neptune-client
📘 The MLOps stack component for experiment tracking (by neptune-ai)
Caffe
Caffe: a fast open framework for deep learning. (by BVLC)
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| neptune-client | Caffe | |
|---|---|---|
| 24 | 6 | |
| 526 | 33,832 | |
| 6.1% | 0.1% | |
| 9.6 | 0.0 | |
| 3 days ago | about 2 months ago | |
| Python | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
neptune-client
Posts with mentions or reviews of neptune-client.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-05.
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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.
Caffe
Posts with mentions or reviews of Caffe.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-16.
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List of AI-Models
Click to Learn more...
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Una corta intro a las Redes Neuronales Artificiales
Caffe de BAIR
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Can someone please guide me regarding these different face detection models?
Caffe is a DL framework just like TensorFlow, PyTorch etc. OpenPose is a real-time person detection library, implemented in Caffe and c++. You can find the original paper here and the implementation here.
What are some alternatives?
When comparing neptune-client and Caffe you can also consider the following projects:
Caffe2
MLflow - Open source platform for the machine learning lifecycle
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
mlpack - mlpack: a fast, header-only C++ machine learning library
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Porcupine - On-device wake word detection powered by deep learning
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
Keras - Deep Learning for humans
SHOGUN - Shōgun
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