Python Archives - Page 3 of 14 - aster.cloud

5 min

Engineering

Follow The Pink Pony: A Story Of CSRF, Managed Services, And Unicorns

With the release of Django 4.0, there was a minor change to how Django handles CSRF protections: the Origin header is now checked, if present. Specifically, the URL scheme is now checked. Now, this seems like an innocuous change, something that shouldn’t affect many users. However, this change would break Django 4.0 deployments to Cloud Run using our tutorial. But not deployments to App Engine. What follows is one engineer’s story (hi!) into the depths of managed services, web server gateway interfaces, and magic strings. Managed hosting manages your hosting, turns out When you use managed hosting, you delegate control…

6 min

Announcing Serverless Spark Components For Vertex AI Pipelines

Developers and ML engineers face a variety of challenges when it comes to operationalizing Spark ML workloads. One set of challenges may come in the form of infrastructure concerns, for example, how to provision infrastructure clusters in advance, how to ensure that there are enough resources to run different kinds of tasks like data preparation, model training, and model evaluation in a reasonable time. Another set of challenges could come from task orchestration and data handling, for example, how to ensure that the most up-to-date features are available when a model training task is run. In order to solve those…

4 min

Calling Google APIs From Serverless (Part I: Cloud APIs)

Introduction As Google Cloud users, you may already be familiar with Google App Engine, Cloud Functions, or Cloud Run, our serverless platforms allowing technical practitioners to focus on building solutions, not what they run on. Google as a whole, has many divisions which provide developer tools beyond these platforms, so a common question which arises is: How do you call Google APIs from serverless? We’ll tackle this query in two phases, starting with how to access Cloud APIs from serverless. Most Cloud APIs have their own product client libraries you’ll use from serverless (and even non-serverless) platforms. Below, we’ll demonstrate…

4 min

Some Beans And Gems, Some Snakes And Elephants, With Java 17, Ruby 3, Python 3.10 And PHP 8.1 In App Engine And Cloud Functions

Time to spill the beans and show the gems, to our friendly snakes and elephants: we’ve got some great news for Java, Ruby, Python and PHP serverless developers today. Google App Engine and Cloud Functions are adding new modern runtimes, allowing you to update to the major version release trains of those programming languages. In short, here’s what’s new: Access to App Engine legacy bundled services for Java 11/17, Python 3 and Go 1.12+ runtimes, is Generally Available The Java 17, Ruby 3.0, Python 3.10, and PHP 8.1 runtimes come into preview in App Engine and Cloud Functions Let’s…

14 min

Orchestrating PyTorch ML Workflows On Vertex AI Pipelines

Previously in the PyTorch on Google Cloud series, we trained, tuned and deployed a PyTorch text classification model using Training and Prediction services on Vertex AI. In this post, we will show how to automate and monitor a PyTorch based ML workflow by orchestrating the pipeline in a serverless manner using Vertex AI Pipelines. Let’s get started! Why Pipelines? Before we dive in, first, let’s understand why pipelines are needed for ML workflows? As seen previously, training and deploying a PyTorch based model encapsulates a sequence of tasks such as processing data, training a model, hyperparameter tuning, evaluation, packaging the…

3 min

Software Engineering

Improving The Availability Of Your Cloud Storage: Automatic Retries In Client Libraries

You can use client libraries to interact with Cloud Storage. Using client libraries provides several benefits, including abstracting away low level details when making requests (such as authentication), and providing automatic retries of transient operations. Benefits of using client libraries for retries Most Cloud Storage client libraries perform retries automatically. Retries are helpful in case of temporary server errors or connectivity issues, and using retries can help you reduce the number of intermittent failures. This is especially important when running a large volume of operations. Retries in the library make the service more reliable and fit to handle production-quality applications…

4 min

Getting Started With Google Cloud Logging Python V3.0.0

We’re excited to announce the release of a major update to the Google Cloud Python logging library. v3.0.0 makes it even easier for Python developers to send and read logs from Google Cloud, providing real-time insights into what is happening in your application. If you’re a Python developer working with Google Cloud, now is a great time to try out Cloud Logging! If you’re unfamiliar with the `google-cloud-logging` library, getting started is simple. First, download the library using pip: $ pip install "google-cloud-logging>=3.0.0" Now, you can set up the client library to work with Python’s built-in `logging` library.…

5 min

15 Books for Computer Science Students

We are in the modern world where the digitization of much of education is already well underway. Nowadays, lots of blogs and articles are on the internet, for free, to learn from. But The Book has its own value: the author puts his all knowledge experience and time to write one whole book. The knowledge and details you get from a book are precious. In this article, I am going to discuss 15 Recommended Books For Computer Science Students 1. Clean Code: A Handbook of Agile Software Craftsmanship Because it’s more approachable, especially for inexperienced developers, Clean Code is one…

11 min

PyTorch/XLA: Performance Debugging On Cloud TPU VM: Part III

This article is the final in the three part series to explore the performance debugging ecosystem of PyTorch/XLA on Google Cloud TPU VM. In the first part, we introduced the key concept to reason about the training performance using PyTorch/XLA profiler and ended with an interesting performance bottleneck we encountered in the Multi-Head-Attention (MHA) implementation in PyTorch 1.8. In the second part, we began with another implementation of MHA introduced in PyTorch 1.9 which solved the performance bottleneck and identified an example of Dynamic Graph during evaluation using the profiler and studied a possible trade-off between CompileTime penalty with ‘device-to-host’…

Input your search keywords and press Enter.