7.09.2025 состоялся релизPithon 3.14!
На фоне хайпа про NoGIL всё позабыли про другие фичи. Особенно про Multiple Interpreters, который обещает изоляцию процессов но с эффективностью потоков! На сколько действительно это будет эффективно мы узнаем позже, потому что сейчас это лишь первый релиз с ограничениями и недоработками.
Но что там про NoGIL? Теперь этот режим не экспериментальный, а официально поддерживаемый, но опциональный.
Чтобы запустить без GIL нужна специальная сборка. И перед стартом нужно объявить переменную PYTHON_GIL=0
Для вас я собрал готовый репозиторий где достаточно запустить скрпит, который всё сделает:
▫️ соберет релизный Python 3.14 в новый Docker-образ
▫️ запустит тесты в контейнере (GIL, NoGIL, MultiInterpreter)
▫️ распечатает результаты
Тест очень простой, усложняйте сами)
Вот какие результаты у меня:
=== Running ThreadPoolExecutor GIL ON
TOTAL TIME: 45.48 seconds
=== Running ThreadPoolExecutor GIL OFF
TOTAL TIME: 6.14 seconds
=== Running basic Thread GIL ON
TOTAL TIME: 45.54 seconds
=== Running basic Thread GIL OFF
TOTAL TIME: 4.74 seconds
=== Running with Multi Interpreter
TOTAL TIME: 18.30 seconds
Если сравнивать GIL и NoGIL, то на мои 32 ядра прирост х7-x10 (почему не х32? 🤷). При этом нам обещают что скорости будут расти с новыми релизами.
Режим без GIL похож (визуально) на async, тоже параллельно, тоже не по порядку. Но это не IO! и от того некоторый диссонанс в голове 😵💫, нас учили не так!
Интересно, что чистый Thread работает быстрей чем ThreadPoolExecutor без GIL.
Ну и где-то плачет один адепт мульти-интерпретаторов😭 Теперь нужно искать где они могут пригодиться с такой-то скоростью. Скорее всего своя область применения найдется.
Отдельно я затестил память и вот что вышло на 32 потока:
ThreadPoolExecutor GIL ON
305.228 MB
ThreadPoolExecutor GIL OFF
500.176 MB
basic Thread GIL ON
90.668 MB
basic Thread GIL OFF
472.444 MB
with Multi Interpreter
1267.788 MB
Пока не знаю как к этому относиться)
В целом - радует направление развития!
#release
Google 为 AI Pro 及 Ultra 订阅者，提供Google Cloud 抵扣金额
Google 宣布将 Google Developer Program (GDP) 高级权益整合至 Google AI Pro 与 Google AI Ultra 订阅方案中，且无需额外费用。
其中，Pro 订阅者每月可获 10 美元 Google Cloud 抵扣金额，Ultra 订阅者为 100 美元。此举旨在打通从原型开发到生产部署的路径，支持开发者在 Vertex AI 或 Cloud Run 等平台直接调用资源。
目前，活跃订阅者已可前往官网激活相关权益。
🗒 标签: #Google#AI#GCP#GDP
📢 频道: @GodlyNews1
🤖 投稿: @GodlyNewsBot
#Biopharma Institute offers effective and engaging #RemoteLearning solutions in Good Manufacturing Practice and Good Clinical Practice.
Earn your #GMP & #GCP professional certification.
http://biopharmainstitute.com
👌 Join @OnlineEducation10
#python#agents#gcp#gemini#genai_agents#generative_ai#llmops#mlops#observability
You can quickly create and deploy AI agents using the Agent Starter Pack, a Python package with ready-made templates and full infrastructure on Google Cloud. It handles everything except your agent’s logic, including deployment, monitoring, security, and CI/CD pipelines. You can start a project in just one minute, customize agents for tasks like document search or real-time chat, and extend them as needed. This saves you time and effort by providing production-ready tools and integration with Google Cloud services, letting you focus on building smart AI agents without worrying about backend setup or deployment details.
https://github.com/GoogleCloudPlatform/agent-starter-pack
#DataEngineer#ContractPosition#Remote#GCP#Snowflake#dbt#Fintech#API#Airflow#GitHub
Разыскивается Data Engineer на работу по контракту с крупной американской венчурной компанией.
Контракт на 6 месяцев с возможностью перезаключения договора.
Предпочтительна возможность работать в их часовых поясах, минимальное время пересечения – 4 часа.
Стек технологий: GCP, Snowflake, dbt, Airflow, GitHub, API/SFTP, Python, SQL.
Английский B2 и выше – условие обязательное.
Опыт работы в финтех/банковском секторе - условие обязательное.
Работать за пределами России и Беларуси - условие обязательное.
Зарплата: $5000 – 7000 NET.
Для самых внимательных, кто действительно читает описание вакансии:
• Пожалуйста, откликайтесь только в том случае, если у вас есть необходимый опыт по всему стеку (GCP, Snowflake, dbt, Airflow, GitHub, Python and SQL, API/SFTP), а также опыт работы в финтех/банковском секторе.
• Присылайте резюме в формате Word.
Спасибо!
Для связи: https://t.me/Tary_bird
____________________________________
Description of the Data Engineer contract position:
Location: Preferably Pacific Time Zone, with at least 4 hours overlap with working hours.
Company:
A large venture company with assets of over $11 billion and employees in Austin, London, Menlo Park, and San Francisco.
What to expect:
Your role as a data engineer involves reporting to the head of the data and analytics department and participating in the creation of the entire structure and infrastructure necessary to support operations in the fintech/banking sector.
Responsibilities:
• Developing, creating, and maintaining data infrastructure for optimal extraction, transformation, and loading of data from various sources using SQL, and big data technologies.
• Creating and implementing data collection systems that integrate various sources, including company proprietary data and external sources.
• Automating the process of collecting and visualizing user engagement data.
• Developing and supporting data processes on the Google Cloud platform and in the Snowflake system for efficient data processing.
• Extracting data via API/SFTP and ensuring its correctness and relevance.
What we are looking for:
Qualifications:
• Fintech/Bank working experience (must have).
• Minimum 6 years of professional experience as a data engineer/data analyst in the fintech/banking sector.
• Deep knowledge of GCP, Snowflake, dbt, Airflow, and GitHub.
• Strong proficiency in Python and SQL.
• Experience in data intake via API/SFTP.
• Attention to detail and strong communication skills, both orally and in writing.
Nice to have:
• Bachelor's or master's degree in computer science, database management, etc.
Please send the completed application form together with your CV.
• How many years of experience do you have with Google Cloud Platform (GCP)?
• How many years of experience do you have with Snowflake?
• How many years of experience do you have with dbt?
• How many years of experience do you have with Airflow?
• How many years of experience do you have with GitHub?
• Do you have experience working with data intake through API/SFTP? If yes, please describe.
• How many years of experience do you have with Python?
• How many years of experience do you have with SQL?
• What salary USD is expected?
#DataEngineer#ContractPosition#Remote#GCP#ThoughtSpot#BigData#Affinity#Slack#Looker#Snowflake
Разыскивается DataEngineer на работу по контракту с крупной американской венчурной компанией.
Контракт на 6 месяцев с возможностью перезаключения договора.
Предпочтительна возможность работать в их часовых поясах, но возможны варианты.
Стек технологий: GCP, ETL, Snowflake, CRM Affinity, SQL, Airflow, ThoughtSpot (preferred) or Looker , Python, SQL (нужен full stack!!!)
Английский B2 и выше – условие обязательное.
Работать за пределами России и Беларуси - условие обязательное.
Зарплата $5000 – 6500 NET
Для самых внимательных, кто действительно читает описание вакансии: просим - откликаться только в том случае, если у вас есть полный стек, - присылать резюме в формате Word.
Для связи: https://t.me/Tary_bird
Description of the Data Engineer contract position:
Location: Preferably San Francisco Bay Area, or remotely in the Pacific or Central Time zone.
Company:
A large venture company with assets of over $11 billion and employees in Austin, London, Menlo Park, and San Francisco.
What to expect:
Your role as a data engineer involves reporting to the head of the data and analytics department and participating in the creation of the entire structure and infrastructure necessary to support operations.
Responsibilities:
Developing, creating, and maintaining data infrastructure for optimal extraction, transformation, and loading of data from various sources using SQL, NoSQL, and big data technologies.
Creating and implementing data collection systems that integrate various sources, including company proprietary data and external sources.
Automating the process of collecting and visualizing user engagement data from CRM/UI.
Developing and supporting ETL (Extract, Transform, Load) processes on the Google Cloud platform and in the Snowflake system for efficient data processing.
Extracting data from the Affinity CRM system, ensuring its correctness and relevance.
Integrating notifications into Slack to improve communication within the team.
If necessary, developing and supporting analytical reports and dashboards in BI tools such as ThoughtSpot (preferred) or Looker to make data-driven decisions.
What we are looking for:
Qualifications:
• Experience of at least 3 years as a data engineer or full stack in the field of data warehousing, data monitoring, and building and maintaining ETL pipelines, including experience with Google Cloud and Snowflake.
• Deep experience with data pipeline and workflow management tools (Airflow).
• Strong proficiency in SQL and Python
• Experience with BigQuery.
• experience extracting data out of Affinity CRM and integrate notifications back to Slack
• Solid knowledge and experience with database design, setup, and maintenance.
• Proven ability to work in highly dynamic environments with high product velocity
• Strong communication skills, both orally and in writing.• BI tool experience on ThoughtSpot (preferred) or Looker
Nice to have:
• Bachelor's or master's degree in computer science, database management, etc.
#typescript#actions#authentication#gcp#github_actions#google_cloud#google_cloud_platform#iam#identity#security
You can securely connect GitHub Actions to Google Cloud using the Google GitHub Action called `auth`. It supports two main ways: the recommended Workload Identity Federation (WIF), which uses short-lived tokens and avoids long-lived service account keys, and the older Service Account Key JSON method. WIF improves security by creating a trust link between your GitHub workflow and Google Cloud without exposing permanent credentials. To use it, you set up a Workload Identity Pool and Provider in Google Cloud, then configure your GitHub workflow to authenticate with these. This lets your workflows access Google Cloud resources safely and easily, reducing risks and simplifying credential management.
https://github.com/google-github-actions/auth
#go#aws#azure#cncf#cost#cost_optimization#finops#gcp#k8s#kubernetes#monitoring#opencost#prometheus
OpenCost is a free, open-source tool that helps you see and understand the costs of running Kubernetes clusters and cloud services in real time. It breaks down costs by cluster, node, namespace, pod, and more, across multiple cloud providers like AWS, Azure, and GCP, and even supports on-premises setups. This lets you track where your money is going, spot expensive resources, and manage your cloud spending better. It integrates with Prometheus for metrics and offers a user-friendly web interface and APIs for easy cost monitoring and exporting. Using OpenCost helps you control and optimize your cloud and Kubernetes expenses efficiently[1][2][3][4].
https://github.com/opencost/opencost
#java#ai#apache_kafka#aws#azure#cloud#cloud_first#cloud_native#ebs#gcp#kafka#llm#messaging#minio#s3#serverless#spot#streaming
AutoMQ provides a cloud-native alternative to Apache Kafka that runs on S3 storage, cutting costs by up to 90% while enabling instant scaling and eliminating cross-zone traffic fees. It offers high reliability, serverless operation, and full Kafka compatibility, making it easier and cheaper to manage large-scale data streaming without sacrificing performance or features.
https://github.com/AutoMQ/automq
#python#alibabacloud#android#android_emulator#aws#azure#cloud#docker#docker_android#emulator#gcp#genymotion#jenkins#kubernetes#mobile_app#mobile_web#novnc#saltstack#selenium#selenium_grid#terraform
You can use Docker-Android to run Android emulators inside Docker containers, which helps you develop and test Android apps easily without needing physical devices. It offers many device profiles like Samsung Galaxy and Nexus models, supports viewing the emulator via VNC, sharing logs through a web interface, and controlling the emulator remotely with adb. It works on Ubuntu and can integrate with cloud services like Genymotion. This setup speeds up development, testing, and automation, making your workflow more consistent and efficient while saving resources. You can also persist data and run unit or UI tests with popular frameworks like Appium and Espresso. This helps you build and test Android apps faster and more reliably.
https://github.com/budtmo/docker-android