gradient-sdk 0.0.3

Gradient ML SDK

Gradient ML SDK

This is an SDK for performing Machine Learning with Gradientº, intended to work with Paperspace infrastructure.

Remember: Currently, this SDK does not support machines outside of Paperspace infrastructure!

Requirements

This SDK requires Python 3.5+.

To install it, run:

pip install gradient-sdk

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_sdk 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_sdk 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_sdk import get_mongo_conn_str

conn_str = get_mongo_conn_str()

data_dir()

Function to retrieve path to job space.

Usage example:

from gradient_sdk import data_dir

job_space = data_dir()

model_dir()

Function to retrieve path to model space.

Usage example:

from gradient_sdk import model_dir

model_path = model_dir(model_name)

export_dir()

Function to retrieve path for model export.

Usage example:

from gradient_sdk import export_dir

model_path = export_dir(model_name)

worker_hosts()

Function to retrieve information about worker hosts.

Usage example:

from gradient_sdk import worker_hosts

model_path = worker_hosts()

ps_hosts()

Function to retrieve information about Paperspace hosts.

Usage example:

from gradient_sdk import ps_hosts

model_path = ps_hosts()

task_index()

Function to retrieve information about task index.

Usage example:

from gradient_sdk import task_index

model_path = task_index()

job_name()

Function to retrieve information about job name.

Usage example:

from gradient_sdk import job_name

model_path = job_name()