docarray
examples
docarray | examples | |
---|---|---|
32 | 143 | |
2,768 | 7,763 | |
2.2% | 0.8% | |
8.6 | 5.3 | |
7 days ago | 7 days ago | |
Python | Jupyter Notebook | |
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.
docarray
- DocArray – Represent, send, and store multimodal data for ML
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Some questions about multimodal data.
I’ve heard of DocArray, a library for multimodal data in transit and Pytorch Lightning which is also a tool for multimodal data. These two sound like a promising solution, but I’m not sure how to use it with databases or cloud storage. Do I need to install any additional packages or dependencies?
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Trying to create an AI recommender system that’s also ad-free video streaming.
I'm considering using these tools for a recommender system for analyzing text data like user reviews: DocArray and the EZ-MMLA Toolkit. Can anyone share their experience with the DocArray and EZ-MMLA Toolkit? I would love to hear about others' experiences before making a final decision.
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do you know any systems that can handle multimodal data fusion and representation learning?
I have been thinking about trying out DocArray and the EZ-MMLA Toolkit .. Has anyone had experience with these two projects?? Let me know what you think!
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I plan to build my own AI powered search engine for my portfolio. Do you know ones that are open-source?
For some alternatives, I know there’s DocArray where you can handle text, image and audio data. is basically a toolbox for multimodal data and then there should be Haystack which is also let you build search systems and also has to do something with Transformers and LLMs.
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A Guide to Using OpenTelemetry in Jina for Monitoring and Tracing Applications
DocArray to manipulate data and interact with the storage backend using document store.
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This week(s) in DocArray
It's already been two weeks since the last alpha release of DocArray v2. And since then a lot has happened — we've merged features we're really proud of, and we've cried tears of joy and misery trying to coerce Python into doing what we want. If you want to learn about interesting Python edge cases or follow the advancement of DocArray v2 development then you’ve come to the right place in this blog post!
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Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models
The German Fashion12k dataset is available for free use by the Jina AI community. After logging into Jina AI Cloud, you can download it directly in DocArray format:
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Want to Search Inside Videos Like a Pro? CLIP-as-service Can Help
Jina AI’s DocArray library
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Looking for open source projects in Machine Learning and Data Science
You could try spaCy. This is the brains of the operation - an open-source NLP library for advanced NLP in Python. Another is DocArray - It's built on top of NumPy and Dask, and good for preprocessing, modeling, and analysis of text data.
examples
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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🔥14 Excellent Open-source Projects for Developers😎
10. TensorFlow - Make Machine Learning Work for You 🤖
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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
cppflow - Run TensorFlow models in C++ without installation and without Bazel
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
mlpack - mlpack: a fast, header-only C++ machine learning library
bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
kaggle-environments
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Selenium WebDriver - A browser automation framework and ecosystem.
discoart - 🪩 Create Disco Diffusion artworks in one line
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing