towhee
fluent-bit
towhee | fluent-bit | |
---|---|---|
26 | 35 | |
3,001 | 5,344 | |
1.6% | 1.3% | |
8.6 | 9.8 | |
3 months ago | 6 days ago | |
Python | 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.
towhee
- FLaNK Stack Weekly for 14 Aug 2023
- Welcome to generate your embeddings with Towhee
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Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
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What Is DocArray?
The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).
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Deep Dive into Real-World Image Search Engine with Python
Benchmarking the models with towhee is as simple as:
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A quick tip on DataFrame.apply
The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.
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Build an Image Search Engine in Minutes
I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:
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Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
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A python framework for unstructured data processing
You can check the result from the tutorial.
fluent-bit
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Observability at KubeCon + CloudNativeCon Europe 2024 in Paris
Fluentbit
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Fluent Bit with ECS: Configuration Tips and Tricks
$ docker run --rm fluent-bit-dummy WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested Fluent Bit v1.9.10 * Copyright (C) 2015-2022 The Fluent Bit Authors * Fluent Bit is a CNCF sub-project under the umbrella of Fluentd * https://fluentbit.io [2023/12/24 16:06:59] [ info] [fluent bit] version=1.9.10, commit=557c8336e7, pid=1 [2023/12/24 16:06:59] [ info] [storage] version=1.4.0, type=memory-only, sync=normal, checksum=disabled, max_chunks_up=128 [2023/12/24 16:06:59] [ info] [cmetrics] version=0.3.7 [2023/12/24 16:06:59] [ info] [output:stdout:stdout.0] worker #0 started [2023/12/24 16:06:59] [ info] [sp] stream processor started [0] dummy.0: [1703434019.553880465, {"message"=>"custom dummy"}] [0] dummy.0: [1703434020.555768799, {"message"=>"custom dummy"}] [0] dummy.0: [1703434021.550525174, {"message"=>"custom dummy"}] [0] dummy.0: [1703434022.551563050, {"message"=>"custom dummy"}] [0] dummy.0: [1703434023.551944509, {"message"=>"custom dummy"}] [0] dummy.0: [1703434024.550027843, {"message"=>"custom dummy"}] [0] dummy.0: [1703434025.550901801, {"message"=>"custom dummy"}] [0] dummy.0: [1703434026.549279385, {"message"=>"custom dummy"}] ^C[2023/12/24 16:07:08] [engine] caught signal (SIGINT) [0] dummy.0: [1703434027.549678344, {"message"=>"custom dummy"}] [2023/12/24 16:07:08] [ warn] [engine] service will shutdown in max 5 seconds [2023/12/24 16:07:08] [ info] [engine] service has stopped (0 pending tasks) [2023/12/24 16:07:08] [ info] [output:stdout:stdout.0] thread worker #0 stopping... [2023/12/24 16:07:08] [ info] [output:stdout:stdout.0] thread worker #0 stopped
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Should You Be Scared of Unix Signals?
> Libc is a lot more tricky about signals, since not all libc functions can be safely called from handlers.
And this is a huge thing. People do all kinds of operations in signal handlers completely oblivious to the pitfalls. Pitfalls which often do not manifest, making it a great "it works for me" territory.
I once raised a ticket on fluentbit[1] about it but they have abused signal handlers so thoroughly that I do not think they can mitigate the issue without a major rewriting of the signal and crash handling.
[1] https://github.com/fluent/fluent-bit/issues/4836
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Vector: a Rust-based lightweight alternative to Fluentd/Logstash
Fluentbit is Fluentd's lightweight alternative to itself.
https://fluentbit.io
- FLaNK Stack Weekly for 14 Aug 2023
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Ultimate EKS Baseline Cluster: Part 1 - Provision EKS
From here, we can explore other developments and tutorials on Kubernetes, such as o11y or observability (PLG, ELK, ELF, TICK, Jaeger, Pyroscope), service mesh (Linkerd, Istio, NSM, Consul Connect, Cillium), and progressive delivery (ArgoCD, FluxCD, Spinnaker).
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Fluentbit Kubernetes - How to extract fields from existing logs
From this (https://github.com/fluent/fluent-bit/issues/723), I can see there is no grok support for fluent-bit.
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Parsing multiline logs using a custom Fluent Bit configuration
apiVersion: v1 kind: ConfigMap metadata: name: fluent-bit-config namespace: newrelic labels: k8s-app: newrelic-logging data: # Configuration files: server, input, filters and output # ====================================================== fluent-bit.conf: | [SERVICE] Flush 1 Log_Level ${LOG_LEVEL} Daemon off Parsers_File parsers.conf HTTP_Server On HTTP_Listen 0.0.0.0 HTTP_Port 2020 @INCLUDE input-kubernetes.conf @INCLUDE output-newrelic.conf @INCLUDE filter-kubernetes.conf input-kubernetes.conf: | [INPUT] Name tail Tag kube.* Path ${PATH} Parser ${LOG_PARSER} DB /var/log/flb_kube.db Mem_Buf_Limit 7MB Skip_Long_Lines On Refresh_Interval 10 filter-kubernetes.conf: | [FILTER] Name multiline Match * multiline.parser multiline-regex [FILTER] Name record_modifier Match * Record cluster_name ${CLUSTER_NAME} [FILTER] Name kubernetes Match kube.* Kube_URL https://kubernetes.default.svc.cluster.local:443 Merge_Log Off output-newrelic.conf: | [OUTPUT] Name newrelic Match * licenseKey ${LICENSE_KEY} endpoint ${ENDPOINT} parsers.conf: | # Relevant parsers retrieved from: https://github.com/fluent/fluent-bit/blob/master/conf/parsers.conf [PARSER] Name docker Format json Time_Key time Time_Format %Y-%m-%dT%H:%M:%S.%L Time_Keep On [PARSER] Name cri Format regex Regex ^(?[^ ]+) (?stdout|stderr) (?[^ ]*) (?.*)$ Time_Key time Time_Format %Y-%m-%dT%H:%M:%S.%L%z [MULTILINE_PARSER] name multiline-regex key_content message type regex flush_timeout 1000 # # Regex rules for multiline parsing # --------------------------------- # # configuration hints: # # - first state always has the name: start_state # - every field in the rule must be inside double quotes # # rules | state name | regex pattern | next state # ------|---------------|--------------------------------|----------- rule "start_state" "/(Dec \d+ \d+\:\d+\:\d+)(.*)/" "cont" rule "cont" "/^\s+at.*/" "cont"
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Tool to scrape (semi)-structured log files (e.g. log4j)
There are also log forwarding tools like promtail and fluentbit that can be used to both ship logs to something like Loki and produce metrics.
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How to Deploy and Scale Strapi on a Kubernetes Cluster 2/2
FluentBit, is a logging processor that can help you to push all of your application logs to a central location like an ElasticSearch or OpenSearch cluster.
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
loki - Like Prometheus, but for logs.
Milvus - A cloud-native vector database, storage for next generation AI applications
rsyslog - a Rocket-fast SYStem for LOG processing
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
syslog-ng - syslog-ng is an enhanced log daemon, supporting a wide range of input and output methods: syslog, unstructured text, queueing, SQL & NoSQL.
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
jaeger - CNCF Jaeger, a Distributed Tracing Platform
AI - Artificial Intelligence Projects
winston - A logger for just about everything.
pgvector - Open-source vector similarity search for Postgres
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.