Maggy is a framework for distribution transparent machine learning experiments on Apache Spark. In this post, we introduce a new unified framework for writing core ML training logic as oblivious training functions. Maggy enables you to reuse the same training code whether training small models on your laptop or reusing the same code to scale out hyperparameter tuning or distributed deep learning on a cluster. Maggy enables the replacement of the current waterfall development process for distributed ML applications, where code is rewritten at every stage to account for the different distribution context.
Maggy uses PySpark as an engine to distribute the training processes. To get started, install Maggy in the Python environment used by your Spark Cluster, or install Maggy in your local Python environment with the
'spark' extra, to run on Spark in local mode:
pip install maggy
The programming model consists of wrapping the code containing the model training inside a function. Inside that wrapper function provide all imports and parts that make up your experiment.
Single run experiment:
```python def train_fn(): # This is your training iteration loop for i in range(number_iterations): ... # add the maggy reporter to report the metric to be optimized reporter.broadcast(metric=accuracy) ... # Return metric to be optimized or any metric to be logged return accuracy
from maggy import experiment result = experiment.lagom(train_fn=train_fn, name='MNIST') ```
lagom is a Swedish word meaning "just the right amount". This is how MAggy uses your resources.
Full documentation is available at maggy.ai
There are various ways to contribute, and any contribution is welcome, please follow the CONTRIBUTING guide to get started.
Issues can be reported on the official GitHub repo of Maggy.
Please see our publications on maggy.ai to find out how to cite our work.
TypeError Traceback (most recent call last)
TypeError: lagom() got an unexpected keyword argument 'searchspace'
pip install the latest version of maggy(version1.1.0), and run a simple maggy example and it is not working. import maggy from maggy import experiment ... result = experiment.lagom(train_fn=training_fn, name='MNIST')
returns AttributeError: module 'maggy.experiment' has no attribute 'lagom'
I have can install maggy on my PySpark cluster from pip but whenever I issue this command
from maggy.experiment_config import OptimizationConfig I get the error
ModuleNotFoundError: No module named 'maggy.experiment_config'. Any idea of what could be happening. I am using JupiterLab with Python3 Kenrnel
from maggy import experiment says no module named
The imports that I have seen working are from
maggy.ablation import AblationStudy and
from maggy import Searchspace
Edit: I noticed that I cannot use pre-release version (if it has a fix of this that is). I get this error when I try to install the pre-release version ERROR: Could not find a version that satisfies the requirement maggy==1.0.0rc0 (from versions: 0.0.1, 0.1, 0.1.1, 0.2, 0.2.1, 0.2.2, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.4.0, 0.4.1, 0.4.2, 0.5.0, 0.5.1, 0.5.2, 0.5.3) ERROR: No matching distribution found for maggy==1.0.0rc0
As per https://github.com/logicalclocks/maggy/blob/master/maggy/ablation/ablationstudy.py , we need to write a custom function for the dataset generator to make AblationStudy to work on databricks.
This is the first release candidate for the first major Maggy release 1.0.0.
This release contains many new features, which will be documented on maggy.ai.
These include: - Distribution transparency for distributed training, hyperparameter optimization and ablation studies. - Distributed training support for PyTorch, including DeepSpeed ZeRO - Distributed training support for TensorFlow, using MultiWorkerMirroredStrategy
This release changes the LICENSE to Apache V2.
Developers of HopsworksGitHub Repository Homepage
hyperparameter-optimization hyperparameter-search automl ablation spark hyperparameter-tuning blackbox-optimization ablation-studies ablation-study