ML Experiment tracking tool for R People
Track and organize your entire experimentation process from exploratory analysis, to model training runs and hyperparameter sweeps, and everything in between.
Log metrics, hyperparameters, data versions, hardware usage and more. Work on any infra, any language, scripts or notebooks.
Record data exploration
Experiments don’t have to stop with running training scripts. Version your exploratory data analysis and share with your team.
Manage your team with organizations, projects, and user roles. Organize experiments with tags and custom views.
Quick and simple setup
Start tracking experiments in minutes, work like you used to… just log it
Insert a few lines of code into your standard training and validation scripts and start logging your experiment data.
Run on your laptop, in the cloud, on Google Colab or wherever you want.
Use in the scripts or in Jupyter notebooks. Run experiments your way just let us track them.
library(neptune) init_neptune(project_name = "YOUR/PROJECT", api_token = "YOURKEY" ) create_experiment(name = "training on Sonar", tags = c("lgbm", "no-preprocesisng"), params = list(tuneLength = 100, model = "rf") ) set_property(property = "data-version", value = digest(dataset) ) log_metric("Train Accuracy", scores$TrainAccuracy) log_artifact("model.Rdata") log_image("parameter_search", "param_plot.jpeg")
UI that scales, super customizable and designed for teams
Log and organize millions of experiment runs.
Create custom views for data scientists or managers, and save them for later.
Search through experiments quickly with a powerful language.
Inteligent table that shows you diffs and more
When you compare multiple experiment runs sometimes it is difficult to figure out what is different and what you should look for.
We’ve created a table that automatically finds the columns and values that are different and displays them for you!
Track versions of your datasets, group results by datasets
Datasets change during the project lifetime. You can log datasets signatures as you run experiemnts and group the results by dataset in the UI.
set_property(property = "feature-version", value = digest(dataset) )
Organize your projects, give different roles to different people
You can assign people to different organizations and projects.
You can choose whether they should be able to edit experiment data or simply view what is happening and comment on it.
Try it out on Google Colab. No registration needed.
Lets me see the progress anytime
“Neptune allow us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination.”
VP, Machine Learning @Zesty.ai
Gives us flexibility we need
Senior NLP/ML Engineer @reply.ai
Hooks to multiple frameworks
Head of Data Science @New Yorker
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