cloud-accelerator-diagnostics
Monitor, debug and profile the jobs running on Cloud accelerators like TPUs and GPUs.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Vertex AI API enabled in Google Cloud console and Vertex AI User IAM role assigned to the service account.
- Low install friction with a pure-Python wheel.
- Actively maintained with last commit on 2026-08-14.
License · maintenance · safety
(unclear) — 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.
last release 2024-10-15 (668 days) · last repo commit 2026-08-14 · 32 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 274,717 downloads/mo, #8,186 on PyPI
Alternatives
Verify before relying
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"
)- 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.
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 on this page. Verify before relying on it.
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
cloud-accelerator-diagnostics on PyPI
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.
Requires Vertex AI API enabled in Google Cloud console and Vertex AI User IAM role assigned to the service account.
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"
)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 the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagegoogle-cloud-aiplatform |
| Maintenance | Actively maintained 668 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 274,717 / month, #8,186 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 :: 3.8Programming Language :: Python :: 3.9 |
Evidence: cloud_accelerator_diagnostics-0.1.1-py3-none-any.whl
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See also accelforge · google-cloud-mldiagnostics · ml-goodput-measurement · torch-tb-profiler · cloud-tpu-diagnostics · google-cloud-pipeline-components · tensorboard-data-server · tensorboard · tb-nightly · tf-nightly