irisml 0.0.35

Simple ML pipeline platform

IrisML

Proof of Concept for a simple framework to create a ML pipeline.

Features

• Run a ML training/inference with a simple JSON configuration.
• Modularized interfaces for task components.
• Cache task outputs for faster experiments.

Getting started

Installation

Prerequisite: python 3.8+

# Install the core framework and standard tasks.
pip install irisml irisml-tasks irisml-tasks-training

Run an example job

# Install additional packages that are required for the example
pip install irisml-tasks-torchvision

# Run on local machine
irisml_run docs/examples/mobilenetv2_mnist_training.json

Available commands

# Run the specified pipeline. You can provide environment variables by "-e" option, which will be acceible through $env variable in the json config.
irisml_run <pipeline_json_path> [-e <ENV_NAME>=<env_value>] [--no_cache] [--no_cache_read] [-v]

# Show information about the specified task. If <task_name> is not provided, shows a list of available tasks in the current environment.
irisml_show [<task_name>]

# Manage a cache storage on Azure Blob Storage
# list - Show a list of matched blobs.
# download - Download matched blobs.
# remove - Remove matched blobs.
# show - Show the contents of matched blobs.
irisml_cache <list|download|remove|show> [--mtime <+|->N] [--name NAME]

Pipeline definition

PipelineDefinition = {"tasks": List[TaskDefinition], "on_error": Optional[List[TaskDescription]]}

TaskDefinition = {
    "task": <task module name>,
    "name": <optional unique name of the task>,
    "inputs": <list of input objects>,
    "config": <config for the task. Use irisml_show command to find the available configurations.>
}

In the TaskDefinition.inputs and TaskDefinition.config, you cna use the following two variable.

• $env.<variable_name> This variable will be replaced by the environment variable that was provided as arguments for irisml_run command.
• $outputs.<task_name>.<field_name> This variable will be replaced by the outputs of the specified previous task.

It raises an exception on runtime if the specified variable was not found.

If a task raised an exception, the tasks specified in on_error field will be executed. The exception object will be assigned to "$env.IRISML_EXCEPTION" variable.

Pipeline cache

Using cache, you can modify and re-run a pipeline config with minimum cost. If the cache is enabled, IrisML will calculate hash values for all task inputs/configs and upload the task outputs to a specified storage. When it found a task with same hash values, it can download the cache and skip the task execution.

To enable cache, you must specify the cache storage location by setting IRISML_CACHE_URL environment variable. Currently Azure Blob Storage and local filesystem is supported.

To use Azure Blob Storage, a container URL must be provided. It the URL contains a SAS token, it will be used for authentication. Otherwise, interactive authentication and Managed Identity authentication will be used.

Python API

To run a pipeline from python code, you can use the following APIs.

import json
import pathlib
from irisml.core import JobRunner

job_description = json.loads(pathlib.Path('example.json').read_text())
runner = JobRunner(job_description)

runner.run({'DATASET_NAME': 'mnist'})

runner.run({'DATASET_NAME': 'cifar10'})

Available official tasks

To show the detailed help for each task, run the following command after installing the package.

irisml_show <task_name>

irisml-tasks

Task	Description
assertion	Assert the given input.
assign_class_to_strings	Assigns a class to a string based on the class name being present in the string.
branch	'If' conditional branch.
calculate_cosine_similarity	Calculate cosine similarity between two sets of vectors.
check_model_parameters	Check Inf/NaN values in model parameters.
compare	Compare two values
deserialize_tensor	Deserialize a pytorch tensor.
divide_float	Floating point division.
download_azure_blob	Download a single blob from Azure Blob Storage.
extract_image_bytes_from_dataset	Extract images from a dataset and convert them to bytes.
get_current_time	Get the current time in seconds since the epoch
get_dataset_split	Get a train/val split of a dataset.
get_dataset_stats	Get statistics of a dataset.
get_dataset_subset	Get a subset of a dataset.
get_fake_image_classification_dataset	Generate a fake image classification dataset.
get_fake_object_detection_dataset	Generate a fake object detection dataset.
get_int_from_json_strings	Get an integer from a JSON string.
get_item	Get an item from the given list.
get_kfold_cross_validation_dataset	Get train/test dataset for k-fold cross validation.
get_secret_from_azure_keyvault	Get a secret from Azure KeyVault.
get_topk	Get the largest Topk values and indices.
join_filepath	Join a given dir_path and a filename.
load_state_dict	Load a state_dict from various sources.
make_cached_dataset	Save dataset cache on disk.
make_prompt_for_each_string	Make a prompt for each string.
make_prompt_with_strings	Make a prompt with a list of strings.
pickling_object	Pickling an object.
print	Print or Pretty Print the input object.
print_environment_info	Print various environment information to stdout/stderr.
read_file	Reads a file and returns its contents as bytes.
repeat_tasks	Repeat the given tasks for multiple times.
run_parallel	Run the given tasks in parallel. A new process will be forked for each task. Each task must have an unique name.
run_profiler	Run profiler on the given tasks.
run_sequential	Run the given tasks in sequence. Each task must have an unique name.
save_file	Save the given input binary to a file.
save_images_from_dataset	Save images from a dataset to disk.
save_state_dict	Save the model's state_dict to the specified file.
search_grid_sequential	Grid search hyperparameters. Tasks are run in sequence.
serialize_tensor	Serialize a pytorch tensor.
switch_pick	pick from vals based on conditions. Task will return the first val with condition being True.
upload_azure_blob	Upload a binary file to Azure Storage Blob.

irisml-tasks-training

This package contains tasks related to pytorch training

Task	Description
append_classifier	Append a classifier model to a given model. A predictor and a loss module will be added, too.
benchmark_dataset	Benchmark dataset loading and preprocessing
benchmark_model	Benchmark a given model using a given dataset.
benchmark_model_with_grad_cache	Benchmark a given model using a given dataset with grad caching. Useful for cases which require sub batching.
build_classification_prompt_dataset	Create a classification prompt dataset.
build_zero_shot_classifier	Create a zero-shot classification layer.
concatenate_datasets	Concatenate the given two datasets together.
create_classification_prompt_generator	Create a prompt generator for a classification task.
evaluate_accuracy	Calculate accuracy of the given prediction results.
evaluate_detection_average_precision	Calculate mean average precision for object detection task results.
exclude_negative_samples_from_classification_dataset	Exclude negative samples from classification dataset.
export_onnx	Export the given model as ONNX.
get_targets_from_dataset	Extract only targets from a given Dataset.
load_simple_classification_dataset	Load a simple classification dataset from a directory of images and an index file.
make_classification_dataset_from_object_detection	Convert an object detection dataset into a classification dataset.
make_classification_dataset_from_predictions	Make a classification dataset from predictions.
make_feature_extractor_model	Make a wrapper model to extract a feature vector from a vision model.
make_fixed_prompt_image_transform	Make a transform function for image and a fixed prompt.
make_image_text_contrastive_model	Make a model for image-text contrastive training.
make_image_text_transform	Make a transform function for image-text classification.
make_oversampled_dataset	Make an oversampled dataset.
num_iters_to_epochs	Convert number of iterations to number of epochs. Min value is 1.
predict	Predict using a given model.
remove_empty_images_from_dataset	Remove empty images from dataset.
sample_few_shot_dataset	Few-shot sampling of a IC/OD dataset.
split_image_text_model	Split a image-text model into an image model and a text model.
train	Train a pytorch model.
train_with_gradient_cache	Train a model using gradient cache. Useful for contrastive learning with a large model.

irisml-tasks-torchvision

Adapter tasks for torchvision library.

Task	Description
create_torchvision_model	Create a torchvision model.
create_torchvision_transform	Create transform objects in torchvision library.
load_torchvision_dataset	Load a dataset from torchvision package.

irisml-tasks-transformers

Adapter tasks for HuggingFace transformers library.

Task	Description
create_transformers_model	Create a model using the transformers library.
create_transformers_tokenizer	Create a tokenizer using the transformers library.

irisml-tasks-timm

Adapter for models in timm library.

Task	Description
create_timm_model	Create a model using the timm library.
create_timm_transform	Create a preprocessing function using the timm library.

irisml-tasks-onnx

Adapter tasks for OnnxRuntime library.

Task	Description
predict_onnx	Run inference for an ONNX model.

irisml-tasks-azureml

Task	Description
run_azureml_child	Run tasks as a new child AzureML Run.
add_aml_tag	Tag the AML Run with a string key and optional value.

irisml-tasks-fiftyone

Task	Description
launch_fiftyone	Launch a fiftyone interface.

Development

Create a new task

To create a Task, you must define a module that contains a "Task" class. Here is a simple example:

# irisml/tasks/my_custom_task.py
import dataclasses
import irisml.core

class Task(irisml.core.TaskBase):  # The class name must be "Task".
  VERSION = '1.0.0'
  CACHE_ENABLED = True  # (default: True) This is optional.

  @dataclasses.dataclass
  class Inputs:  # You can remove this class if the task doesn't require inputs.
    int_value: int
    float_value: float

  @dataclasses.dataclass
  class Config:  # If there is no configuration, you can remove this class. All fields must be JSON-serializable.
    another_float: float
    child_dataclass: dataclass  # If you'd like to define a nested config, you can define another dataclass.

  @dataclasses.dataclass
  class Outputs:  # Can be removed if the task doesn't have outputs.
    float_value: float = 0  # If dry_run() is not implemented, Outputs fields must have default value or default factory.

  def execute(self, inputs: Inputs) -> Outputs:
    return self.Outputs(inputs.int_value * inputs.float_value * self.config.another_float)

  def dry_run(self, inputs: Inputs) -> Outputs:  # This method is optional.
    return self.Outputs(0)  # Must return immediately without actual processing.

Each Task must define "execute" method. The base class has empty implementation for Inputs, Config, Outputs and dry_run(). For the detail, please see the document for TaskBase class.

Related repositories

• irisml-tasks
• irisml-tasks-training
• irisml-tasks-torchvision
• irisml-tasks-transformers
• irisml-tasks-timm
• irisml-tasks-azureml
• irisml-tasks-fiftyone