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I wrote one of the fastest DataFrame libraries
Update 2021/03/14: benchmarks age poorly, Polars now has the fastest join algorithm in the benchmark! Know your hardware If you want to design for optimal performance you cannot ignore hardware. There are cases where algorithmic complexity doesn’t give you a good intuition of the real performance due to hardware-related issues like cache hierarchies and branch prediction. For instance, up to a cer
Optunaã«ã‚ˆã‚‹æœ€é©åŒ–çµæžœã‚’手軽ã«ç¢ºèªã§ãã‚‹Webダッシュボードã®é–‹ç™º | | AI tech studio
​​AI Labã®èŠç”°ã§ã™ (GitHub: @c-bata)。 以å‰ã€Optunaã«ã‚ˆã‚‹æœ€é©åŒ–çµæžœã‚’手軽ã«ç¢ºèªã§ãã‚‹Webダッシュボードを開発・公開ã—ã¾ã—ãŸã€‚公開ã‹ã‚‰ã™ã§ã«åŠå¹´ä»¥ä¸ŠãŒçµŒéŽã—ã€ç¾åœ¨ã¯å…¬å¼ã«åˆ©ç”¨ã‚’推奨ã•ã‚Œã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã—ãŸã€‚Google Summer of Codeãªã©ã‚’通ã—ã¦contributorã‚„committerも増ãˆã¤ã¤ã‚る一方ã§ã€è¨è¨ˆã‚„実装ã«é–¢ã—ã¦ã¯è³‡æ–™ã‚’残ã—ã¦ãã¾ã›ã‚“ã§ã—ãŸï¼ˆâ€»1）。本記事ã§ã¯ãƒ€ãƒƒã‚·ãƒ¥ãƒœãƒ¼ãƒ‰ã®ç´¹ä»‹ã‚’ã™ã‚‹ã¨ã¨ã‚‚ã«ã€é–‹ç™ºã«èˆˆå‘³ãŒã‚ã‚‹æ–¹å‘ã‘ã«é–‹ç™ºã«å½¹ç«‹ã¤æƒ…å ±ã‚’ã¾ã¨ã‚ã¦ãŠãã¾ã™ã€‚ GitHub: https://github.com/optuna/optuna-dashboard optuna-dashboardã¨ã¯ï¼Ÿ optuna-dashboardã¯Optunaã«ã‚ˆã‚‹ãƒã‚¤ãƒ‘ーパラメータã®æœ€é©åŒ–çµæžœã‚’Webブラウザ上ã§ç°¡å˜ã«ç¢ºèªã§ãるツールã§ã™ï¼ˆâ€»2）。
What is Delta Lake in Databricks? | Databricks on AWS
Delta Lake is the optimized storage layer that provides the foundation for tables in a lakehouse on Databricks. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing yo
Optimize query computation  | BigQuery  | Google Cloud Documentation
Send feedback Stay organized with collections Save and categorize content based on your preferences. Optimize query computation This document provides the best practices for optimizing your query performance. When you run a query, you can view the query plan in the Google Cloud console. You can also request execution details by using the INFORMATION_SCHEMA.JOBS* views or the jobs.get REST API meth
Dockerイメージ分æžãƒ„ール「diveã€ã‚’利用ã—ã¦Dockerイメージを軽é‡åŒ–ã™ã‚‹ - 🤖
ã¯ã˜ã‚ã« Docker イメージサイズã¯å°ã•ã‘ã‚Œã°å°ã•ã„ã»ã©ã€Push 㨠Pull ã®é«˜é€ŸåŒ–ã«ã¤ãªãŒã‚Šå¬‰ã—ã„ã§ã™ã€‚ docker historyã«ã‚ˆã£ã¦ã‚¤ãƒ¡ãƒ¼ã‚¸ãƒ¬ã‚¤ãƒ¤ãƒ¼ã”ã¨ã®ã‚µã‚¤ã‚ºã¯åˆ†ã‹ã‚Šã¾ã™ãŒã€ã©ã®ãƒ¬ã‚¤ãƒ¤ãƒ¼ã®ã©ã®ãƒ•ã‚¡ã‚¤ãƒ«ã®ã‚µã‚¤ã‚ºãŒå¤§ãã„ã‹ã¯åˆ†ã‹ã‚Šã¾ã›ã‚“。 $ docker history maven:3-amazoncorretto-11 IMAGE CREATED CREATED BY SIZE COMMENT eb8a5bbcd061 12 days ago /bin/sh -c #(nop) CMD ["mvn"] 0B <missing> 12 days ago /bin/sh -c #(nop) ENTRYPOINT ["/usr/local/b… 0B <missing> 12 days ago /bin/sh -c #(nop) COPY file:2bbb488dd73
Top 10 Performance Tuning Tips for Amazon Athena | Amazon Web Services
AWS Big Data Blog Top 10 Performance Tuning Tips for Amazon Athena February 2024: This post was reviewed and updated to reflect changes in Amazon Athena engine version 3, including cost-based optimization and query result reuse. Amazon Athena is an interactive analytics service built on open source frameworks that make it straightforward to analyze data stored using open table and file formats in
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GitHub - gepa-ai/gepa: Optimize prompts, code, and more with AI-powered Reflective Text Evolution
GitHub - meta-llama/prompt-ops: An open-source tool for LLM prompt optimization.
Optimizing Images for Web Performance with NGINX
pymoo: Multi-objective Optimization in Python
pymoode: Differential Evolution in Python | Towards Data Science
The modern portfolio theory: A multi-objective approach
View edition slot recommendations  | BigQuery  | Google Cloud
Google Cloud launches BigQuery capacity recommendations | Google Cloud Blog
GitHub - google/jaxopt: Hardware accelerated, batchable and differentiable optimizers in JAX.
Using SSDs to scale up Google Fusion Tables, a Database-in-the-Cloud
Google Brain Paper Demystifies Learned Optimizers | Synced
GitHub - wagoodman/dive: A tool for exploring each layer in a docker image
https://www.weeklydevtips.com/episodes/006
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