optscale-arcee 0.1.45

ML profiling tool for OptScale

Arcee

The OptScale ML profiling tool by Hystax

Arcee is a tool that helps you to integrate ML tasks with OptScale. This tool can automatically collect executor metadata from the cloud and process stats.

Installation

Arcee requires Python 3.7+ to run. To install the optscale_arcee package, use pip:

pip install optscale-arcee

Import

Import the optscale_arcee module into your code as follows:

import optscale_arcee as arcee

Initialization

To initialize the arcee collector you need to provide a profiling token and a task key for which you want to collect data. To initialize the collector using a context manager, use the following code snippet:

with arcee.init(token, task_key):
    # some code

Example:

with arcee.init("00000000-0000-0000-0000-000000000000", "linear_regression"):
    # some code

This method automatically handles error catching and terminates arcee execution.

Alternatively, to get more control over error catching and execution finishing, you can initialize the collector using a corresponding method. Note that this method will require you to manually handle errors or terminate arcee execution using the error and finish methods.

arcee.init(token, task_key)
# some code
arcee.finish()
# or in case of error
arcee.error()

To use a custom endpoint and enable/disable SSL checks (enable self-signed SSL certificates support):

with arcee.init(token, task_key, endpoint_url="https://my.custom.endpoint:443/arcee/v2", ssl=False):
    # some code

Arcee daemon process periodically sends hardware and process data. The default heartbeat period is 1 second. However, arcee can be initialized with a custom period:

with arcee.init(token, task_key, period=5):
    # some code

Sending metrics

To send metrics, use the send method with the following parameter:

• data (dict, required): a dictionary of metric names and their respective values (note that metric data values should be numeric).

arcee.send({ "metric_key_1": value_1, "metric_key_2": value_2 })

Example:

arcee.send({ "accuracy": 71.44, "loss": 0.37 })

Adding hyperparameters

To add hyperparameters, use the hyperparam method with the following parameters:

• key (str, required): the hyperparameter name.
• value (str | number, required): the hyperparameter value.

arcee.hyperparam(key, value)

Example:

arcee.hyperparam("EPOCHS", 100)

Tagging task run

To tag a run, use the tag method with the following parameters:

• key (str, required): the tag name.
• value (str | number, required): the tag value.

arcee.tag(key, value)

Example:

arcee.tag("Algorithm", "Linear Learn Algorithm")

Adding milestone

To add a milestone, use the milestone method with the following parameter:

• name (str, required): the milestone name.

arcee.milestone(name)

Example:

arcee.milestone("Download training data")

Adding stage

To add a stage, use the stage method with the following parameter:

• name (str, required): the stage name.

arcee.stage(name)

Example:

arcee.stage("preparing")

Logging datasets

To log a dataset, use the dataset method with the following parameters:

• path (str, required): the dataset path.
• name (str, optional): the dataset name.
• description (str, optional): the dataset description.
• labels (list, optional): the dataset labels.

arcee.dataset(path, name, description, labels)

Example:

arcee.dataset("https://s3/ml-bucket/datasets/training_dataset.csv",
              name="Training dataset",
              description="Training dataset (100k rows)",
              labels=["training", "100k"])

Creating models

To create a model, use the model method with the following parameters:

• key (str, required): the unique model key.
• path (str, optional): the run model path.

arcee.model(key, path)

Example:

arcee.model("my_model", "/home/user/my_model")

Setting model version

To set a custom model version, use the model_version method with the following parameter:

• version (str, required): the version name.

arcee.model_version(version)

Example:

arcee.model_version("1.2.3-release")

Setting model version alias

To set a model version alias, use the model_version_alias method with the following parameter:

• alias (str, required): the alias name.

arcee.model_version_alias(alias)

Example:

arcee.model_version_alias("winner")

Setting model version tag

To add tags to a model version, use the model_version_tag method with the following parameters:

• key (str, required): the tag name.
• value (str, required): the tag value.

arcee.model_version_tag(key, value)

Example:

arcee.model_version_tag("env", "staging demo")

Creating artifacts

To create an artifact, use the artifact method with the following parameters:

• path (str, required): the run artifact path.
• name (str, optional): the artifact name.
• description (str, optional): the artifact description.
• tags (dict, optional): the artifact tags.

arcee.artifact(path, name, description, tags)

Example:

arcee.artifact("https://s3/ml-bucket/artifacts/AccuracyChart.png",
               name="Accuracy line chart",
               description="The dependence of accuracy on the time",
               tags={"env": "staging"})

Setting artifact tag

To add a tag to an artifact, use the artifact_tag method with the following parameters:

• path (str, required): the run artifact path.
• key (str, required): the tag name.
• value (str, required): the tag value.

arcee.artifact_tag(path, key, value)

Example:

arcee.artifact_tag("https://s3/ml-bucket/artifacts/AccuracyChart.png",
                   "env", "staging demo")

Finishing task run

To finish a run, use the finish method.

arcee.finish()

Failing task run

To fail a run, use the error method.

arcee.error()