---
id: cloud-accelerator-diagnostics
version: "0.1.1"
license: unclear
license_treatment: unclear
maintenance: active
---

# cloud-accelerator-diagnostics — Monitor, debug and profile the jobs running on Cloud accelerators like TPUs and GPUs.

License: unclear · Maintenance: active · Downloads: 274.7K/mo

## What it is and what it does

Cloud Accelerator Diagnostics is a library for monitoring and profiling machine learning workloads on cloud TPUs and GPUs. It wraps Vertex AI Tensorboard integration, allowing you to automatically capture and upload diagnostic logs from accelerator runs without manual instrumentation of your training code.

The package provides three main entry points: creating Vertex AI Tensorboard instances, creating Experiments within those instances, and starting a background thread that continuously monitors a log directory and uploads new data to Tensorboard. It is designed to work alongside profiling frameworks, and handles the Google Cloud authentication and API calls on your behalf. The main runtime dependency is google-cloud-aiplatform.

Use it for:

- Profile workloads running on TPUs and automatically upload traces to Vertex AI Tensorboard for analysis.
- Monitor GPU training jobs and stream diagnostic logs to a centralized Tensorboard instance for debugging.
- Set up a Vertex AI Experiment with automatic Tensorboard attachment to compare multiple accelerator runs.
- Capture and persist accelerator performance metrics without writing custom Google Cloud API boilerplate.
- Debug training failures by reviewing uploaded logs in Vertex AI Tensorboard after a job completes.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Monitors, debugs, and profiles workloads running on cloud accelerators (TPUs and GPUs), with built-in support for uploading diagnostic data to Vertex AI Tensorboard.

Yes, if you are running workloads on Google Cloud TPUs or GPUs and want streamlined Tensorboard integration. The package is actively maintained, has low install friction, and eliminates boilerplate for Vertex AI setup. However, verify the license treatment before use in commercial contexts, and confirm that google-cloud-aiplatform's dependencies fit your environment. Not relevant for non-Google-Cloud accelerator setups.

## Install

pip install cloud-accelerator-diagnostics
uv add cloud-accelerator-diagnostics
poetry add cloud-accelerator-diagnostics

## Installing cloud-accelerator-diagnostics

Before you install: Low install friction with a pure-Python wheel. Actively maintained with last commit on 2026-08-14. Depends on google-cloud-aiplatform, which brings substantial Google Cloud infrastructure as a transitive dependency.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata. The description excerpt shows Apache 2.0 headers, but verify the actual license before adopting in a commercial or restricted-license context.

Quickstart:

pip install cloud-accelerator-diagnostics

from cloud_accelerator_diagnostics import tensorboard
instance_id, tensorboard_url = tensorboard.create_experiment(
    project="test-project",
    location="us-central1",
    experiment_name="test-experiment",
    tensorboard_name="test-instance"
)

Requires Vertex AI API enabled in Google Cloud console and Vertex AI User IAM role assigned to the service account.

Verify before relying:

- Actual license classification—description shows Apache 2.0 headers but metadata does not declare it formally.
- Whether google-cloud-aiplatform's full dependency tree is acceptable for your environment.
- Real-world performance and stability on production TPU/GPU workloads at scale.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 274.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

TPU GPU profiling monitoring, cloud accelerator diagnostics, Vertex AI Tensorboard upload, workload profiling debugging, accelerator performance monitoring, cloud training diagnostics, accelerator workload debugging, accelerator-profiling, vertex-ai, google-cloud

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