gradient-utils 0.3.2

This is an SDK for performing Machine Learning with Gradient.

Gradient Utils

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Gradient is an an end-to-end MLOps platform that enables individuals and organizations to quickly develop, train, and deploy Deep Learning models. The Gradient software stack runs on any infrastructure e.g. AWS, GCP, on-premise and low-cost Paperspace GPUs. Leverage automatic versioning, distributed training, built-in graphs & metrics, hyperparameter search, GradientCI, 1-click Jupyter Notebooks, our Python SDK, and more.

This is an SDK for performing Machine Learning with Gradientº, it can be installed in addition to gradient-cli.

Requirements

This SDK requires Python 3.6+.

To install it, run:

pip install gradient-utils

Usage

Multinode Helper Functions

Multinode GRPC Tensorflow

Set the TF_CONFIG environment variable For multi-worker training, you need to set the TF_CONFIG environment variable for each binary running in your cluster. Set the value of TF_CONFIG to a JSON string that specifies each task within the cluster, including each task's address and role within the cluster. We've provided a Kubernetes template in the tensorflow/ecosystem repo which sets TF_CONFIG for your training tasks.

get_tf_config()

Function to set value of TF_CONFIG when run on machines within Paperspace infrastructure.

It can raise a ConfigError exception with message if there's a problem with its configuration in a particular machine.

Usage example:

from gradient_utils import get_tf_config

get_tf_config()

Hyperparameter Tuning

Currently, Gradientº only supports Hyperopt for Hyperparameter Tuning.

hyper_tune()

Function to run hyperparameter tuning.

It accepts the following arguments:

• train_model User model to tune.
• hparam_def User definition (scope) of search space. To set this value, refer to hyperopt documentation.
• algo Search algorithm. Default: tpe.suggest (from hyperopt).
• max_ecals Maximum number of function evaluations to allow before returning. Default: 25.
• func Function to be run by hyper tune. Default: fmin (from hyperopt). Do not change this value if you do not know what you are doing!

It returns a dict with information about the tuning process.

It can raise a ConfigError exception with message if there's no connection to MongoDB.

Note: You do not need to worry about setting your MongoDB version; it will be set within Paperspace infrastructure for hyperparameter tuning.

Usage example:

from gradient_utils import hyper_tune

# Prepare model and search scope

# minimal version
argmin1 = hyper_tune(model, scope)

# pass more arguments
argmin2 = hyper_tune(model, scope, algo=tpe.suggest, max_evals=100)

Utility Functions

get_mongo_conn_str()

Function to check and construct MongoDB connection string.

It returns a connection string to MongoDB.

It can raise a ConfigError exception with message if there's a problem with any values used to prepare the MongoDB connection string.

Usage example:

from gradient_utils import get_mongo_conn_str

conn_str = get_mongo_conn_str()

data_dir()

Function to retrieve path to job space.

Usage example:

from gradient_utils import data_dir

job_space = data_dir()

model_dir()

Function to retrieve path to model space.

Usage example:

from gradient_utils import model_dir

model_path = model_dir(model_name)

export_dir()

Function to retrieve path for model export.

Usage example:

from gradient_utils import export_dir

model_path = export_dir(model_name)

worker_hosts()

Function to retrieve information about worker hosts.

Usage example:

from gradient_utils import worker_hosts

model_path = worker_hosts()

ps_hosts()

Function to retrieve information about Paperspace hosts.

Usage example:

from gradient_utils import ps_hosts

model_path = ps_hosts()

task_index()

Function to retrieve information about task index.

Usage example:

from gradient_utils import task_index

model_path = task_index()

job_name()

Function to retrieve information about job name.

Usage example:

from gradient_utils import job_name

model_path = job_name()

Metrics

Prometheus wrapper for logging custom metrics

Usage example:

from gradient_utils import MetricsLogger
# Comment: add_metrics is not supported at the moment. Stay tuned!
# from gradient_utils.metrics import add_metrics
m_logger = MetricsLogger()
m_logger.add_gauge("some_metric_1")
m_logger["some_metric_1"].set(3)
m_logger["some_metric_1"].inc()

m_logger.add_gauge("some_metric_2")
m_logger["some_metric_2"].set_to_current_time()

m_logger.push_metrics()

# Insert metrics with a single command
# add_metrics({
#   'loss': 0.25,
#   'accuracy': 0.99
# })

Contributing

Setup

We use Docker and Docker-compose to run the tests locally.

# To setup the integration test framework
docker-compose up --remove-orphans -d pushgateway
docker-compose build utils

# To run tests
docker-compose -f docker-compose.ci.yml run utils poetry run pytest