ToolBench
flink-kubernetes-operator
ToolBench | flink-kubernetes-operator | |
---|---|---|
3 | 8 | |
4,455 | 725 | |
4.2% | 4.3% | |
8.3 | 9.2 | |
5 days ago | 5 days ago | |
Python | Java | |
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.
ToolBench
- FLaNK Stack Weekly for 07August2023
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[R] ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs - WeChat AI, Tencent Inc. 2023 - Open-source! Comparble performance to ChatGPT while using tools!
Github: https://github.com/OpenBMB/ToolBench
- [N] ToolBench is a set of data and tools that you can use to further customize and improve your language model (LLM).
flink-kubernetes-operator
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Top 10 Common Data Engineers and Scientists Pain Points in 2024
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example, implementing a real-time anomaly detection model in Kafka Streams would require translating Python code into Java, slowing down pipeline performance, and requiring a complex initial setup.
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling.
- FLaNK Stack Weekly 22 January 2024
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg.
- FLaNK Stack Weekly for 07August2023
What are some alternatives?
gorilla-cli - LLMs for your CLI
hugging-chat-api - HuggingChat Python API🤗
NiFi-Man - Like Travel Man, But With Data. The Data is Here, But Should We Have Ingested it?
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
EverythingApacheNiFi - EverythingApacheNiFi
CallCMLModel - An example on calling models deployed in CML
harlequin - The SQL IDE for Your Terminal.
Qwen-7B - The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud. [Moved to: https://github.com/QwenLM/Qwen]
cdf-workshop
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more