d2l-en
polyaxon
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| d2l-en | polyaxon | |
|---|---|---|
| 6 | 9 | |
| 21,564 | 3,476 | |
| 2.8% | 0.7% | |
| 8.7 | 8.8 | |
| about 1 month ago | 4 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | 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.
d2l-en
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
polyaxon
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Any MLOps platform you use?
If you're not concerned about self-hosting, WandB is one of the more fully featured training monitoring tools (I've used it in the past without any issues but the lack of data and training privacy and lack of self-hosting possibilities makes it a hard no for anything that isn't scholastic). Polyaxon is an alternative but rewriting all your variable logging to conform to their requirements makes it very difficult to switch to it in the middle of a project so you have to commit to it from the get-go.
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[D] Kubernetes for ML - how are y'all doing it?
[4]: https://github.com/polyaxon/polyaxon
We use Polyaxon and it’s pretty good
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Polyaxon.
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[D] Productionalizing machine learning pipelines for small teams
For running experiments, http://polyaxon.com/ is a really good free open-source package that has lots of nice integrations so you can quickly run experiments in k8s but it might be overkill in some cases.
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Top 5 tools to get started with MLOps !
Polyaxon : https://polyaxon.com
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
kubeflow - Machine Learning Toolkit for Kubernetes
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
ssd_keras - A Keras port of Single Shot MultiBox Detector
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
dvc - 🦉 ML Experiments and Data Management with Git