MLflow
clearml
Our great sponsors
| MLflow | clearml | |
|---|---|---|
| 27 | 16 | |
| 11,867 | 3,173 | |
| 2.9% | 2.6% | |
| 9.8 | 8.3 | |
| about 21 hours ago | 5 days ago | |
| Python | Python | |
| 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.
MLflow
- mlflow: Open source platform for the machine learning lifecycle
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MLflow VS VevestaX - a user suggested alternative
2 projects | 12 May 2022
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MLOps with MLflow on Kraken CI
Besides building, testing and deploying, Kraken CI is also a pretty nice tool to build an MLOps pipeline. In this article, it will be shown how to leverage Kraken CI to build a CI workflow for machine learning using MLflow.
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Serving Python Machine Learning Models With Ease
For MLFlow users you can now serve models directly in MLFlow using MLServer and if you're a Kubernetes user you should definitely check out Seldon Core - an open source tool that deploys models to Kubernetes (it uses MLServer under the covers).
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Data Science Workflows — Notebook to Production
But as you can imagine, tracking each experiment with Git can become a hassle. We’d like to automate the logging process of each run. The same as for large file versioning, many tools emerged in recent years for experiment logging, such as W&B, MLflow, TensorBoard, and the list goes on. In this case, I believe that it doesn’t matter with which hammer you choose to hit the nail, as long as you punch it through.
- [D] Tips for ML workflow on raw data
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Machine Learning adventures with MLFlow - Deploying models from local system to Production
Its a bug with mlflow -> https://github.com/mlflow/mlflow/issues/3755 Keep the server on, open another terminal export MLFLOW_TRACKING_URI env variable, if on windows set the env variable.....should work.
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Old guy programmer here, need to brush up on Python quickly!
mlflow for logging and visualizing ML model experiments
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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.
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[D] 5 considerations for Deploying Machine Learning Models in Production – what did I miss?
Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.
clearml
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Show HN: Open-source infra for data scientists
It looks like Magniv is targeting Python in general. This is similar to ClearML. What are the differentiating points to Magniv compared to similar products?
It seems like the product also integrates with SCM systems. Are you using gitea and then containers to push code and data to execution like CodeOcean?
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[D] Drop your best open source Deep learning related Project
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code.
- Everything you need to log, share, and version experiments, orchestrate pipelines, and scale within one open-source MLOps solution.
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code
- Your entire MLOps stack in one open-source tool
- Experiment, Orchestrate, and Automate with one MLOps tool.
What are some alternatives?
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
zenml - ZenML 🙏: MLOps framework to create reproducible pipelines. https://zenml.io.
dvc - 🦉Data Version Control | Git for Data & Models | ML Experiments Management
guildai - Experiment tracking, ML developer tools
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
neptune-client - :ledger: Experiment tracking tool and model registry
tensorflow - An Open Source Machine Learning Framework for Everyone
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
gensim - Topic Modelling for Humans
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
BentoML - The Unified Model Serving Framework 🍱