plan in a read-only plan agent, asking for your approval before switching to a build agent to execute changes. Furthermore, Atlas reads your `git` branches, status, and diffs, and can stage and create commits on your behalf. It also snapshots file changes as `git` patches, providing a robust mechanism to diff edits and roll back changes if needed, offering complete control over your TensorFlow codebase.

## TensorFlow-Specific Setup for Atlas Dependency Upgrades

To prepare Atlas for TensorFlow dependency upgrades in 2026, ensure your project has a pyproject.toml that pins tensorflow and keras. Atlas will then read your Keras 3 model definitions, tf.data input pipelines, and any custom training_step overrides, providing it with the necessary context to understand your specific codebase.

For Atlas to effectively manage dependency upgrades and fix breakage in your TensorFlow project, a minimal setup is required. You must run Atlas within a project that contains a `pyproject.toml` file, which explicitly pins your `tensorflow` and `keras` versions. This configuration allows Atlas to build its internal code index using AST declarations and tree-sitter, rather than relying on blind line windows. Atlas can build this index with local Ollama embeddings, ensuring your proprietary code remains off third-party servers. With this setup, Atlas gains a deep understanding of your specific TensorFlow codebase. It reads your Keras model definitions, analyzes your `tf.data` input pipelines, and comprehends any custom `training_step` overrides you have implemented. This contextual awareness, including how `tf.function` graph tracing is applied, is critical for Atlas to accurately identify and repair issues that arise from major version bumps, ensuring its fixes are tailored to your project's unique architecture.

## Steps

1. Run `atlas` in your TensorFlow project, ensuring a `pyproject.toml` pins `tensorflow` and `keras`.
2. Ask Atlas to upgrade a specific dependency, for example, "Upgrade `keras` to its latest major version and fix all resulting compile and test failures."
3. Approve Atlas's `bash` command to run `uv update keras` and capture its output, saving large logs to a file if necessary.
4. Review the release notes fetched by Atlas using `webfetch` for the upgraded `keras` package to understand breaking changes.
5. Approve Atlas's `bash` command to run `pytest` and any build/typecheck commands to enumerate initial failures.
6. Approve Atlas's `edit` operations as it fixes each error, using `lsp`'s `goToDefinition` to inspect new `tf.keras` or `tf.function` signatures.
7. Approve Atlas's repeated `bash` commands to run `pytest` and build until all tests pass and the project compiles cleanly.
8. Review the unified diff of all changes, including `black` formatting, and approve Atlas to stage and commit the changes.

## FAQ

### How does Atlas handle `tf.function` retracing warnings after a dependency upgrade?

Atlas uses `lsp`'s `goToDefinition` to inspect new function signatures in upgraded packages, then applies precise `edit` operations to resolve `tf.function` retracing warnings, ensuring optimal graph tracing.

### Can Atlas upgrade Keras 3 dependencies in my TensorFlow project?

Yes, Atlas can upgrade Keras 3 dependencies. It reads your Keras model definitions from `pyproject.toml` and uses `uv` to manage the upgrade, then fixes any resulting API breakages.

### What package manager does Atlas use for TensorFlow projects?

Atlas drives `uv`, the Python package manager, through `bash` commands to perform dependency upgrades and installations in TensorFlow projects.

### How does Atlas ensure my TensorFlow tests pass after an upgrade?

Atlas repeatedly runs `pytest` via `bash` after each set of fixes, iterating until all tests pass cleanly, ensuring the upgraded TensorFlow project remains functional.

### Does Atlas format my TensorFlow code with `black` during a migration?

Yes, Atlas integrates `black` formatting into its workflow. After making edits to fix dependency breakage, it automatically formats the modified TensorFlow code and includes it in the final diff for review.

### How does Atlas know about `tf.data` pipeline changes?

Atlas indexes your code by AST declarations, including `tf.data` input pipelines. When a dependency upgrade alters an API, Atlas uses `webfetch` for changelogs and `lsp` to inspect new signatures, then applies targeted `edit` fixes.

### Is it safe to let Atlas modify my TensorFlow codebase?

Yes, Atlas is designed for safety. Every tool call is permission-gated, and all file edits are presented as unified diffs for your approval before being written, allowing you to review changes to your TensorFlow project.

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