Metadata-Version: 2.1
Name: neptune-client
Version: 0.16.10
Summary: Neptune Client
Home-page: https://neptune.ai/
Author: neptune.ai
Author-email: contact@neptune.ai
License: Apache License 2.0
Project-URL: Tracker, https://github.com/neptune-ai/neptune-client/issues
Project-URL: Source, https://github.com/neptune-ai/neptune-client
Project-URL: Documentation, https://docs.neptune.ai/
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            <a href="https://badge.fury.io/py/neptune-client"><img src="https://badge.fury.io/py/neptune-client.svg" alt="PyPI version"></a>
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        </div>
        
        ## Flexible metadata store for MLOps, built for research and production teams that run a lot of experiments.
        
        Neptune is a lightweight solution designed for:
        
        * **Experiment tracking:** log, display, organize, and compare ML experiments in a single place.
        * **Model registry:** version, store, manage, and query trained models and model-building metadata.
        * **Monitoring ML runs live:** record and monitor model training, evaluation, or production runs live.
        &nbsp;
        
        ### Getting started
        
        **Step 1:** Sign up for a **[free account](https://neptune.ai/register)**
        
        **Step 2:** Install the Neptune client library
        
        ```bash
        pip install neptune-client
        ```
        
        **Step 3:** Connect Neptune to your code
        
        ```python
        import neptune.new as neptune
        
        run = neptune.init_run(
            project="common/quickstarts",
            api_token=neptune.ANONYMOUS_API_TOKEN,
        )
        
        run["parameters"] = {
            "batch_size": 64,
            "dropout": 0.2,
            "optim": {"learning_rate": 0.001, "optimizer": "Adam"},
        }
        
        for epoch in range(100):
            run["train/accuracy"].log(epoch * 0.6)
            run["train/loss"].log(epoch * 0.4)
        
        run["f1_score"] = 0.66
        ```
        
        [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neptune-ai/examples/blob/master/how-to-guides/hello-world/notebooks/Neptune_hello_world.ipynb)
        
        <div align="center">
            <a href="https://youtu.be/15p-mAuIMlA" target="_blank">
              <img border="0" alt="W3Schools" src="https://neptune.ai/wp-content/uploads/manage-all-your-model-building-metadat-in-a-single-place.jpg" width="600">
            </a>
        </div>
        &nbsp;
        
        Learn more in the [documentation](https://docs.neptune.ai/) or check our [video tutorials](https://www.youtube.com/c/NeptuneAI/) to find your specific use case.
        
        ### Features
        
        ***Log and display***
        
        Neptune [supports log and display](https://docs.neptune.ai/logging/what_you_can_log) for many different types of metadata generated during the ML model lifecycle:
        * metrics and learning curves
        * parameters, tags, and properties
        * code, Git info, files, and Jupyter notebooks
        * hardware consumption (CPU, GPU, memory)
        * images, interactive charts, and HTML objects
        * audio and video files
        * tables and CSV files
        * and [more](https://docs.neptune.ai/logging/what_you_can_log)
        
        <div align="center">
             <img border="0" alt="W3Schools" src="https://neptune.ai/wp-content/uploads/Log-and-display.gif" width="600">
        </div>
        &nbsp;
        
        ***Compare***
        
        You can [compare model-building runs](https://docs.neptune.ai/app/comparison) you log to Neptune using various comparison views:
        * **Charts:** where you can compare learning curves for metrics or losses
        * **Images:** where you can compare images across runs
        * **Parallel coordinates:** where you can see parameters and metrics displayed on a parallel coordinates plot
        * **Side-by-side:** which shows you the difference between runs in a table format
        * **Artifacts:** where you can compare datasets, models, and other artifacts that you version in Neptune
        * **Notebooks:** which shows you the difference between notebooks (or checkpoints of the same notebook) logged to the project
        
        <div align="center">
             <img border="0" alt="W3Schools" src="https://neptune.ai/wp-content/uploads/Compare.gif" width="600">
        </div>
        &nbsp;
        
        ***Filter and organize***
        
        Filter, sort, and group model training runs using [highly configurable dashboards](https://docs.neptune.ai/app/custom_dashboard/).
        
        <div align="center">
             <img border="0" alt="W3Schools" src="https://neptune.ai/wp-content/uploads/Filter-and-organize-3688604602-1644927778194.png" width="600">
        </div>
        &nbsp;
        
        ***Collaborate***
        
        Improve [team management and collaboration](https://docs.neptune.ai/about/collaboration/) by grouping all experiments into projects and workspaces and quickly sharing any result or visualization within the team.
        
        <div align="center">
             <img border="0" alt="W3Schools" src="https://neptune.ai/wp-content/uploads/Collaboration.gif" width="600">
        </div>
        &nbsp;
        
        ### Integrate with your favourite ML libraries
        Neptune comes with **25+ integrations with Python libraries** popular in machine learning, deep learning and reinforcement learning.
        Available integrations:
        * PyTorch and PyTorch Lightning
        * TensorFlow / Keras and TensorBoard
        * Scikit-learn, LightGBM, and XGBoost
        * Optuna, Scikit-Optimize, and Keras Tuner
        * Bokeh, Altair, Plotly, and Matplotlib
        * and [more](https://docs.neptune.ai/integrations)
        
        #### PyTorch Lightning
        
        <img src="https://neptune.ai/wp-content/uploads/PyTorch-Lightning.png" width="350" /><br><br>
        
        Example:
        
        ```python
        from pytorch_lightning import Trainer
        from pytorch_lightning.loggers import NeptuneLogger
        
        # Create NeptuneLogger
        neptune_logger = NeptuneLogger(
            api_key="ANONYMOUS",  # replace with your own
            project="common/pytorch-lightning-integration",  # "WORKSPACE_NAME/PROJECT_NAME"
            tags=["training", "resnet"],  # optional
        )
        
        # Pass it to the Trainer
        trainer = Trainer(max_epochs=10, logger=neptune_logger)
        
        # Run training
        trainer.fit(my_model, my_dataloader)
        ```
        
        [![neptune-pl](https://img.shields.io/badge/PytorchLightning-experiment-success?logo=data:image/png;base64,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)](https://app.neptune.ai/common/pytorch-lightning-integration/experiments?split=tbl&dash=charts&viewId=faa75e77-5bd6-42b9-9379-863fe7a33227)
        &nbsp;
        
        [![github-code](https://img.shields.io/badge/GitHub-code-informational?logo=github)](https://github.com/neptune-ai/examples/tree/main/integrations-and-supported-tools/pytorch-lightning/scripts)
        [![jupyter-code](https://img.shields.io/badge/Jupyter-code-informational?logo=jupyter)](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/pytorch-lightning/notebooks/Neptune_PyTorch_Lightning.ipynb)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/pytorch-lightning/notebooks/Neptune_PyTorch_Lightning.ipynb)
        [<img src="https://img.shields.io/badge/docs-PyTorch%20Lightning-yellow">](https://docs.neptune.ai/integrations/lightning/)
        
        #### TensorFow/Keras
        
        <img src="https://neptune.ai/wp-content/uploads/TensorFowKeras.png" width="400" /><br><br>
        
        Example:
        
        ```python
        import tensorflow as tf
        import neptune.new as neptune
        from neptune.new.integrations.tensorflow_keras import NeptuneCallback
        
        run = neptune.init_run(
            project="common/tf-keras-integration",
            api_token=neptune.ANONYMOUS_API_TOKEN,
        )
        
        mnist = tf.keras.datasets.mnist
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        x_train, x_test = x_train / 255.0, x_test / 255.0
        
        model = tf.keras.models.Sequential(
            [
                tf.keras.layers.Flatten(),
                tf.keras.layers.Dense(256, activation=tf.keras.activations.relu),
                tf.keras.layers.Dropout(0.5),
                tf.keras.layers.Dense(10, activation=tf.keras.activations.softmax),
            ]
        )
        
        optimizer = tf.keras.optimizers.SGD(
            learning_rate=0.005,
            momentum=0.4,
        )
        
        model.compile(
            optimizer=optimizer, loss="sparse_categorical_crossentropy", metrics=["accuracy"]
        )
        
        neptune_cbk = NeptuneCallback(run=run, base_namespace="metrics")
        model.fit(x_train, y_train, epochs=5, batch_size=64, callbacks=[neptune_cbk])
        ```
        
        [![neptune-pl](https://img.shields.io/badge/TF/Keras-experiment-success?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIAAAAgCAYAAABQISshAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAF6klEQVRYCa2YXWiWZRjH301nGWVTa36ESWqb1bTSorSTUg8kwg4iSIhOgo49yDoIOomgjiqQPCroIEGQqA6ighoIZYbZNEtnhU1rUc3U5fzY3Nbv//hcT//33rN37zu74Nr1fV33fd1fz95KpQaMjo7OHBsb2wy+A74HbgXbaoTUZSLHevBj8A/wGLiNWjfVFdyokxJT4CMwhb0obm40X/iT9zHiB9KkyF3Yrg+//4WSdBq4s6RYqN6GaWq0GDGLwePgRLCl0Zw1/amyDhyaqBr6U2B7zSQlRmJeLcvJSoR6P8w1JaGNq0jUBO6IzDm9CB1OdFsbyU7sQvDXJMfZRL6EvKGRvOHbHIzRpfAbTT4L/zj4FHjB9I/SyRkmT8Y+jIMf6N3Ia8HtFjgNfpPJU2fpyJakS7siG/ous52DXxW2WhS/ZvB9ixX7pGJoxhL4k2Y7At/woa9aEZKqI+qcw2cm7DF+Jvw6kydkGdgCjPebwwD815Kbmpp+CV4ysAxcnXEN/KmaCEl1rXqSIeRuy3cAfszkhxikJl8TyKuVu9GcjsD3SsY2CvlKfA7K92AI9dKqiRB0HzjHgvvgj4bMoA/BnwsZugrdPJMnYtdg8FoHmMBFc94PrwkFPFBPg8JZ1JNLVkGHHxBOhYLi6uLxkKFt6FaYPI7NB5SeJa1sAfiojrZbQCc6bce6oZgIgbqBfFspiTrnnRpEp6IBik9jwhZ0LsztIUAvgVrZAqjxG8LPhaJSuQG+ZoPMN2OLiSC1ge2Jw3cuU1Dnw8+MzKtHRkZqvfLKOV+OOZyEFttVOvLqWvfJNaNLV1GuE4JPZCles83zPHyPycFqIr5KnRSdFcYSuhJdi+k1iX6Tg00bpPNXq0ERl9F0ItPN+jv8MZODVedOhwBdxESWmJyydyWKQ/gPJzqJB8ER03fCX2dyFcskV4CvgM/xbMzxgS+u8ry8GmcSncS4yeJd0HtyB/itjA75yy+bQ9VBN4OuZK1U3IKL4G8Bx/kz+Dnod4CarLZmh6/IVVIapAc9MxGUvi3Sp10PX507PXABWgk/C6EX/RP0sxMNcp+MZyKq5w1a7xPZhfF4HnUC5505X0Z07zusxL/sYbwVJ3Uv4C/8fgzBKQ3Sbabt5VDaIBxuA/38/FNsLRLtYytsgN6N04Hm5uayg54VYTAH85WZkSkqlQ50s+G1NRzUtaIGfA9xf7uD8+Toxu4qnQPdYH65aCtlW8oce7xIhcGrW6UdsyAlko/ufu1hwXx0uvXSiejGctBBV+dLAZuu+4tgbPPlTKQVuZg8ssa8HHTY51vLDZPxp3HwN0Yr43u2QsFxOny6ayUmRg3qM58FeYNMlW3VZabQah2c0kRIruBvLJnYdD/rI9EL6tHzySumCnhY1aDDptTKVDUIWSuvSyRgAKZnShPJM2givnc7GYhv1XbsftBPINfcti0tLcqXXrd3onPQQddqB/Sykn1XMhHdMPrcCFjOSs0NAaoV8olpW50xeynLoNQg/1dBB95vxPSgf9/f33/+Siail9/fBH0J68YLuCcYUQbzJXYfoJsLHh9tP22XAH1wZv/LkENXWkcYcrq/ra1t3Gd84jOxSEHdPuk/RI8ogoLXQu4Vn4O+23aHMAntxe7nROchcrXC+4oMI/sYsuK+DbBPDrw7Gxn0CBign4meAV8LRU734nv15BkvexCjbygHvXGbULwpJXzYDsPPyqLQzANfB7vAlziwlw11VCVJKzGHQU8uMYUX60hXuJB3DQkuKEkMGpqNXiR0mN8oghDeVYDBy0ND+pyqD4h7wWLL2DMo0yu0ZnL8p4MfKpkNWqLL+q1tbZEIQdvBYQ/B/v9D4VvG4LuA4J+UIC2aJ902ODhY9d1RlifVkUv/t6c/4OUpM/IWf/+7rBA+ldoGsR2xocLEbiZGHfI84j8HsxsnHWg9MumeVUrlTeATZH8Usx/I2lF+ASrgAwovrKdI6kPsE2A3KOgD9TvvlCcR+cnxNKgf7QRHwec5x+N+wPsX+f7UoKzjPDEAAAAASUVORK5CYII=)](https://app.neptune.ai/common/tf-keras-integration/e/TFK-18/all)
        &nbsp;
        
        [![github-code](https://img.shields.io/badge/GitHub-code-informational?logo=github)](https://github.com/neptune-ai/examples/tree/main/integrations-and-supported-tools/tensorflow-keras/scripts)
        [![jupyter-code](https://img.shields.io/badge/Jupyter-code-informational?logo=jupyter)](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/tensorflow-keras/notebooks/Neptune_TensorFlow_Keras.ipynb)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/tensorflow-keras/notebooks/Neptune_TensorFlow_Keras.ipynb)
        [<img src="https://img.shields.io/badge/docs-TensorFow%2FKeras-yellow">](https://docs.neptune.ai/integrations/keras/)
        
        #### Scikit-learn
        
        <img src="https://neptune.ai/wp-content/uploads/Scikit-learn.png" width="200" /><br><br>
        
        Example:
        
        ```python
        from sklearn.datasets import load_digits
        from sklearn.ensemble import GradientBoostingClassifier
        from sklearn.model_selection import train_test_split
        import neptune.new as neptune
        import neptune.new.integrations.sklearn as npt_utils
        
        run = neptune.init_run(
            project="common/sklearn-integration",
            api_token=neptune.ANONYMOUS_API_TOKEN,
            name="classification-example",
            tags=["GradientBoostingClassifier", "classification"],
        )
        
        parameters = {
            "n_estimators": 120,
            "learning_rate": 0.12,
            "min_samples_split": 3,
            "min_samples_leaf": 2,
        }
        
        gbc = GradientBoostingClassifier(**parameters)
        
        X, y = load_digits(return_X_y=True)
        X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size=0.20, random_state=28743
        )
        
        gbc.fit(X_train, y_train)
        
        run["cls_summary"] = npt_utils.create_classifier_summary(
            gbc, X_train, X_test, y_train, y_test
        )
        ```
        
        [![neptune-pl](https://img.shields.io/badge/sklearn-experiment-success?logo=data:image/png;base64,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)](https://app.neptune.ai/common/sklearn-integration/e/SKLEAR-97/all?path=rfr_summary%2Fdiagnostics_charts&attribute=feature_importance)
        &nbsp;
        
        [![github-code](https://img.shields.io/badge/GitHub-code-informational?logo=github)](https://github.com/neptune-ai/examples/tree/main/integrations-and-supported-tools/sklearn/scripts)
        [![jupyter-code](https://img.shields.io/badge/Jupyter-code-informational?logo=jupyter)](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/sklearn/notebooks/Neptune_Scikit_learn.ipynb)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/sklearn/notebooks/Neptune_Scikit_learn.ipynb)
        [<img src="https://img.shields.io/badge/docs-Scikit--learn-yellow">](https://docs.neptune.ai/integrations/sklearn/)
        
        #### fastai
        
        <img src="https://neptune.ai/wp-content/uploads/fastai-1.png" width="150" /><br><br>
        
        Example:
        
        ```python
        import fastai
        from neptune.new.integrations.fastai import NeptuneCallback
        from fastai.vision.all import *
        import neptune.new as neptune
        
        run = neptune.init_run(
            project="common/fastai-integration",
            api_token=neptune.ANONYMOUS_API_TOKEN,
            tags="basic",
        )
        
        path = untar_data(URLs.MNIST_TINY)
        dls = ImageDataLoaders.from_csv(path)
        
        # Log all training phases of the learner
        learn = cnn_learner(
            dls, resnet18, cbs=[NeptuneCallback(run=run, base_namespace="experiment")]
        )
        learn.fit_one_cycle(2)
        learn.fit_one_cycle(1)
        
        run.stop()
        ```
        
        [![neptune-pl](https://img.shields.io/badge/fastai-experiment-success?logo=data:image/png;base64,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)](https://app.neptune.ai/common/fastai-integration/e/FAS-61/dashboard/fastai-dashboard-1f456716-f509-4432-b8b3-a7f5242703b6)
        &nbsp;
        
        [![github-code](https://img.shields.io/badge/GitHub-code-informational?logo=github)](https://github.com/neptune-ai/examples/tree/main/integrations-and-supported-tools/fastai/scripts)
        [![jupyter-code](https://img.shields.io/badge/Jupyter-code-informational?logo=jupyter)](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/fastai/notebooks/Neptune_fastai.ipynb)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/fastai/notebooks/Neptune_fastai.ipynb)
        [<img src="https://img.shields.io/badge/docs-fastai-yellow">](https://docs.neptune.ai/integrations/fastai/)
        
        #### Optuna
        
        <img src="https://neptune.ai/wp-content/uploads/Optuna-1.png" width="300" /><br><br>
        
        Example:
        
        ```python
        import lightgbm as lgb
        import neptune.new as neptune
        import neptune.new.integrations.optuna as optuna_utils
        import optuna
        from sklearn.datasets import load_breast_cancer
        from sklearn.metrics import roc_auc_score
        from sklearn.model_selection import train_test_split
        
        def objective(trial):
            data, target = load_breast_cancer(return_X_y=True)
            train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)
            dtrain = lgb.Dataset(train_x, label=train_y)
        
            param = {
                "verbose": -1,
                "objective": "binary",
                "metric": "binary_logloss",
                "num_leaves": trial.suggest_int("num_leaves", 2, 256),
                "feature_fraction": trial.suggest_uniform("feature_fraction", 0.2, 1.0),
                "bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.2, 1.0),
                "min_child_samples": trial.suggest_int("min_child_samples", 3, 100),
            }
        
            gbm = lgb.train(param, dtrain)
            preds = gbm.predict(test_x)
            accuracy = roc_auc_score(test_y, preds)
        
            return accuracy
        
        
        # Create a Neptune run
        run = neptune.init_run(
            api_token=neptune.ANONYMOUS_API_TOKEN,
            project="common/optuna-integration",
        )
        
        # Create a NeptuneCallback for Optuna
        neptune_callback = optuna_utils.NeptuneCallback(run)
        
        # Pass NeptuneCallback to Optuna Study .optimize()
        study = optuna.create_study(direction="maximize")
        study.optimize(objective, n_trials=20, callbacks=[neptune_callback])
        
        # Stop logging to the run
        run.stop()
        ```
        
        [![neptune-pl](https://img.shields.io/badge/Optuna-experiment-success?logo=data:image/png;base64,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)](https://app.neptune.ai/common/optuna-integration/experiments?split=bth&dash=parallel-coordinates-plot&viewId=b6190a29-91be-4e64-880a-8f6085a6bb78)
        &nbsp;
        
        [![github-code](https://img.shields.io/badge/GitHub-code-informational?logo=github)](https://github.com/neptune-ai/examples/tree/main/integrations-and-supported-tools/optuna/scripts)
        [![jupyter-code](https://img.shields.io/badge/Jupyter-code-informational?logo=jupyter)](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/optuna/notebooks/Neptune_Optuna_integration.ipynb)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/optuna/notebooks/Neptune_Optuna_integration.ipynb)
        [<img src="https://img.shields.io/badge/docs-Optuna-yellow">](https://docs.neptune.ai/integrations/optuna/)
        
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Keywords: MLOps,ML Experiment Tracking,ML Model Registry,ML Model Store,ML Metadata Store
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Provides-Extra: kedro
Provides-Extra: fastai
Provides-Extra: lightgbm
Provides-Extra: optuna
Provides-Extra: pytorch-lightning
Provides-Extra: sacred
Provides-Extra: sklearn
Provides-Extra: tensorflow-keras
Provides-Extra: transformers
Provides-Extra: xgboost
Provides-Extra: prophet
