optscale-arcee 0.1.47

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 use the init method with the following parameters:

• token (str, required): the profiling token.
• task_key (str, required): the task key for which you want to collect data.
• run_name (str, optional): the run name.
• endpoint_url (str, optional): the custom OptScale endpoint (default is https://my.optscale.com/arcee/v2).
• ssl (bool, optional): enable/disable SSL checks (self-signed SSL certificates support).
• period (int, optional): arcee daemon process heartbeat period in seconds (default is 1).

To initialize the collector using a context manager, use the following code snippet:

with arcee.init(token="YOUR-PROFILING-TOKEN",
                task_key="YOUR-TASK-KEY",
                run_name="YOUR-RUN-NAME",
                endpoint_url="https://YOUR-OPTSCALE-PUBLIC-IP:443/arcee/v2",
                ssl=SSL,
                period=PERIOD):
    # some code

Examples:

with arcee.init("00000000-0000-0000-0000-000000000000", "linear_regression",
                run_name="My run name", ssl=True, period=1):
    # some code

For custom OptScale deployments:

with arcee.init("00000000-0000-0000-0000-000000000000", "linear_regression",
                run_name="My run name", endpoint_url="https://172.18.12.3:443/arcee/v2",
                ssl=False, period=5):
    # 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="YOUR-PROFILING-TOKEN", task_key="YOUR-TASK-KEY")
# some code
arcee.finish()
# or in case of error
arcee.error()

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({"YOUR-METRIC-1-KEY": YOUR_METRIC_1_VALUE, "YOUR-METRIC-2-KEY": YOUR_METRIC_2_VALUE})

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="YOUR-PARAM-KEY", value=YOUR_PARAM_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="YOUR-TAG-KEY", value=YOUR_TAG_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="YOUR-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="YOUR-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="YOUR-DATASET-PATH",
              name="YOUR-DATASET-NAME",
              description="YOUR-DATASET-DESCRIPTION",
              labels=["YOUR-DATASET-LABEL-1", "YOUR-DATASET-LABEL-2"])

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="YOUR-MODEL-KEY", path="YOUR-MODEL-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="YOUR-MODEL-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="YOUR-MODEL-VERSION-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 | number, required): the tag value.

arcee.model_version_tag(key="YOUR-MODEL-VERSION-TAG-KEY", value=YOUR_MODEL_VERSION_TAG_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="YOUR-ARTIFACT-PATH",
               name="YOUR-ARTIFACT-NAME",
               description="YOUR-ARTIFACT-DESCRIPTION",
               tags={"YOUR-ARTIFACT-TAG-KEY": YOUR_ARTIFACT_TAG_VALUE})

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 | number, required): the tag value.

arcee.artifact_tag(path="YOUR-ARTIFACT-PATH",
                   key="YOUR-ARTIFACT-TAG-KEY",
                   value=YOUR_ARTIFACT_TAG_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()