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NeMo Run is still in active development and this is a pre-release. The API is subject to change without notice while in pre-release. First official release will be 0.1.0 and will be included in NeMo FW 24.09 as well.
NeMo Run is a powerful tool designed to streamline the configuration, execution, and management of machine learning experiments across various computing environments. NeMo Run has three core responsibilities:
Make sure you have pip installed and configured properly.
Get Started
To get started with NeMo Run, follow these three steps based on the core responsibilities mentioned above. For this example, we’ll showcase a pre-training example in Nemo 2.0 using Llama3.
Apart from these, we also build on other libraries. A full list of dependencies can be found in pyproject.toml.
NeMo Run was designed keeping the following principles in mind:
Pythonic
In NeMo Run, you can build and configure everything using Python, eliminating the need for multiple combinations of tools to manage your experiments. The only exception is when setting up the environment for remote execution, where we rely on Docker.
Modular
The decoupling of task and executor allows you to form different combinations of execution units with relative ease. You configure different remote environments once, and you can reuse it across a variety of tasks in a Pythonic way.
Opinionated but Flexible
NeMo Run is opinionated in some places, like storing of metadata information for experiments in a particular manner. However, it remains flexible enough to accommodate most user experiments.
Set Up Once and Scale Easily
While it may take some time initially for users to become familiar with NeMo Run concepts, the tool is designed to scale experimentation in a fluid and easy manner.
Tutorials
Hello world
The hello_world tutorial series provides a comprehensive introduction to NeMo Run, demonstrating its capabilities through a simple example. The tutorial covers:
Configuring Python functions using Partial and Config classes.
Executing configured functions locally and on remote clusters.
Visualizing configurations with graphviz.
Creating and managing experiments using run.Experiment.