google-cloud-mldiagnostics
diagnostic packages for profiling and ML experiment management
Decision gist · record as of 2026-08-14
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—not suitable for on-premises or non-Google-Cloud deployments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Google Cloud project with Cluster Director API enabled, appropriate IAM roles (clusterdirector.editor, logging.logWriter, storage.objectUser), and Log Analytics enabled on Cloud Logging _Default bucket.
- Low friction: pure Python wheel with 6 runtime dependencies (google-api-core, google-auth, google-cloud-logging, psutil, packaging, pynvml).
- Active maintenance as of 9 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production environments.
last release 2026-08-05 (9 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 268,054 downloads/mo, #8,284 on PyPI
Alternatives
Verify before relying
pip install google-cloud-mldiagnostics
from google.cloud import mldiagnostics
# Integrate with your ML workload to track metrics and profiles- Whether the package works with frameworks beyond JAX (description says 'JAX on Google Cloud TPUs and GPUs today').
- Whether multi-host profiling requires additional setup beyond the SDK itself.
- Performance overhead of metrics collection on large-scale workloads.
What it is and 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—all 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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—not suitable for on-premises or non-Google-Cloud deployments.
Install
google-cloud-mldiagnostics on PyPI
Before you install
Low friction: pure Python wheel with 6 runtime dependencies (google-api-core, google-auth, google-cloud-logging, psutil, packaging, pynvml). Active maintenance as of 9 days ago.
Requires Google Cloud project with Cluster Director API enabled, appropriate IAM roles (clusterdirector.editor, logging.logWriter, storage.objectUser), and Log Analytics enabled on Cloud Logging _Default bucket.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production environments.
Quickstart
pip install google-cloud-mldiagnostics
from google.cloud import mldiagnostics
# Integrate with your ML workload to track metrics and profilesVerify before relying
- Whether the package works with frameworks beyond JAX (description says 'JAX on Google Cloud TPUs and GPUs today').
- Whether multi-host profiling requires additional setup beyond the SDK itself.
- Performance overhead of metrics collection on large-scale workloads.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesgoogle-api-coregoogle-authgoogle-cloud-loggingpsutilpackagingpynvml |
| Maintenance | Actively maintained 9 days since the last release |
| First released | |
| Downloads | 268,054 / month, #8,284 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: google_cloud_mldiagnostics-1.0.7-py3-none-any.whl
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