neptune-client
docker-data-science
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neptune-client | docker-data-science | |
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24 | 1 | |
504 | 0 | |
8.9% | - | |
9.6 | 0.0 | |
4 days ago | over 1 year ago | |
Python | Dockerfile | |
Apache License 2.0 | Do What The F*ck You Want To Public License |
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neptune-client
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[D] The hype around Mojo lang
Other companies followed the same route to promote their paid product, e.g. plotly -> dash, Pytorch Lightning -> Lightning AI, run.ai, neptune.ai . It's actually a fair strategy, but some people may fear the conflict of interest. Especially, when the tools require some time investment, and it seems like a serious vendor lock-in. Investing some time to learn a tool is not such a big deal, but once you adapt a workflow of an entire team it can be tough to go back.
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[P] New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio
track and compare your model performance visually. In addition, Neptune integration can be used.
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Any MLOps platform you use?
Neptune.ai, which promises to streamline your workflows and make collaboration a breeze.
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A huge list of AI/ML news sources
Blog – neptune.ai - Metadata store for MLOps, built for teams that run a lot of experiments. (RSS feed: https://neptune.ai/blog/feed)
- Who needs MLflow when you have SQLite?
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Machine Learning experiment tracking library for Rust
Therefore I am looking for frameworks which can help me with tracking all the ML experiments. There are an endless plethora of such libraries for Python, most notably perhaps [wandb](wandb.ai), but others include Neptune, Comet ML and TensorBoard.
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[D] Maintaining documentation with live results from experiments
In the case of neptune.ai we don't have this feature but you can query and retrieve the metadata you logged programmatically using the Python Client and use it to create a custom report/dashboard using tools like notion, streamlit, gradio, dash and etc. You also can have a cron-job that updates the report periodically or when there is a new experiment logged to Neptune.
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What are the differences between MLflow and neptune?
Hello u/MLBoi_TM! I was wondering: The pros/cons you've listed, is this comparing Managed MLflow <> neptune.ai or the OSS MLflow compenent <> neptune.ai?
The key difference between MLflow and neptune.ai on a shallow level is really that neptune.ai does not offer a standalone OSS solution. Apart from that, its offering overlaps with MLflow's in the sense that it focuses on experiment tracking (incl. metadata store) as well as model artifact management ("model registry"). Of course, there' lots of differences in the detail then. However, since I've never used neptune.ai, I cannot really comment on that.
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Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production
So, if you even want to use MLFlow to track your experiments, run the pipeline on Airflow, and then deploy a model to a Neptune Model Registry, ZenML will facilitate this MLOps Stack for you. This decision can be made jointly by the data scientists and engineers. As ZenML is a framework, custom pieces of the puzzle can also be added here to accommodate legacy infrastructure.
docker-data-science
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Jupyter Notebooks
Exactly. You can pre-configure yours for data science projects and publish it to Docker Hub for others to leverage. There’s one here: https://github.com/0xnu/docker-data-science
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
Caffe - Caffe: a fast open framework for deep learning.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
jupyterlab-kite - Kite Autocomplete Extension for JupyterLab
Porcupine - On-device wake word detection powered by deep learning
Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
Caffe2
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
spokestack-python - Spokestack is a library that allows a user to easily incorporate a voice interface into any Python application with a focus on embedded systems.