dvc
MLflow
Our great sponsors
dvc | MLflow | |
---|---|---|
108 | 54 | |
13,032 | 17,021 | |
1.6% | 3.9% | |
9.7 | 9.9 | |
about 20 hours ago | 6 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.
dvc
-
Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
See pachyderm too.
-
First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
-
Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
- Ask HN: How do your ML teams version datasets and models?
-
Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
-
[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
-
Ask HN: Data Management for AI Training
* User interface for less tech savy people ( e.g just a git like command line is fine for engineers but not for field personell who are not in IT )
I know of tools like https://dvc.org/ but a) they are just layers on top of git b) break appart on huge datasets without a folder hierarchy ( git tree objects just don't work for linear lists of items ) are only useable by IT personell, and require checking out at least a part of the dataset.
Our datasets would be 100.000.000 x 100 MB = 10 PB of raw data. Training data should be delivered to training nodes via network etc.. we just can't have a full checkout of that data...
-
Do you wonder why MLOps is not at the same level as DevOps?
Hey, great find! However, it only explains concepts but not how to actually use any tool. I personally use DVC, but it's more focused on the model development/engineering phase. The different phases of ML are also done independently, which makes it even more difficult for an individual to have exposure to all the different areas. Moreover, the lack of standard tools and best practices makes it difficult, and the fact that every ML problem is different.
MLflow
-
Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
-
cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
-
EL5: Difference between OpenLLM, LangChain, MLFlow
MLFlow - http://mlflow.org
-
Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
MLflow:
-
Options for configuration of python libraries - Stack Overflow
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
-
[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
-
Looking for recommendations to monitor / detect data drifts over time
Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?
-
Integrating Hugging Face Transformers & DagsHub
While Transformers already includes integration with MLflow, users still have to provide their own MLflow server, either locally or on a Cloud provider. And that can be a bit of a pain.
-
Any MLOps platform you use?
I have an old labmate who uses a similar setup with MLFlow and can endorse it.
MLflow - an open-source platform for managing your ML lifecycle. What’s great is that they also support popular Python libraries like TensorFlow, PyTorch, scikit-learn, and R.
What are some alternatives?
clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
guildai - Experiment tracking, ML developer tools
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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.
neptune-client - :ledger: The MLOps stack component for experiment tracking
dagster - An orchestration platform for the development, production, and observation of data assets.
lakeFS - lakeFS - Data version control for your data lake | Git for data
gensim - Topic Modelling for Humans
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator