{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Collects metrics, configs, and performance profiles from ML workloads running on Google Cloud TPUs and GPUs, integrating with Google Cloud Logging and XProf for centralized diagnostics.","skillfed_tags":["google-cloud","ml-profiling","performance-monitoring"],"use_cases":["Track metrics and profiles for JAX training runs on Google Cloud TPUs to identify performance bottlenecks.","Collect system metrics (CPU, memory, GPU utilization) alongside model performance during inference workloads.","Manage and version workload configs (software, system, user-defined) for reproducible ML experiments.","Trigger XProf profiling sessions programmatically from within your ML code to capture performance data at key training steps.","Collaborate on ML run diagnostics using shareable links to profiles and run metadata in the Google Cloud console."],"what_it_does":"Google Cloud ML Diagnostics is a Python SDK for integrating diagnostics into ML workloads running on Google Cloud. It lets you track machine learning runs, collect workload metrics (model quality, performance, system metrics), manage configs, and capture performance profiles using XProf\u2014all visualized in the Google Cloud console. The package depends on google-api-core, google-auth, google-cloud-logging, psutil, packaging, and pynvml to communicate with Google Cloud services and gather system/GPU telemetry.\n\nThe SDK is designed for ML engineers to embed into their training or inference code on Google Cloud TPUs and GPUs. It works with orchestrators like Google Kubernetes Engine and custom setups. Metrics and profiles attach to a Machine Learning Run object, which can have multiple profiling sessions triggered either programmatically or on-demand from the UI. The package requires Python 3.8 or later and specific Google Cloud IAM permissions to function.","worth_installing":"Yes, if you run ML workloads on Google Cloud TPUs or GPUs and need integrated diagnostics. The low install friction, active maintenance, and permissive license make it a straightforward add to JAX-based projects. Requires upfront Google Cloud setup (API enablement, IAM roles, Log Analytics) and is tightly coupled to Google Cloud infrastructure\u2014not suitable for on-premises or non-Google-Cloud deployments."},"id":"google-cloud-mldiagnostics","links":{"html":"https://skillfed.io/packages/google-cloud-mldiagnostics","md":"https://skillfed.io/packages/google-cloud-mldiagnostics.md","pypi":"https://pypi.org/project/google-cloud-mldiagnostics/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":null,"license_treatment":"permissive","name":"google-cloud-mldiagnostics","python_support":"supports_current","summary":"diagnostic packages for profiling and ML experiment management"},"popularity":{"monthly_downloads":268054,"position":8284,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.7"}