Google Cloud TPU Metrics via Telegraf

Google Cloud TPU is a specialized hardware accelerator provided by Google Cloud that is designed to accelerate machine learning workloads. TPUs are specifically optimized for deep learning tasks and offer high performance and energy efficiency, enabling organizations to train and run machine learning models at scale.

• Begin by heading over to the 'Project Selector' and select the specific project from which you wish to send metrics.
• Progress to the 'Service Account Details' screen. Here, assign a distinct name to your service account and opt for 'Create and Continue'.
• In the 'Grant This Service Account Access to Project' screen, ensure the following roles: 'Compute Viewer', 'Monitoring Viewer', and 'Cloud Asset Viewer'.
• Upon completion of the above, click 'Done'.
• Now find and select your project in the 'Service Accounts for Project' list.
• Move to the 'KEYS' section.
• Navigate through Keys > Add Key > Create New Key, and specify 'JSON' as the key type.
• Lastly, click on 'Create', and make sure to save your new key.

Now add the environment variable for the key

On the machine run:

export GOOGLE_APPLICATION_CREDENTIALS=<your-gcp-key>

This integration allows you to configure a Telegraf agent to send your metrics, in multiple formats, to Logit.io.

Telegraf is a flexible server agent equipped with plug-in support, useful for sending metrics and events from data sources like web servers, APIs, application logs, and cloud services.

To ship your metrics to Logit.io, we will integrate the relevant input and outputs.http plug-in into your Telegraf configuration file.

Choose the install for your operating system below to get started:

Windows

wget https://dl.influxdata.com/telegraf/releases/telegraf-1.19.2_windows_amd64.zip

Download and extract to: C:\Program Files\Logitio\telegraf\

Configuration file: C:\Program Files\Logitio\telegraf\

MacOS

brew install telegraf

Configuration file x86_64 Intel: /usr/local/etc/telegraf.conf Configuration file ARM (Apple Silicon): /opt/homebrew/etc/telegraf.conf

Ubuntu/Debian

wget -q https://repos.influxdata.com/influxdata-archive_compat.key
echo '393e8779c89ac8d958f81f942f9ad7fb82a25e133faddaf92e15b16e6ac9ce4c influxdata-archive_compat.key' | sha256sum -c && cat influxdata-archive_compat.key | gpg --dearmor | sudo tee /etc/apt/trusted.gpg.d/influxdata-archive_compat.gpg > /dev/null
echo 'deb [signed-by=/etc/apt/trusted.gpg.d/influxdata-archive_compat.gpg] https://repos.influxdata.com/debian stable main' | sudo tee /etc/apt/sources.list.d/influxdata.list

sudo apt-get update
sudo apt-get install telegraf

Configuration file: /etc/telegraf/telegraf.conf

RedHat and CentOS

cat <<EOF | sudo tee /etc/yum.repos.d/influxdata.repo
[influxdata]
name = InfluxData Repository - Stable
baseurl = https://repos.influxdata.com/stable/\$basearch/main
enabled = 1
gpgcheck = 1
gpgkey = https://repos.influxdata.com/influxdata-archive_compat.key
EOF

sudo yum install telegraf

Configuration file: /etc/telegraf/telegraf.conf

SLES & openSUSE

zypper ar -f obs://devel:languages:go/ go
zypper in telegraf

Configuration file: /etc/telegraf/telegraf.conf

FreeBSD/PC-BSD

sudo pkg install telegraf

Configuration file: /etc/telegraf/telegraf.conf

Read more about how to configure data scraping and configuration options for Telegraf

First you need to set up the input plug-in to enable Telegraf to scrape the GCP data from your hosts. This can be accomplished by incorporating the following code into your configuration file:

# Gather timeseries from Google Cloud Platform v3 monitoring API
[[inputs.stackdriver]]
  ## GCP Project
  project = "<your-project-name>"

  ## Include timeseries that start with the given metric type.
  metric_type_prefix_include = [
    "tpu.googleapis.com",
  ]

  ## Most metrics are updated no more than once per minute; it is recommended
  ## to override the agent level interval with a value of 1m or greater.
  interval = "1m"

Read more about how to configure data scraping and configuration options for Stackdriver

Once you have generated the configuration file, you need to set up the output plug-in to allow Telegraf to transmit your data to Logit.io in Prometheus format. This can be accomplished by incorporating the following code into your configuration file:

[[outputs.http]]
  
  url = "https://<your-metrics-username>:<your-metrics-password>@<your-metrics-stack-id>-vm.logit.io:0/api/v1/write"
  data_format = "prometheusremotewrite"

  [outputs.http.headers]
    Content-Type = "application/x-protobuf"
    Content-Encoding = "snappy"

Windows

telegraf.exe --service start

MacOS

telegraf --config telegraf.conf

Linux

sudo service telegraf start

for systemd installations

systemctl start telegraf

Data should now have been sent to your Stack.

View my data

If you don't see metrics take a look at How to diagnose no data in Stack below for how to diagnose common issues.

If you don't see data appearing in your Stack after following the steps, visit the Help Centre guide for steps to diagnose no data appearing in your Stack or Chat to support now.

Telegraf, the comprehensive open-source server agent created by InfluxData, is adept at collecting metrics and data from a vast array of sources, including specialized computing resources. Google Cloud TPUs are custom-developed hardware accelerators designed to significantly speed up and scale up specific machine learning (ML) workloads programmed with TensorFlow. These processors are optimized to deliver high performance for both training and inference across a range of ML models, enabling researchers, developers, and businesses to accelerate their machine learning applications more efficiently than with general-purpose GPU and CPU computing.

Integrating Telegraf with Google Cloud TPUs allows organizations to monitor the performance and utilization of these powerful resources in real-time. This is crucial for optimizing machine learning workflows, ensuring efficient use of TPU resources, and reducing computational costs. By collecting metrics such as operation per second, processing time, and resource utilization, teams can gain insights into their TPU performance, identify bottlenecks in their ML pipelines, and make informed decisions to enhance model training and inference processes.

However, the complexity and volume of data generated by TPUs, combined with the critical nature of ML workloads, necessitate a robust platform for data analysis and visualization. Logit.io offers a comprehensive solution, providing an advanced analytics platform that simplifies the processing, visualization, and analysis of metrics from Telegraf and Google Cloud TPUs.

With Logit.io, organizations can enhance their monitoring and analytics capabilities for machine learning projects utilizing Google Cloud TPUs, enabling them to maximize resource utilization, improve model accuracy, and accelerate time to insights. The platform's advanced features support proactive management of ML workflows, helping to ensure the successful deployment of machine learning applications at scale.

For those leveraging Telegraf in conjunction with Google Cloud TPUs and seeking to improve their ML infrastructure monitoring and analytics, Logit.io provides the essential tools and expertise. Our platform facilitates effective management of complex ML metrics, allowing organizations to derive actionable insights and achieve their machine learning objectives. With Google Cloud TPUs serving as powerful accelerators for AI and ML tasks, it's crucial to have a robust log management and analysis solution in place. Google Storage Transfer Metrics can be a pivotal component in this process. By utilizing the integration between Logit.io and Google Cloud integrations, you can keep track of the data transfer metrics with precision. In addition to TPUs, Logit.io's integration also extends to Google App Engine, offering a holistic solution for monitoring and managing your applications running on this serverless platform. By connecting your App Engine instances to Logit.io, you can obtain real-time insights into application performance, error tracking, and resource utilization.