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

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)

model.fit(
    x_train,
    y_train,
    epochs=5,
    batch_size=64,
    callbacks=[neptune_cbk],
)

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):

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

    run["logs/training/batch/loss"].log(loss)
    run["logs/training/batch/acc"].log(acc)

    loss.backward()
    optimizer.step()

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(
    params,
    lgb_train,
    num_boost_round=200,
    valid_sets=[lgb_train, lgb_eval],
    valid_names=["training", "validation"],
    callbacks=[neptune_callback],
)

run["lgbm_summary"] = create_booster_summary(
    booster=gbm,
    log_trees=True,
    list_trees=[0, 1, 2, 3, 4],
    log_confusion_matrix=True,
    y_pred=y_pred,
    y_true=y_test,
)

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"],
}

xgb.train(
    params=params,
    dtrain=dtrain,
    num_boost_round=num_round,
    evals=evals,
    callbacks=[neptune_callback],
)

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)
    neptune_run["nodes/report/confusion_matrix"].upload(fig)

def create_pipeline(**kwargs):
    return Pipeline(
        [# ...
            node(
                report_accuracy,
                ["example_predictions", "example_test_y","neptune_run"],
                None,
                name="report",
            ),
        ]
    )