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# NeMo-Run
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# NeMoRun
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> [!IMPORTANT]
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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.
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> NeMoRun 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.
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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:
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NeMoRun is a powerful tool designed to streamline the configuration, execution, and management of machine learning experiments across various computing environments. NeMoRun has three core responsibilities:
Make sure you have `pip` installed and configured properly.
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## Get Started
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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.
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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.
In building NeMo-Run, we drew inspiration from and relied on the following primary libraries. We would like to extend our gratitude for their work.
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In building NeMoRun, we drew inspiration from and relied on the following primary libraries. We would like to extend our gratitude for their work.
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-[Fiddle](https://github.com/google/fiddle)
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-[TorchX](https://github.com/pytorch/torchx/)
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Apart from these, we also build on other libraries. A full list of dependencies can be found in [pyproject.toml](pyproject.toml).
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NeMo-Run was designed keeping the following principles in mind:
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NeMoRun was designed keeping the following principles in mind:
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### Pythonic
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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.
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In NeMoRun, 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.
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### Modular
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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.
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### Opinionated but Flexible
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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.
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NeMoRun 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.
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### Set Up Once and Scale Easily
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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.
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While it may take some time initially for users to become familiar with NeMoRun concepts, the tool is designed to scale experimentation in a fluid and easy manner.
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## Tutorials
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#### Hello world
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The `hello_world` tutorial series provides a comprehensive introduction to NeMo-Run, demonstrating its capabilities through a simple example. The tutorial covers:
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The `hello_world` tutorial series provides a comprehensive introduction to NeMoRun, demonstrating its capabilities through a simple example. The tutorial covers:
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- Configuring Python functions using `Partial` and `Config` classes.
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- Executing configured functions locally and on remote clusters.
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