Experiment tracker and model registry

Log, organize, compare, register, and share
all your ML model metadata in a single place

  • Automate and standardize as your modeling team grows
  • Collaborate on models and results with your team and across the org
  • Use hosted, deploy on-premises or in a private cloud. Integrate with any MLOps stack
No credit card required
run["parameters"] = {
    "batch_size": 64,
    "optimizer": {
        "type": "SGD", "learning_rate": 0.001

def any_module_function_or_hook(run):

model_version = neptune.init_model_version()

# access model later
# model_version["model/binary"].download("models/")
ML engineers and data scientists
Hours logged
Commercial and research teams
icon Log ML metadata

Log model metadata from anywhere in your pipeline. See results in the web app. All in 5 minutes

Add a snippet to any step of your ML pipeline once. Decide what and how you want to log. Run a million times

  • Any framework
  • Any metadata type
  • From anywhere in your ML pipeline
Any framework
import neptune.new as neptune

# Connect to Neptune and create a run
run = neptune.init_run()

# Log hyperparameters
run["parameters"] = {
    "batch_size": 64,
    "optimizer": {"type":"SGD", "learning_rate": 0.001},
# Log dataset versions

# Log the training process
for iter in range(100):

# Log test metrics and charts
run["test/f1_score"] = test_score

# Log model weights and versions

# Stop logging to your run
from pytorch_lightning.loggers import NeptuneLogger

neptune_logger = NeptuneLogger()

trainer = Trainer(max_epochs=10, logger=neptune_logger)

trainer.fit(my_model, my_dataloader)
from neptune.new.integrations.tensorflow_keras import NeptuneCallback

run = neptune.init_run()
neptune_cbk = NeptuneCallback(run=run)

run = neptune.init_run()

data_dir = "data/CIFAR10"
params = {
    "lr": 1e-2,
    "bs": 128,
    "input_sz": 32 * 32 * 3,
    "n_classes": 10,
    "model_filename": "basemodel",
run["config/data_dir"] = data_dir
run["config/params"] = params

for i, (x, y) in enumerate(trainloader, 0):

    outputs = model.forward(x)
    _, preds = torch.max(outputs, 1)
    loss = criterion(outputs, y)
    acc = (torch.sum(preds == y.data)) / len(x)


import neptune.new.integrations.sklearn as npt_utils

run = neptune.init_run()

parameters = {
    "n_estimators": 120,
    "learning_rate": 0.12,
    "min_samples_split": 3,
    "min_samples_leaf": 2,

gbc = GradientBoostingClassifier(**parameters)
gbc.fit(X_train, y_train)

run["cls_summary"] = npt_utils.create_classifier_summary(
    gbc, X_train, X_test, y_train, y_test
from neptune.new.integrations.lightgbm import NeptuneCallback, create_booster_summary

run = neptune.init_run()
neptune_callback = NeptuneCallback(run=run)

params = {
    "boosting_type": "gbdt",
    "objective": "multiclass",
    "num_class": 10,
    "metric": ["multi_logloss", "multi_error"],
    "num_leaves": 21,
    "learning_rate": 0.05,
    "max_depth": 12,

# Train the model
gbm = lgb.train(
    valid_sets=[lgb_train, lgb_eval],
    valid_names=["training", "validation"],

run["lgbm_summary"] = create_booster_summary(
    list_trees=[0, 1, 2, 3, 4],
from neptune.new.integrations.xgboost import NeptuneCallback

run = neptune.init_run()
neptune_callback = NeptuneCallback(run=run, log_tree=[0, 1, 2, 3])

params = {
    "eta": 0.7,
    "gamma": 0.001,
    "max_depth": 9,
    "objective": "reg:squarederror",
    "eval_metric": ["mae", "rmse"],

import neptune.new.integrations.optuna as optuna_utils

run = neptune.init_run()
neptune_callback = optuna_utils.NeptuneCallback(run)

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=20, callbacks=[neptune_callback])
kedro neptune init
def report_accuracy(predictions: np.ndarray, test_y: pd.DataFrame,
                    neptune_run: neptune.run.Handler) -> None:
    # ...
    neptune_run["nodes/report/accuracy"] = accuracy

    fig, ax = plt.subplots()
    plot_confusion_matrix(target, predictions, ax=ax)
def create_pipeline(**kwargs):
    return Pipeline(
        [# ...
                ["example_predictions", "example_test_y","neptune_run"],
decor decor
Any metadata type
run["score"] = 0.97

for epoch in range(100):
run["model/parameters"] = {
    "optimizer": {"name": "Adam", "momentum": 0.9},

for name in misclassified_images_names:
run = neptune.init_run(capture_hardware_metrics=True)
run = neptune.init_run(source_files=["**/*.py", "config.yaml"])
decor decor
From anywhere in your ML pipeline
#Log from many pipeline nodes to the same run

export NEPTUNE_CUSTOM_RUN_ID=`date | md5`
# Log from multiple machines to the same run

# Open finished run "SUN-123"
run = neptune.init_run(with_id="SUN-123")

# Download model

# Continue logging
# Script
run = neptune.init_run(mode="offline")
# Console
neptune sync
decor decor
icon Organize experiments

Organize and display experiment and model metadata however you want

Organize logs in a fully customizable nested structure. Display model metadata in user-defined dashboard templates

  • Nested metadata structure
  • Custom dashboards
  • Table views
Nested metadata structure
run["accuracy"] = 0.62
run["model/parameters"] = {
    "lr": 0.2,
    "optimizer": {"name": "Adam", "momentum": 0.9}
decor decor
Custom dashboards
decor decor
Table views
icon Compare results

Search, debug, and compare experiments, datasets, and models

Visualize training live in the Neptune web app. See how different parameters and configs affect the results. Optimize models quicker

  • Compare
  • Search, sort, and filter
  • Visualize and display
  • Monitor live
  • Group by
decor decor
Search, sort, and filter
decor decor
Visualize and display
#Supports rendering Altair, Plotly, Bokeh, video, audio, or any fully contained HTML.
decor decor
Monitor live
decor decor
Group by
decor decor
icon Register models

Save your production-ready models to a centralized registry

Version, review and access production-ready models and metadata associated with them in a single place

  • Version models
  • Review and change stages
  • Access and share models
Version models
#Register a production-ready model. 
#You can attach any metadata or artifacts to it and organize them in any structure you want.
model = neptune.init_model(
    name="face_detection", key="DET",
#For any registered model, create as many model versions as you want. 
#Again, you can attach whatever metadata you want to it. 
model_version = neptune.init_model_version(
model_version["validation/acc"] = 0.97
#Save hash, location and other model artifact metadata. You don’t have to upload the model to Neptune. Just keep track of the model reference to local or S3-compatible storage.  
Review and change stages
decor decor
Access and share models
model_version = neptune.init_model_version(with_id="FACE-DET-42")
decor decor
icon Share results

Share and collaborate on experiment results and models across the org

Have a single place where your team can see the results and access all models and experiments

  • Send a link
  • Query API
  • Manage users and projects
  • Add your entire org
Send a link
decor decor
Query API
run = neptune.init_run(with_id="DET-135")
batch_size = run["parameters/batch_size"].fetch()
losses = run["train/loss"].fetch_values()
md5 = run["dataset/train"].fetch_hash()
decor decor
Manage users and projects
decor decor
Add your entire org
decor decor

Integrates with any MLOps tool stack

Integrations (7)
avatar lazyload
I’ve been mostly using Neptune just looking at the UI which I have, let’s say, kind of tailored to my needs. So I added some custom columns which will enable me to easily see the interesting parameters and based on this I’m just shifting over the runs and trying to capture what exactly interests me.
Wojciech Rosiński CTO at ReSpo.Vision
avatar lazyload
Gone are the days of writing stuff down on google docs and trying to remember which run was executed with which parameters and for what reasons. Having everything in Neptune allows us to focus on the results and better algorithms.
Andreas Malekos Chief Scientist at Continuum Industries

Code examples, videos, projects gallery, and other resources

Frequently asked questions

Yes, you can deploy neptune on-premises and other answers

  • You can deploy Neptune on your on-prem infrastructure or in your private cloud. 

    It is a set of microservices distributed as a Helm chart that you deploy on Kubernetes. 

    If you don’t have your own Kubernetes cluster deployed, our installer will set up a single-node cluster for you. 

    As per infrastructure requirements, you need a machine with at least 8 CPUs,  32GB RAM, and 1TB SSD storage.

    Read the full installation manual if interested, or talk to us (support@neptune.ai) if you have questions.

    If you have any trouble, our deployment engineers will help you all the way.

  • Yes, you can just reference datasets that sit on your infrastructure or in the cloud. 

    For example, you can have your datasets on S3 and just reference the bucket. 


    Neptune will save the following metadata about this dataset: 

    • version (hash), 
    • location (path), 
    • size, 
    • folder structure, and contents (files)

    Neptune never uploads the dataset, just logs the metadata about it. 

    You can later compare datasets or group experiments by dataset version in the UI.

  • Short version. People choose Neptune when:

    • They don’t want to maintain infrastructure (including autoscaling, backups etc)
    • They keep scaling their projects (and get into thousands of runs)
    • They collaborate with a team (and want user access, multi-tenant UI etc)


    For the long version, read this full feature-by-feature comparison.

  • Short version. People choose Neptune when:

    • They want to pay a reasonable price and the ability to invite unlimited users for free (at $150/month you get unlimited team members and 15000 logging hours added every month)
    • They want a super flexible tool (customizable logging structure, dashboards, works great with time series ML)
    • They want a component for experiment tracking and model registry NOT an end-to-end platform (wandb is adding HPO, orchestration, model deployment. We integrate with best-in-class tools in the space)

    For the long version, read this full feature-by-feature comparison.

  • It depends on what “model monitoring” you mean. 

    As we talk to teams, it seems that “model monitoring” means six different things to three different people: 

    • (1) Monitor model performance in production: See if the model performance decays over time, and you should re-train it
    • (2) Monitor model input/output distribution: See how the distribution of input data, features, and predictions distribution change over time?
    • (3) Monitor model training and re-training: See learning curves, trained model predictions distribution, or confusion matrix during training and re-training
    • (4) Monitor model evaluation and testing: log metrics, charts, prediction, and other metadata for your automated evaluation or testing pipelines
    • (5) Monitor hardware metrics: See how much CPU/GPU or Memory your models use during training and inference
    • (6) Monitor CI/CD pipelines for ML: See the evaluations from your CI/CD pipeline jobs and compare them visually

    So when looking at tooling landscape and Neptune:

    • Neptune does (3) and (4) really well, but we saw teams use it for (5) and (6)
    • Prometheus + Grafana is really good at (5), but people use it for (1) and (2)
    • Whylabs or Arize are really good at (1) and (2)

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