Deeplearning4j
Greenplum
Deeplearning4j | Greenplum | |
---|---|---|
13 | 9 | |
13,427 | 6,204 | |
0.3% | 0.2% | |
5.8 | 9.9 | |
6 days ago | about 11 hours ago | |
Java | C | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Deeplearning4j
- Deeplearning4j Suite Overview
- Java for ML?
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Best way to combine Python and Java?
Have you considered migrating off of Python to just using JVM ML libraries then? I hear good things about Deeplearning4j, but there's quite a few.
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Anybody here using Java for machine learning?
I've gone to the linux workflow as directed in the docs and reconstructed the maven command line:
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Data Science Competition
DL4J
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Java Matrix Benchmark is Updated! See how linear algebra libraries compare for speed
Hey folks, just letting you know we see this thread and I appreciate you guys running these benchmarks. I'm not seeing any of your posts on our forums. I think I saw a notification from our examples but we do not actually monitor that. Please use: https://community.konduit.ai/ or at least the main repo dl4j issues: https://github.com/eclipse/deeplearning4j/issues and you'll get a lot more visibility. Thanks!
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Does Java has similar project like this one in C#? (ml, data)
Also, the website is now redirected to: https://deeplearning4j.konduit.ai/
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If it gets better w age, will java become compatible for machine learning and data science?
On top of this several popular projects have been built. This includes tensorflow-java and our project eclipse deeplearning4j: https://github.com/eclipse/deeplearning4j
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Matrices multiplication benchmark: Apache math vs colt vs ejml vs la4j vs nd4j
Nd4j is actively developed. The latest commit was 6 hours ago. Nd4j is part of deeplearning4j which is now owned by eclipse (but the main contributors are from a company) https://github.com/eclipse/deeplearning4j/tree/master/nd4j
Greenplum
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Ask HN: It's 2023, how do you choose between MySQL and Postgres?
Friends don't let their friends choose Mysql :)
A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data (MyIsam), bad transactions (MyIsam lacks transaction integrity), and the number of Mysql gotchas being a really long list influenced me.
In time I actually found out that I had underestimated one of the most important attributes of Postgres that was a huge strength over Mysql: the power of community. Because Postgres has a really superb community that can be found on Libera Chat and elsewhere, and they are very willing to help out, I think Postgres has a huge advantage over Mysql. RhodiumToad [Andrew Gierth] https://github.com/RhodiumToad & davidfetter [David Fetter] https://www.linkedin.com/in/davidfetter are incredibly helpful folks.
I don't know that Postgres' licensing made a huge difference or not but my perception is that there are a ton of 3rd party products based on Postgres but customized to specific DB needs because of the more liberalness of the PG license which is MIT/BSD derived https://www.postgresql.org/about/licence/
Some of the PG based 3rd party DBs:
Enterprise DB https://www.enterprisedb.com/ - general purpose PG with some variants
Greenplum https://greenplum.org/ - Data warehousing
Crunchydata https://www.crunchydata.com/products/hardened-postgres - high security Postgres for regulated environments
Citus https://www.citusdata.com - Distributed DB & Columnar
Timescale https://www.timescale.com/
Why Choose PG today?
If you want better ACID: Postgres
If you want more compliant SQL: Postgres
If you want more customizability to a variety of use-cases: Postgres using a variant
If you want the flexibility of using NOSQL at times: Postgres
If you want more product knowledge reusability for other backend products: Postgres
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Show HN: Postgres WASM
I was wondering if anyone had thought about using this to experiment with the planner.
The engineering and support teams at Greenplum, a fork of Postgres, have a tool (minirepro[0]) which, given a sql query, can grab a minimal set of DDLs and the associated statistics for the tables involved in the query that can then be loaded into a "local" GPDB instance. Having the DDL and the statistics meant the team was able to debug issues in the optimizer (example [1]), without having access to a full set of data. This approach, if my understanding is correct, could be enabled in the browser with this Postgres WASM capability.
[0] https://github.com/greenplum-db/gpdb/blob/6X_STABLE/gpMgmt/b...
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Amazon Aurora's Read/Write Capability Enhancement with Apache ShardingSphere-Proxy
A database solution architect at AWS, with over 10 years of experience in the database industry. Lili has been involved in the R&D of the Hadoop/Hive NoSQL database, enterprise-level database DB2, distributed data warehouse Greenplum/Apache HAWQ and Amazon’s cloud native database.
- Greenplum Database – Massively Parallel PostgreSQL for Analytics
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What’s the Database Plus concept and what challenges can it solve?
Today, it is normal for enterprises to leverage diversified databases. In my market of expertise, China, in the Internet industry, MySQL together with data sharding middleware is the go to architecture, with GreenPlum, HBase, Elasticsearch, Clickhouse and other big data ecosystems being auxiliary computing engine for analytical data. At the same time, some legacy systems (such as SQLServer legacy from .NET transformation, or Oracle legacy from outsourcing) can still be found in use. In the financial industry, Oracle or DB2 is still heavily used as the core transaction system. New business is migrating to MySQL or PostgreSQL. In addition to transactional databases, analytical databases are increasingly diversified as well.
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Data Science Competition
Green Plum
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Inspecting joins in PostgreSQL
PostgreSQL is a free and advanced database system with the capacity to handle a lot of data. It’s available for very large data in several forms like Greenplum and Redshift on Amazon. It is open source and is managed by an organized and very principled community.
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What’s so special about distributed SQL? Ask us anything!
2003 - https://greenplum.org/
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Using Postgres as a Data Warehouse
There's Greenplum!
What are some alternatives?
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
citus - Distributed PostgreSQL as an extension
Weka
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
tensorflow - An Open Source Machine Learning Framework for Everyone
ClickHouse - ClickHouse® is a free analytics DBMS for big data
Smile - Statistical Machine Intelligence & Learning Engine
cockroach - CockroachDB - the open source, cloud-native distributed SQL database.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Apache Mahout - Mirror of Apache Mahout
dremio-oss - Dremio - the missing link in modern data