Why is Docker build context so large?
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| Papers (10) | Insight |
|---|---|
01 Aug 2017 23 Citations | Docker container is experiencing a rapid development with the support from industry and being widely used in large scale production cloud environment, due to the benefits of speedy launching time and tiny memory footprint. |
141 Citations | The provided modularity, combined with the orchestration supplied by Docker, simplifies management and enables distributed deployments, creating a highly dynamic system. |
| A particular strength of Docker is its simple format for describing and managing software containers, which has benefits for software developers, system administrators and end users. | |
01 Jul 2018 35 Citations | By defining the specific Docker image architecture and building orders, dockerfile plays an important role in the Docker-based containerization process. |
Open access•Posted Content | Docker offers an ecosystem that offers a platform for application packaging, distributing and managing within containers. |
12 Oct 2017 157 Citations | We identify an important performance problem during Docker container migration. |
30 Oct 2017 | This Docker container-based system is an inexpensive and user-friendly framework for everyone who has the knowledge of basic IT skills. |
08 May 2017 | verify the superiority of the Docker technology. |
22 Mar 2017 75 Citations | The modularity provided by the proposed architecture combined with lightweight virtualization orchestration supplied by Docker simplifies management and enables distributed deployments. |
20 Apr 2016 18 Citations | Docker is a light weight alternative to the usual virtualization technology achieving notable better performance. |
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What are the potential benefits and challenges of increasing the context size of language models?4 answersIncreasing the context size of language models (LMs) can significantly enhance their ability to understand and generate more coherent and contextually relevant text, as it allows for a broader understanding of the input, leading to improvements in tasks that require long-context understanding such as multi-document question answering and key-value retrieval. This is particularly beneficial for complex tasks that necessitate a deep understanding of the context, including language modeling, passkey retrieval, and long document summarization. However, the challenge lies in the models' ability to effectively utilize the longer context. Studies have found that performance often degrades when relevant information is located in the middle of long contexts and that there is a substantial decrease in performance as the input context grows longer, even for models explicitly designed for long contexts. Moreover, extending the context window sizes requires careful consideration of the model's architecture and training process. For instance, Position Interpolation (PI) has been proposed as a method to extend the context window sizes of RoPE-based pretrained LLMs with minimal fine-tuning, demonstrating strong empirical results on various tasks while preserving quality on tasks within its original context window. However, this approach requires linear down-scaling of input position indices to match the original context window size, avoiding the potential pitfalls of extrapolation that could ruin the self-attention mechanism. In summary, while increasing the context size of LMs offers potential benefits in enhancing their understanding and generation capabilities, it also presents challenges in model performance and requires innovative solutions to effectively leverage the extended context.
What are some techniques for increasing the context size of language models in natural language processing tasks?10 answersTo increase the context size of language models in natural language processing tasks, several techniques have been proposed in recent research. One approach is Position Interpolation (PI), which extends the context window sizes of RoPE-based pretrained LLMs up to 32768 tokens with minimal fine-tuning, demonstrating strong empirical results on various tasks. Another technique is the use of AutoCompressors, which compress long contexts into summary vectors that can be used as soft prompts, allowing for the utilization of long contexts to improve perplexity and accuracy in tasks requiring long context. Additionally, the framework LongMem enables LLMs to memorize long history by decoupling the network architecture into a memory encoder and retriever, allowing for the caching and updating of long-term past contexts for memory retrieval without memory staleness. Parallel Context Windows (PCW) is another method that carves long contexts into chunks or windows, restricting the attention mechanism within each window and re-using positional embeddings across windows to alleviate the context window restriction for off-the-shelf LLMs without further training, showing substantial improvements for tasks with diverse input and output spaces. Lastly, In-Context Retrieval-Augmented Language Modeling (RALM) involves prepending grounding documents to the input of an unchanged LM, utilizing off-the-shelf general purpose retrievers to provide significant LM gains across model sizes and diverse corpora.
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