Introduction to Metrax: Evaluation metrics for JAX

Table of Contents

• Overview 🧭
• Why a JAX-native metrics library matters ⚡
• Core capabilities and scope 🧩
• Ranking metrics at K — a feature I use often 🔢
• NLP and speech metrics 🗣️
• Vision metrics 🖼️
• The three-step API: create, merge, compute 🛠️
• Performance and jittability 🚀
• Integration into JAX workflows 🧭
• Real-world usage and adoption 🏢
• Community-driven development and licensing 🤝
• Best practices and pitfalls to avoid ⚠️
• How I think about metric selection and reporting 📊
• Examples of typical workflows 🔍
• Metric examples and what they tell you 🧾
• Extending Metrax and contributing 🚧
• Documentation and examples 📚
• Comparisons to existing tooling 🔬
• Practical tips for large-scale evaluation 🛡️
• What I hope teams take away 🌟
• Frequently asked questions ❓
• Final notes and next steps ✨

Overview 🧭

I’m Yufeng Guo. I built and helped shape Metrax to solve a practical problem I kept running into: evaluating machine learning models at scale should be fast, reliable, and free from repeated reimplementation work. Metrax is a JAX-native, high-performance, open source library that provides battle-tested implementations of common evaluation metrics so you can focus on model development and analysis rather than re-verifying metric definitions.

Evaluation might sound straightforward on paper. Accuracy, precision, recall — these are familiar terms. But when you train and evaluate models across distributed compute environments, with large batches, varying sequence lengths, and a need for compiled pipelines, evaluation becomes a nontrivial engineering challenge. Metrax addresses those challenges by providing a consistent API, jittable implementations where possible, and a wide range of metrics spanning classification, ranking, natural language processing, speech, and vision.

Why a JAX-native metrics library matters ⚡

JAX has become a backbone of modern ML research and production workflows because of its composability, automatic differentiation, and powerful compilation primitives like jit and vmap. When evaluation code is written outside of the JAX paradigm, you lose many of those benefits: compiled pipelines, fast vectorized operations, and smooth integration into training loops.

Metrax is designed to take full advantage of JAX. Many of the metrics are written so they can be wrapped by jax.jit. That means the metric computation itself can be part of the compiled graph, letting you compute large-scale metrics with the same speed characteristics as your model forward pass.

In practice this translates to benefits such as:

• Faster evaluation in tight inner loops when metrics are jittable and vectorized.
• Consistency when evaluating with the same numerical behavior across devices.
• Reduced engineering overhead because you don’t need to reimplement optimized versions of every metric to get performance.

Core capabilities and scope 🧩

Metrax focuses on offering a comprehensive suite of standard evaluation metrics implemented in a consistent, functional API. The library covers metrics across several domains:

• Classification and regression: accuracy, precision, recall, F1, mean squared error, and more.
• Ranking and recommender systems: precision at k, recall at k, NDCG, mean reciprocal rank — including the ability to compute multiple values of k in a single pass.
• Natural language processing: perplexity, BLEU, ROUGE and token-level metrics.
• Speech and text generation: word error rate and edit-distance-based scoring.
• Vision: intersection over union for segmentation, PSNR and SSIM for image quality.

My goal was to provide both breadth and depth: the right balance of commonly used metrics that are implemented correctly and optimized where possible, with room for future expansion driven by community needs.

Ranking metrics at K — a feature I use often 🔢

One of my favorite Metrax features is how it handles ranking metrics at K. In recommender systems and search, evaluating ranking quality for different cutoff values is essential. Rather than computing precision@1, precision@8, precision@20 in separate passes, Metrax can compute multiple at-K metrics in parallel within a single forward pass.

This design produces two practical advantages. First, it reduces computation by doing the heavy work once and extracting multiple scalars from the result. Second, it reduces numerical and implementation inconsistency that can creep in when people implement multiple near-identical metrics separately.

For common ranking evaluations I often compute several metrics in tandem — precision@k, recall@k, and NDCG@k — for an array of k values. Metrax makes this both efficient and easy to reason about.

NLP and speech metrics 🗣️

Language tasks come with many domain-specific evaluation needs. Metrax includes standard NLP metrics like perplexity, BLEU, and ROUGE. For speech recognition and text generation, the library includes word error rate, which measures edit distance at the token level.

Implementing these correctly is surprisingly subtle. Tokenization differences, normalization rules (case, punctuation), and handling of variable-length sequences all affect scores. Metrax provides sane defaults and consistent implementations so teams don’t have to reinvent normalization or naive edit-distance code repeatedly.

Perplexity is straightforward but important: when computed using the model’s log-likelihoods it becomes a direct indicator of generative model quality. BLEU and ROUGE are helpful for translation and summarization tasks, and they are implemented to match standard definitions closely.

Vision metrics 🖼️

Computer vision evaluation also features domain-specific metrics. Metrax implements metrics such as mean Intersection over Union (IoU) for semantic segmentation and image quality metrics like PSNR and SSIM. These metrics depend on pixelwise operations and occasionally windowed computations, so having vectorized, numerically stable implementations in JAX is very valuable.

One advantage of using a single library across vision and language tasks is consistency in API and numerical behavior. Whether I am evaluating segmentation masks or language generation, I reach for the same pattern: create, merge, compute.

The three-step API: create, merge, compute 🛠️

To keep the usage model simple and consistent, Metrax exposes a functional API with three main operations:

1. create — instantiate the metric state from model outputs and references.
2. merge — update or accumulate metric state across additional batches or shards.
3. compute — finalize and return the scalar or structured metric results.

That pattern fits naturally into training and evaluation loops. The flow usually looks like this: create metric state from a batch, inside a loop merge each subsequent batch into that state (possibly via a reduce on multiple devices), and then call compute at the end to obtain the final aggregated metrics.

Because the API is functional, it composes nicely with JAX primitives. For jittable metrics this means you can embed metric accumulation in a compiled loop or use vectorized map across multiple inputs with jax.vmap. For non-jittable metrics, a well-written implementation still ensures robust correctness and good performance.

Performance and jittability 🚀

Not every metric can be compiled by jax.jit due to Python-side control flow, external library dependencies, or operations that depend on Python objects. Still, Metrax follows best practices to maximize jittability where feasible.

When a metric is jittable you gain several benefits:

• Lower overhead per batch because JAX compiles the metric computation into optimized XLA kernels.
• Better device utilization in multi-device setups.
• Fewer data transfers between host and device if the accumulation remains on-device.

When metrics are not jittable, the library keeps them efficient and well-structured, so that you can still run evaluation at scale without excessive engineering effort. The goal is to provide as many jittable implementations as it makes sense while maintaining correctness and clarity for the others.

Integration into JAX workflows 🧭

Integration is straightforward. I designed Metrax so that typical usage patterns for model training or evaluation map cleanly onto the library's API. The three-step create, merge, compute model aligns well with the way JAX users structure their loops and device parallelism strategies.

Common integration patterns include:

• Computing metrics per-step and aggregating them with jax.lax collectives during distributed training.
• Running evaluation after checkpoints where compiled metrics are used to accelerate the evaluation pass.
• Using Metrax as part of a larger observability or post-training pipeline that computes many metrics across multiple datasets.

Because the API is consistent across metric types, I find it easy to mix and match metrics for multi-task models or to reuse the same evaluation driver across projects.

Real-world usage and adoption 🏢

Metrax is used by teams inside Google across demanding production environments. Teams across Search, YouTube, and Google’s post-training tooling leverage Metrax for robust, high-performance evaluation. That production usage helped shape design decisions focused on correctness, stability, and integration into large-scale workflows.

Seeing Metrax run reliably across different products gave me confidence that it handles real-world edge cases, large inputs, and distributed evaluation patterns. The aim is to provide an open library that others can adopt without surprising behavior or hidden assumptions.

Community-driven development and licensing 🤝

Metrax is developed as a GitHub-first, open-source project under the Apache 2.0 license. A number of metrics and improvements were added by external contributors, and the project actively welcomes pull requests and issues.

If you need a metric that isn’t present, or if you have an optimized implementation for a particular case, contributing that back keeps the library growing in a way that benefits everyone. The repository includes documentation with metric definitions to make it easier to verify behavior and ensure consistency.

Best practices and pitfalls to avoid ⚠️

Over the years I’ve seen teams make the same mistakes evaluating models at scale. Here are a few practical pointers based on that experience:

• Be explicit about preprocessing. Tokenization, normalization, and label alignment affect metric outcomes. Use canonical preprocessing or clearly document your pipeline.
• Avoid ad hoc metric reimplementation. Subtle bugs in metrics are common and costly. Use an established library or cross-check against trusted references.
• Prefer jittable patterns when you need high throughput. Structure your metric computation to allow JAX compilation where possible.
• Aggregate deterministically. When combining results from shards or replicas, ensure consistent aggregation to avoid non-repeatable scores.
• Choose the right metric for the question. Accuracy is not always the most informative metric; task-specific measures like NDCG or IoU often tell a better story.

These practices save time and prevent ambiguous results that slow down model iteration.

How I think about metric selection and reporting 📊

Picking the right metric is part science and part judgment. I typically follow this approach:

1. Start with task-aligned metrics. For ranking, use precision@k, recall@k, NDCG. For segmentation, use IoU. For generation, consider BLEU and ROUGE along with human evaluations.
2. Report both aggregate and granular views. A single scalar loses important information. Report distributions, per-slice metrics, and error modes.
3. Use multiple complementary metrics. For example, BLEU and ROUGE capture different aspects of text similarity. Pair them to get a fuller picture.
4. Track metric stability over time and across releases. Small changes in preprocessing or tokenization can lead to unexpected shifts.

Good metrics allow you to make reproducible claims about model progress. Metrax was built to give teams confidence that the numbers are computed correctly and consistently.

Examples of typical workflows 🔍

Here are three concise patterns that illustrate how teams commonly use Metrax in practice.

Evaluation after checkpointing

After a training run completes or a checkpoint is saved, teams often run a compiled evaluation pass on a held-out dataset. They will:

• Instantiate metric state with create.
• Loop over the evaluation dataset, merging batches into the state.
• Call compute to produce final metrics that are logged or tracked.

Online logging for monitoring

Metrics can be computed at short intervals and pushed to dashboards. For efficiency, jittable metrics let teams compute metrics without incurring heavy CPU-to-GPU transfers every step.

Distributed evaluation

When evaluating across multiple devices or machines, the merge step is where deterministic aggregation logic is required. Teams typically perform device-local accumulation and then combine shards using collectives so the final compute step returns the same numbers regardless of device count.

Metric examples and what they tell you 🧾

Understanding what a metric measures helps interpret results. Below are brief descriptions of a few commonly used metrics and how I use them.

• Accuracy: Percentage of correct predictions. Useful for balanced classification but misleading with class imbalance.
• Precision and Recall: Precision measures false positive rate with respect to predicted positives; recall measures false negatives with respect to true positives. I often examine both and the F1 score to balance trade-offs.
• F1: Harmonic mean of precision and recall. Use when both false positives and false negatives matter.
• Perplexity: Exponential of average negative log-likelihood used in language modeling. Lower perplexity indicates better language modeling.
• BLEU/ROUGE: Overlap-based n-gram metrics for translation and summarization. Complement them with qualitative checks.
• Word Error Rate: Edit distance normalized by reference length for speech recognition. A direct, interpretable measure of transcription quality.
• IoU: Intersection over union for segmentation masks. Robust to class imbalance and spatial overlap considerations.
• PSNR/SSIM: Image quality metrics. PSNR measures signal-to-noise ratio; SSIM captures perceived structural similarity.

Extending Metrax and contributing 🚧

The library is intentionally open to contributions. If you want to add a metric, follow these general steps:

1. Implement the metric using the library's functional API style so it supports create, merge, compute.
2. Write unit tests that validate the implementation against reference values and include edge cases.
3. Document assumptions (tokenization, normalization, expected input shapes) in the metric’s docstring.
4. Submit a pull request and be prepared to iterate based on code reviews.

Community contributions help the project grow in directions I might not anticipate. Some current metrics were added by external contributors and they improved the library for everyone.

Documentation and examples 📚

I made sure there is thorough documentation and example notebooks so teams can get started quickly. The docs provide detailed explanations for each metric’s definition and expected inputs. Example notebooks demonstrate typical evaluation workflows and integration patterns.

Good documentation reduces onboarding time and helps teams avoid common pitfalls when comparing metrics across projects.

Comparisons to existing tooling 🔬

There are many metric libraries in the ecosystem. Two common alternatives are scikit-learn’s metrics and TensorFlow's metrics modules. Metrax differs in a few practical ways:

• JAX-native design: Metrax is written to operate in JAX’s functional, array-oriented world and integrates seamlessly with JAX compilation and vectorization.
• Performance focus: Many metrics are optimized for jittability and for batched evaluation across different values of k.
• Unified API: A consistent create/merge/compute pattern across metrics makes it easier to build generic evaluation drivers.

That said, I encourage users to cross-check results against trusted libraries when migrating existing pipelines to ensure consistent behavior during transition.

Practical tips for large-scale evaluation 🛡️

Large datasets and distributed evaluation introduce operational complexity. Here are practical tips that I use:

• Compute on-device when possible to avoid roundtrip costs between host and device. Jittable metrics help here.
• Use deterministic reduction when merging shard results to avoid nondeterminism across different machine counts.
• Keep metric state small per-device; if per-example information is needed, persist only what you must and summarize early.
• Profile evaluation passes periodically to ensure that metrics remain a small fraction of total evaluation time.

What I hope teams take away 🌟

Evaluating models is a core part of the ML lifecycle. Good evaluation practices accelerate iteration and improve trust in model behavior. With Metrax I wanted to remove the grunt work of implementing and verifying metrics and give teams a fast, consistent toolkit that integrates naturally with JAX.

If you adopt Metrax you get:

• Well-tested, domain-relevant metrics implemented in JAX.
• A small, consistent API surface that fits naturally into JAX training and evaluation loops.
• Optimizations for common evaluation scenarios, like computing multiple at-K metrics in one pass.
• Open source governance and a path to contribute additions and improvements.

Frequently asked questions ❓

What is Metrax and what problems does it solve?

Metrax is a JAX-native evaluation metrics library that provides ready-to-use, well-tested implementations for a wide range of metrics across classification, ranking, language, speech, and vision. It solves the problem of repeated reimplementation, inconsistent metric definitions, and inefficient evaluation pipelines by offering a consistent API, jittable implementations where possible, and optimized patterns such as computing multiple at-K metrics in a single pass.

Which frameworks and environments is Metrax designed to work with?

Metrax is designed for JAX-based workflows. It integrates naturally with JAX primitives like jax.jit and jax.vmap and is intended to be used in both research and production JAX environments, including multi-device and distributed evaluation setups.

Are Metrax metrics jittable and why does that matter?

Many Metrax metrics are implemented to be jittable. JIT compilation reduces per-call overhead, improves throughput, and enables on-device metric computation that keeps data movement minimal. Not all metrics can be jitted due to algorithmic constraints, but the library favors jittable designs where feasible while maintaining correctness for others.

How do I integrate Metrax into my training or evaluation loop?

Metrax follows a three-step functional API: create, merge, compute. Typically you create initial state for a metric based on model outputs, merge additional batches into this state during evaluation, and call compute to produce final results. This pattern is compatible with JAX’s compilation and distributed execution primitives.

Can I compute multiple values of K for ranking metrics efficiently?

Yes. Metrax can compute at-K metrics for multiple K values in parallel in a single forward pass. This is efficient and avoids separate computations for each K, reducing both compute time and possible inconsistencies across implementations.

Is Metrax production-ready and used in real applications?

Metrax is used within multiple Google teams including Search and YouTube, as well as Google’s post-training tooling. Those production uses shaped the library to be reliable and performant for real-world demands.

How can I contribute a new metric?

The project is hosted on GitHub under the Apache 2.0 license. Contributions are welcome. Implement metrics using the create/merge/compute pattern, include unit tests and documentation, and submit a pull request. Community contributions have already enriched the library.

Does Metrax handle tokenization differences for NLP metrics?

Metrax provides sensible defaults and guidance, but tokenization and normalization are important external preprocessing steps that can affect scores. The library documents metric definitions and expected inputs so you can apply consistent tokenization policies across runs.

How does Metrax compare to scikit-learn or TensorFlow metrics?

Metrax distinguishes itself by being JAX-native and optimized for JAX compilation primitives. It provides a consistent functional API across metrics and focuses on performance for batched, multi-K, and distributed evaluation patterns. It is complementary to other libraries; validating results against established implementations is a recommended migration step.

What license is Metrax distributed under?

Metrax is released under the Apache 2.0 license, making it suitable for both research and commercial use under typical open-source licensing terms.

Final notes and next steps ✨

I built Metrax to reduce friction and improve trust in model evaluation. If you are working with JAX and need a reliable, high-performance metric toolkit, it is worth trying Metrax in your evaluation pipeline. Start by reading the documentation, running example notebooks, and experimenting with the create/merge/compute pattern in a small evaluation job.

If you contribute a metric or find an edge case, the project’s GitHub-first approach makes it straightforward to collaborate and improve the library for everyone.

"Focus on the model evaluation results rather than expending effort on re-implementing and verifying various metric definitions yourself."

That guiding idea is why I built Metrax. Accurate metrics should inform decisions, not distract from them. I hope the library helps you move faster and with more confidence.