{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/3"}],"enrichment":{"capability":"Measures and monitors the productive utilization (Goodput) and idle time (Badput) of ML training jobs running on cloud accelerators, logging metrics to Google Cloud and exporting analysis to TensorBoard.","skillfed_tags":["ml-training-profiling","gcp-cloud-logging","performance-analysis"],"use_cases":["Identify bottlenecks in ML training pipelines by measuring productive computation versus data loading and overhead.","Monitor training job efficiency in real-time via TensorBoard exports to catch performance regressions early.","Analyze step-time deviation across distributed training to detect stragglers or synchronization issues.","Quantify the impact of system changes on actual training productivity rather than just wall-clock time.","Debug why a training job is slower than expected by breaking down time spent in initialization and preparation."],"what_it_does":"ML Goodput Measurement is a library for quantifying the productive time (Goodput) and idle/overhead time (Badput) of machine learning training jobs running on cloud accelerators. It provides a GoodputRecorder to instrument your training code with timestamps for job start/end, individual training steps, device initialization, data loading, and training preparation. The recorded data is sent to Google Cloud Logging, where a separate GoodputCalculator can analyze it to compute overall productivity metrics and breakdowns of where time is lost. A GoodputMonitor component can asynchronously query and export these metrics to TensorBoard for real-time visibility.\n\nThe package is designed to work with Google Cloud accelerators and requires a GCP project with Cloud Logging enabled and appropriate access scopes. It depends on google-cloud-logging, google-cloud-monitoring, numpy, scipy, requests, and related libraries. The typical workflow is to instrument your training application with recorder calls, let it run, then run a separate analysis program to compute Goodput and understand where compute resources are underutilized.","worth_installing":"Yes, if you are running ML training on Google Cloud accelerators and need visibility into actual compute utilization. The low install friction, active maintenance, and lack of known vulnerabilities make it safe to adopt. However, verify the license status before use in proprietary contexts, and confirm that your GCP project and cluster access scopes are properly configured\u2014setup is non-trivial."},"id":"ml-goodput-measurement","links":{"html":"https://skillfed.io/packages/ml-goodput-measurement","md":"https://skillfed.io/packages/ml-goodput-measurement.md","pypi":"https://pypi.org/project/ml-goodput-measurement/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"unclear","name":"ml-goodput-measurement","python_support":"supports_current","summary":"Package to monitor Goodput, Badput and other metrics of ML workloads."},"popularity":{"monthly_downloads":299293,"position":7861,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.2"}