Experience the Magic of Mosaic ML LLM Cloud

Experience the Magic of Mosaic ML LLM Cloud

Table of Contents

1. Introduction
2. Challenges of Training Large Language Models
3. Introducing Mosaic ML Cloud
4. Running and Monitoring ML Training Jobs
5. Scaling Training Across Multiple GPUs and Nodes
6. Orchestrating Cloud-Native Data Streaming
7. Achieving Algorithmic and System Efficiency
8. Using MCLI: Mosaic ML Command Line Tool
9. Managing Clusters and Submitting Jobs
10. Launching a Training Job
11. Scaling up to 16 GPUs
12. Training with Larger Models
13. Recap of the Demo
14. Conclusion

Introduction

Training large language models is a complex and challenging task. With the increasing size and volume of data required, building a high-performance clustered solution becomes even more difficult. However, with the Mosaic ML Cloud and the LLN stack, these challenges are taken care of, making training large language models easy and efficient.

Challenges of Training Large Language Models

Building a high-performance clustered solution for training large language models comes with several engineering challenges. These challenges include handling the size of the models and the volume of data, orchestrating cloud-native data streaming, achieving algorithmic and system efficiency, and ensuring distributed training simplicity.

Introducing Mosaic ML Cloud

Mosaic ML Cloud is a comprehensive solution designed to simplify the training of large language models. It provides a fully managed environment, taking care of all the infrastructure configuration and orchestration required for efficient training. With Mosaic ML Cloud, users can focus on their models and data, leaving the technical complexities to the platform.

Running and Monitoring ML Training Jobs

Mosaic ML Cloud makes it easy to run and monitor ML training jobs. The platform offers a seamless experience for launching jobs and provides tools for real-time monitoring of the training progress. Users can easily track metrics, log experiments, and Visualize results using popular logging and experiment tracking tools such as Weights and Biases or Comet.

Scaling Training Across Multiple GPUs and Nodes

One of the key features of Mosaic ML Cloud is its ability to Scale training across multiple GPUs and nodes. With a simple configuration change, users can easily scale their training jobs to leverage more capacity. The platform takes care of the infrastructure configuration and distributed training settings, allowing users to focus on their models and achieve faster training times.

Orchestrating Cloud-Native Data Streaming

Mosaic ML Cloud provides efficient and seamless data streaming capabilities. The platform handles all the settings needed for streaming data from cloud storage, ensuring optimal performance and throughput. Users can easily configure data parallelism using popular frameworks like PyTorch FSDP, allowing multiple processes to work in parallel and process billions of tokens efficiently.

Achieving Algorithmic and System Efficiency

Mosaic ML Cloud is built to ensure algorithmic and system efficiency during training. The platform optimizes the system configuration to make use of the available GPU resources effectively. It right-sizes the workload to fit into the GPU memory, allowing for efficient processing of large language models. With Mosaic ML Cloud, users can achieve high levels of efficiency without worrying about the technicalities.

Using MCLI: Mosaic ML Command Line Tool

MCLI is a command-line tool provided by Mosaic ML Cloud for managing clusters and submitting jobs. With MCLI, users can easily interact with the platform, view available clusters, submit jobs, and perform various administrative tasks. The tool provides a simple and intuitive interface, making it convenient for users to manage their training workflows.

Managing Clusters and Submitting Jobs

Using MCLI, users can easily view the available clusters hosted by multiple cloud providers. They can see the GPU types and the number of GPUs available for each training job. MCLI also allows users to view previous training runs, providing insights into past experiments and results. Additionally, users can manage authentication purposes by accessing and modifying the secrets applied, such as SSH keys or API keys.

Launching a Training Job

To launch a training job using Mosaic ML Cloud, users need a YAML file that instructs the job scheduler on what to run and where. By running the MCLI command with the YAML file, the platform takes care of various tasks behind the scenes. This includes pulling the container image with pre-configured drivers and libraries, cloning the git repository with the training code, and connecting to logging and experiment tracking tools.

Scaling up to 16 GPUs

Scaling up a training job to leverage more capacity is effortless with Mosaic ML Cloud. By simply deleting the original job and changing the configuration to specify 16 GPUs, the platform automatically spreads the job across two separate nodes, each with eight GPUs. This infrastructure configuration is handled seamlessly by Mosaic ML Cloud, eliminating the need for users to worry about complex node configuration or distributed training settings.

Training with Larger Models

Mosaic ML Cloud supports training with larger models, including configurations up to 70 billion parameters. Users can leverage pre-published model configurations in the examples repository and modify them as needed. Scaling up training to 30 billion parameter models on 128 GPUs is as simple as launching the 1 billion parameter model job. Mosaic ML Cloud ensures ease of use and efficient throughput for training models of any size.

Recap of the Demo

In the demo, we showcased the ease of launching and managing training jobs with Mosaic ML Cloud. Starting with a 1 billion parameter GPT model on 8 GPUs, we demonstrated how simple it is to scale up the job to 16 GPUs across two nodes. We also highlighted the seamless process of launching a 30 billion parameter GPT model job on 128 GPUs. Mosaic ML Cloud provides effortless scaling and blazingly fast training, making it an ideal platform for training large language models.

Conclusion

With Mosaic ML Cloud, training large language models becomes simple and magical. The platform handles the complexities of infrastructure configuration, distributed training settings, and algorithmic efficiency, allowing users to focus on their models and data. Whether it's scaling up to multiple GPUs and nodes or training with larger models, Mosaic ML Cloud provides the ease of use and performance required for successful training.

Highlights:

• Mosaic ML Cloud simplifies training large language models by taking care of infrastructure configuration and orchestration.
• The platform allows for easy launching and monitoring of ML training jobs, with real-time tracking of metrics and visualization of results.
• Scaling training across multiple GPUs and nodes is effortless, with Mosaic ML Cloud handling the technical complexities.
• The platform ensures efficient cloud-native data streaming and system performance, optimizing training speed and throughput.
• MCLI, the command-line tool provided by Mosaic ML Cloud, allows for easy management of clusters and job submissions.
• Mosaic ML Cloud supports training with larger models, providing pre-configured model configurations and effortless scaling options.

FAQ

Q: Can Mosaic ML Cloud handle training jobs with custom model configurations? A: Yes, Mosaic ML Cloud supports custom model configurations. Users can leverage pre-published configurations or modify them as needed.

Q: How does Mosaic ML Cloud handle infrastructure configuration? A: Mosaic ML Cloud takes care of infrastructure configuration automatically, pulling container images with pre-configured drivers and libraries.

Q: Can Mosaic ML Cloud scale training jobs to more than 128 GPUs? A: Yes, Mosaic ML Cloud can scale training jobs to larger numbers of GPUs, ensuring efficient throughput and performance.

Q: Does Mosaic ML Cloud support data parallelism? A: Yes, Mosaic ML Cloud supports data parallelism using popular frameworks like PyTorch FSDP, allowing for efficient processing of large language models.

Q: Can I track and monitor the progress of my training jobs in real-time? A: Yes, Mosaic ML Cloud provides tools for real-time monitoring, including metrics tracking and visualization of training progress.

Q: Can I use my own experiment tracking tool with Mosaic ML Cloud? A: Yes, Mosaic ML Cloud supports popular experiment tracking tools like Weights and Biases or Comet, allowing users to integrate their preferred tools seamlessly.

Resources:

• Mosaic ML Cloud
• Weights and Biases
• Comet