kiroframe-arcee 0.1.55

ML profiling tool for Kiroframe

Kiroframe_arcee

The Kiroframe ML profiling tool by Hystax

Kiroframe_arcee (short name - Kiro) is a tool that helps you to integrate ML tasks with Kiroframe. This tool can automatically collect executor metadata from the cloud and process stats.

Installation

Kiroframe_arcee requires Python 3.8+ to run. To start, install the kiroframe_arcee package, use pip:

pip install kiroframe-arcee

Import

Import the kiroframe_arcee module into your code as follows:

import kiroframe_arcee as kiro

Initialization

To initialize the Kiro 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.
• period (int, optional): Kiro daemon process heartbeat period in seconds (default is 1).

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

with kiro.init(token="YOUR-PROFILING-TOKEN",
               task_key="YOUR-TASK-KEY",
               run_name="YOUR-RUN-NAME",
               period=PERIOD):
    # some code

Examples:

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

This method automatically handles error catching and terminates Kiro 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 Kiro execution using the error and finish methods.

kiro.init(token="YOUR-PROFILING-TOKEN", task_key="YOUR-TASK-KEY")
# some code
kiro.finish()
# or in case of error
kiro.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).

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

Example:

kiro.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.

kiro.hyperparam(key="YOUR-PARAM-KEY", value=YOUR_PARAM_VALUE)

Example:

kiro.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.

kiro.tag(key="YOUR-TAG-KEY", value=YOUR_TAG_VALUE)

Example:

kiro.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.

kiro.milestone(name="YOUR-MILESTONE-NAME")

Example:

kiro.milestone("Download training data")

Adding stage

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

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

kiro.stage(name="YOUR-STAGE-NAME")

Example:

kiro.stage("preparing")

Datasets

Logging

Logging a dataset allows you to create a dataset or a new version of the dataset if the dataset has already been created, but has been changed. To create a dataset, use the Dataset class with the following parameters:

Dataset parameters:

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

Version parameters:

• aliases (list, optional): the list of aliases for this version.
• meta (dict, optional): the dataset version meta.
• timespan_from (int, optional): the dataset version timespan from.
• timespan_to (int, optional): the dataset version timespan to.

dataset = kiro.Dataset(key='YOUR-DATASET-KEY', 
                       name='YOUR-DATASET-NAME',
                       description="YOUR-DATASET-DESCRIPTION",
                       ...
                       )
dataset.labels = ["YOUR-DATASET-LABEL-1", "YOUR-DATASET-LABEL-2"]
dataset.aliases = ['YOUR-VERSION-ALIAS']

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

• dataset (Dataset, required): the dataset object.
• comment (str, optional): the usage comment.

kiro.log_dataset(dataset=dataset, comment='LOGGING_COMMENT')

Using

To use a dataset, use the use_dataset method with dataset key:version. Parameters:

• dataset (str, required): the dataset identifier in key:version format.
• comment (str, optional): the usage comment.

dataset = kiro.use_dataset(
    dataset='YOUR-DATASET-KEY:YOUR-DATASET-VERSION-OR-ALIAS')

Adding files and downloading

You can add or remove files from dataset and download it as well. Supported file paths:

• file:// - the local files.
• s3:// - the amazon S3 files.

adding / removing files

local:

dataset.remove_file(path='file://LOCAL_PATH_TO_FILE_1')
dataset.add_file(path='file://LOCAL_PATH_TO_FILE_2')
kiro.log_dataset(dataset=dataset)

s3:

os.environ['AWS_ACCESS_KEY_ID'] = 'AWS_ACCESS_KEY_ID'
os.environ['AWS_SECRET_ACCESS_KEY'] = 'AWS_SECRET_ACCESS_KEY'
dataset.remove_file(path='s3://BUCKET/PATH_1')
dataset.add_file(path='s3://BUCKET/PATH_2')
kiro.log_dataset(dataset=dataset)

downloading: Parameters:

• overwrite (bool, optional): overwrite an existing dataset or skip downloading if it already exists.

dataset.download(overwrite=True)

Example:

# use version v0, v1 etc, or any version alias: my_dataset:latest
dataset = kiro.use_dataset(dataset='my_dataset:V0')
path_map = dataset.download()
for local_path in path_map.values():
    with open(local_path, 'r'):
        # read downloaded file

new_dataset = kiro.Dataset('new_dataset')
new_dataset.add_file(path='s3://ml-bucket/datasets/training_dataset.csv')
kiro.log_dataset(dataset=new_dataset)
new_dataset.download()

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.

kiro.model(key="YOUR-MODEL-KEY", path="YOUR-MODEL-PATH")

Example:

kiro.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.

kiro.model_version(version="YOUR-MODEL-VERSION")

Example:

kiro.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.

kiro.model_version_alias(alias="YOUR-MODEL-VERSION-ALIAS")

Example:

kiro.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.

kiro.model_version_tag(key="YOUR-MODEL-VERSION-TAG-KEY", value=YOUR_MODEL_VERSION_TAG_VALUE)

Example:

kiro.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.

kiro.artifact(path="YOUR-ARTIFACT-PATH",
              name="YOUR-ARTIFACT-NAME",
              description="YOUR-ARTIFACT-DESCRIPTION",
              tags={"YOUR-ARTIFACT-TAG-KEY": YOUR_ARTIFACT_TAG_VALUE})

Example:

kiro.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.

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

Example:

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

Finishing task run

To finish a run, use the finish method.

kiro.finish()

Failing task run

To fail a run, use the error method.

kiro.error()