- #FINETUNE FILE SYSTEM PERFORMANCE HOW TO#
- #FINETUNE FILE SYSTEM PERFORMANCE SOFTWARE#
- #FINETUNE FILE SYSTEM PERFORMANCE PLUS#
While we can run training on the Compute instance running our Notebook, SageMaker has a specialized training service called SageMaker training. SageMaker Studio is an IDE that allows you to run Jupyter Notebooks on a variety of different Compute instances. But a better way to utilize your resources as a trainer computer vision models in Amazon SageMaker. We can train our model locally on a large instance, accelerated within video GPUs.
We want to be able to present a wide variety of different types of images to our model during training so that is better able to make accurate predictions on data that it hasn't seen when it's deployed. If we don't have that much data, we can use augmentation techniques described in the previous video to strengthen our dataset.
#FINETUNE FILE SYSTEM PERFORMANCE PLUS#
A good rule of thumb is you should aim to have 100 plus, if possible 1,000 plus instances per class. While fine-tuning a model requires substantially less data, we still want to make sure that the classes we are training on are represented well enough. Tools like NVIDIA transfer learning toolkit simplify this process for data scientists. This allows the object detection head of the model take advantage of the features the backbone has learned and training on millions of images. For object detection tasks, many architectures use an image classifier that has been pre-trained on a large dataset as their backbone. As training on large generic datasets like ImageNet, tends to be rather expensive and time-consuming. Transfer learning is commonly used to train many computer vision models. Under the hood, the new model uses the learned representations from the pre-trained model and typically retrains the top layers of the new model while keeping the lower layers frozen. This allows the new model to be accurate while using substantially less data. Transfer learning is the process of using the learned weights from a model and applying them to a different but related task. One concept that has become increasingly popular in modern machine learning is transfer learning. After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key Amazon SageMaker services applicable to computer vision and NLP ML tasks.
#FINETUNE FILE SYSTEM PERFORMANCE HOW TO#
You will also learn, hands-on, how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases. You will then learn how to prepare a dataset for model training, build a model, execute model training, and deploy and optimize the ML model. Then, you will get hands-on, by running a GPU powered Amazon SageMaker notebook instance.
In this course, you will first get an overview of Amazon SageMaker and NVIDIA GPUs.
#FINETUNE FILE SYSTEM PERFORMANCE SOFTWARE#
Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high performance GPU-optimized instances in the cloud for efficient model training and cost effective model inference hosting. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.
Machine learning (ML) projects can be complex, tedious, and time consuming.
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