Working with artifacts: Sharing dataset versions on project level#

You can log and query metadata on project level, including dataset and model versions, text notes, images, notebook files, and anything else you can log to a single run.

In this tutorial, we'll track artifacts as project metadata. The flow includes:

Logging versions of all the datasets used in a project.
Organizing dataset version metadata in the Neptune app.
Sharing all the currently used dataset versions with your team.
Asserting that you're training on the latest dataset version available.

See results in Neptune Code examples

Before you start#

Sign up at neptune.ai/register.
Create a project for storing your metadata.
Install Neptune:
pipconda
pip install neptune
conda install -c conda-forge neptune
Installing through Anaconda Navigator

To find neptune, you may need to update your channels and index.

In the Navigator, select Environments.

In the package view, click Channels.

Click Add..., enter conda-forge, and click Update channels.

In the package view, click Update index... and wait until the update is complete. This can take several minutes.

You should now be able to search for neptune.

Note: The displayed version may be outdated. The latest version of the package will be installed.
Note: On Bioconda, there is a "neptune" package available which is not the neptune.ai client library. Make sure to specify the "conda-forge" channel when installing neptune.ai.
Passing your Neptune credentials

Once you've registered and created a project, set your Neptune API token and full project name to the NEPTUNE_API_TOKEN and NEPTUNE_PROJECT environment variables, respectively.
```
export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...6Lc"
```
To find your API token: In the bottom-left corner of the Neptune app, expand the user menu and select Get my API token.
```
export NEPTUNE_PROJECT="ml-team/classification"
```
To find your project: Your full project name has the form workspace-name/project-name. To copy the name, click the menu in the top-right corner and select Edit project details.

While it's not recommended especially for the API token, you can also pass your credentials in the code when initializing Neptune.
```
run = neptune.init_run(
    project="ml-team/classification",  # your full project name here
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh...3Kb8",  # your API token here
)
```
For more help, see Set Neptune credentials.
Have a couple of sample CSV files handy: train.csv and test.csv.
Have the scikit-learn Python library installed.

Tip

If you already took the dataset versioning tutorial, or just want to check out the artifact logging without training any models, you can skip to the Track several dataset versions in project metadata section and run the snippets from there.

Prepare a model training script#

To start, create a training script train.py:

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

import neptune

Track several dataset versions in project metadata#

To log project metadata through the API, initialize the project as a Neptune object.

You can log metadata to it just as you would to a run.
```
project = neptune.init_project(
    project="workspace-name/project-name",  # (1)!
)
```
1. The full project name. For example, "ml-team/classification".
  
  To copy it, navigate to the project settings in the top-right () and select Edit project details.

Save a few dataset versions as Neptune artifacts to the project.

SnippetFull script

train = pd.read_csv("train.csv")

for i in range(5):
    train_sample = train.sample(frac=0.5 + 0.1 * i)
    train_sample.to_csv("train_sampled.csv", index=None)
    project[f"datasets/train_sampled/v{i}"].track_files(
        "train_sampled.csv", wait=True  # (1)!
    )

print(project.get_structure())

Use wait=True to ensure that all logging operations are finished. See also: API reference ≫ wait()

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

import neptune

# Initialize Neptune project
project = neptune.init_project(
    project="common/data-versioning",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Create a few versions of a dataset and track them as Neptune artifacts
train = pd.read_csv("train.csv")

for i in range(5):
    train_sample = train.sample(frac=0.5 + 0.1 * i)
    train_sample.to_csv("train_sampled.csv", index=None)
    project[f"datasets/train_sampled/v{i}"].track_files(
        "train_sampled.csv", wait=True
    )

print(project.get_structure())

Save the latest dataset version as a new artifact called "latest":

SnippetFull script

def get_latest_version():
    # Get the latest version of the dataset and save it as 'latest'
    artifact_name = project.get_structure()["datasets"]["train_sampled"].keys()
    versions = [
        int(version.replace("v", ""))
        for version in artifact_name
        if version != "latest"
    ]
    latest_version = max(versions)
    return latest_version

latest_version = get_latest_version()
print("latest version", latest_version)

project["datasets/train_sampled/latest"].assign(
    project[f"datasets/train_sampled/v{latest_version}"].fetch(), wait=True
)

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

import neptune

# Initialize Neptune project
project = neptune.init_project(
    project="common/data-versioning",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Create a few versions of a dataset and track them as Neptune artifacts
train = pd.read_csv("train.csv")

for i in range(5):
    train_sample = train.sample(frac=0.5 + 0.1 * i)
    train_sample.to_csv("train_sampled.csv", index=None)
    project[f"datasets/train_sampled/v{i}"].track_files(
        "train_sampled.csv", wait=True
    )

print(project.get_structure())


def get_latest_version():
    # Get the latest version of the dataset and save it as 'latest'
    artifact_name = project.get_structure()["datasets"]["train_sampled"].keys()
    versions = [
        int(version.replace("v", ""))
        for version in artifact_name
        if version != "latest"
    ]
    latest_version = max(versions)
    return latest_version


latest_version = get_latest_version()
print("latest version", latest_version)

project["datasets/train_sampled/latest"].assign(
    project[f"datasets/train_sampled/v{latest_version}"].fetch(), wait=True
)

Access dataset versions via API#

You can now list the available dataset versions with the get_structure() method:

SnippetFull script

print(project.get_structure()["datasets"])

Sample output

{'train_sampled': {'latest': <neptune.attributes.atoms.artifact.Artifact object at 0x000001BF367D0DC0>, 'v0': <neptune.attributes.atoms.artifact.Artifact object at 0x000001BF367A7700>, 'v1': <neptune.attributes.atoms.artifact.Artifact object at 0x000001BF36806DA0>, 'v2': <neptune.attributes.atoms.artifact.Artifact object at 0x000001BF36806E00>, 'v3': <neptune.attributes.atoms.artifact.Artifact object at 0x000001BF36806E60>, 'v4': <neptune.attributes.atoms.artifact.Artifact object at 0x000001BF36806EC0>}}

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

import neptune

# Initialize Neptune project
project = neptune.init_project(
    project="common/data-versioning",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Create a few versions of a dataset and save them to Neptune
train = pd.read_csv("train.csv")

for i in range(5):
    train_sample = train.sample(frac=0.5 + 0.1 * i)
    train_sample.to_csv("train_sampled.csv", index=None)
    project[f"datasets/train_sampled/v{i}"].track_files(
        "train_sampled.csv", wait=True
    )

print(project.get_structure())


def get_latest_version():
    # Get the latest version of the dataset and save it as 'latest'
    artifact_name = project.get_structure()["datasets"]["train_sampled"].keys()
    versions = [
        int(version.replace("v", ""))
        for version in artifact_name
        if version != "latest"
    ]
    latest_version = max(versions)
    return latest_version


latest_version = get_latest_version()
print("latest version", latest_version)

project["datasets/train_sampled/latest"].assign(
    project[f"datasets/train_sampled/v{latest_version}"].fetch(), wait=True
)

print(project.get_structure()["datasets"])

View dataset versions in app#

To view the dataset versions in the Neptune app:

Select the Project metadata tab.
Click the datasets namespace, then the train_sampled namespace.
Select each artifact in the list to preview the metadata on the right.

See example in Neptune

Going further: Assert that you're training on the latest dataset#

In this last part, we'll show an example of how you can interact with the tracked artifacts.

We'll fetch the dataset version marked as "latest" and assert that we're using that same version to train our model.

Create a Neptune run:

run = neptune.init_run()  # (1)!

If you haven't set up your credentials, you can log anonymously:

neptune.init_run(
    api_token=neptune.ANONYMOUS_API_TOKEN,
    project="common/data-versioning",
)

Log the current dataset as an artifact:

TRAIN_DATASET_PATH = "train_sampled.csv"
run["datasets/train"].track_files(TRAIN_DATASET_PATH, wait=True)

Assert that the current dataset is the latest version:

assert (
    run["datasets/train"].fetch_hash()
    == project["datasets/train_sampled/latest"].fetch_hash()
)

Tip

If you want to download the latest version of the dataset, you can use download() on the artifact field:

project["datasets/train_sampled/latest"].download()

Train the model and log the metadata to Neptune:

SnippetFull script

TEST_DATASET_PATH = "test.csv"

# Log parameters
PARAMS = {
    "n_estimators": 8,
    "max_depth": 3,
    "max_features": 2,
}
run["parameters"] = PARAMS

# Train the model
train = pd.read_csv(TRAIN_DATASET_PATH)
test = pd.read_csv(TEST_DATASET_PATH)

FEATURE_COLUMNS = [
    "sepal.length",
    "sepal.width",
    "petal.length",
    "petal.width",
]
TARGET_COLUMN = ["variety"]
X_train, y_train = train[FEATURE_COLUMNS], train[TARGET_COLUMN]
X_test, y_test = test[FEATURE_COLUMNS], test[TARGET_COLUMN]

rf = RandomForestClassifier(**PARAMS)
rf.fit(X_train, y_train)

# Save the score
score = rf.score(X_test, y_test)
run["metrics/test_score"] = score

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

import neptune

# Initialize Neptune project
project = neptune.init_project(
    project="common/data-versioning",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Create a few versions of a dataset and track them as Neptune artifacts
train = pd.read_csv("train.csv")

for i in range(5):
    train_sample = train.sample(frac=0.5 + 0.1 * i)
    train_sample.to_csv("train_sampled.csv", index=None)
    project[f"datasets/train_sampled/v{i}"].track_files(
        "train_sampled.csv", wait=True
    )

print(project.get_structure())


def get_latest_version():
    # Get the latest version of the dataset and save it as 'latest'
    artifact_name = project.get_structure()["datasets"]["train_sampled"].keys()
    versions = [
        int(version.replace("v", ""))
        for version in artifact_name
        if version != "latest"
    ]
    latest_version = max(versions)
    return latest_version


latest_version = get_latest_version()
print("latest version", latest_version)

project["datasets/train_sampled/latest"].assign(
    project[f"datasets/train_sampled/v{latest_version}"].fetch(), wait=True
)

print(project.get_structure()["datasets"])

# Create a Neptune run
run = neptune.init_run(
    project="common/data-versioning",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Assert that you're training on the latest dataset version
TRAIN_DATASET_PATH = "train_sampled.csv"
run["datasets/train"].track_files(TRAIN_DATASET_PATH, wait=True)

assert (
    run["datasets/train"].fetch_hash()
    == project["datasets/train_sampled/latest"].fetch_hash()
)

TEST_DATASET_PATH = "test.csv"

# Log parameters
PARAMS = {
    "n_estimators": 8,
    "max_depth": 3,
    "max_features": 2,
}
run["parameters"] = PARAMS

# Train the model
train = pd.read_csv(TRAIN_DATASET_PATH)
test = pd.read_csv(TEST_DATASET_PATH)

FEATURE_COLUMNS = [
    "sepal.length",
    "sepal.width",
    "petal.length",
    "petal.width",
]
TARGET_COLUMN = ["variety"]
X_train, y_train = train[FEATURE_COLUMNS], train[TARGET_COLUMN]
X_test, y_test = test[FEATURE_COLUMNS], test[TARGET_COLUMN]

rf = RandomForestClassifier(**PARAMS)
rf.fit(X_train, y_train)

# Save the score
score = rf.score(X_test, y_test)
run["metrics/test_score"] = score

#
# Go to the Neptune app to see datasets logged at the project level!
#

Stop the active Neptune objects:
```
run.stop()
project.stop()
```

To view the run in Neptune, click the link in the console output.