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10 Best AI Code Generators

Unite.AI

Best Features: Predictive code generation: GitHub Copilot goes beyond simple auto-completion. Cody by Sourcegraph Cody is another AI-driven coding assistant, this one developed by Sourcegraph. The tool offers an impressive set of features that extend beyond the scope of code completion.

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AI code-generation software: What it is and how it works

IBM Journey to AI blog

Using generative artificial intelligence (AI) solutions to produce computer code helps streamline the software development process and makes it easier for developers of all skill levels to write code. It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code.

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Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning Blog

of Large Model Inference (LMI) Deep Learning Containers (DLCs). The complete notebook with detailed instructions is available in the GitHub repo. For the TensorRT-LLM container, we use auto. In January 2024, Amazon SageMaker launched a new version (0.26.0) It is returned with the last streamed sequence chunk.

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MIT Researchers Introduce LILO: A Neuro-Symbolic Framework for Learning Interpretable Libraries for Program Synthesis

Marktechpost

Software developers, however, are more interested in creating libraries that may be used to solve whole problem domains than they are in finishing the current work at hand. But DreamCoder’s search process is so computationally demanding that learning a single domain takes over two CPU months.

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Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint

AWS Machine Learning Blog

The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process. Hugging Face is a popular open source hub for machine learning (ML) models. Prerequisites Complete the following prerequisites: Create a SageMaker domain.

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Fine-tune Llama 2 using QLoRA and Deploy it on Amazon SageMaker with AWS Inferentia2

AWS Machine Learning Blog

We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. We then use a large model inference container powered by Deep Java Library (DJLServing) as our model serving solution. In this post, we use the Large Model Inference Container for Neuron.

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Announcing provisioned concurrency for Amazon SageMaker Serverless Inference

AWS Machine Learning Blog

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.