Skip to main content

What is yum

Wiki definition

The Yellowdog Updater, Modified (yum) is an open-source command-line package-management utility for Linux operating systems using the RPM Package Manager.[2] Though yum has a command-line interface, several other tools provide graphical user interfaces to yum functionality.

Yum allows automatic updates, package and dependency management, on RPM-based distributions.[3] Like the Advanced Packaging Tool (APT) from Debian, yum works with software repositories (collections of packages), which can be accessed locally[4] or over a network connection.

Under the hood, yum depends on RPM, which is a packaging standard for digital distribution of software, which automatically uses hashes and digisigs to verify the authorship and integrity of said software; unlike some app stores, which serve a similar function, neither yum nor RPM provide built-in support for proprietary restrictions on copying of packages by endusers. Yum is implemented as libraries in the Python programming language, with a small set of programs that provide a command-line interface.[5] GUI-based wrappers such as Yum Extender (yumex) also exist.[6]

It can perform operations such as:

  1.     installing packages
  2.     deleting packages
  3.     updating existing installed packages
  4.     listing available packages
  5.     listing installed packages

1) Install a package:

   # yum install package

Example:

   # yum install httpd

2) Remove a package:

   # yum remove package

Example:

   # yum remove httpd

3) Update a package:

   # yum update package

Example:

   # yum update httpd

4) Search for a package:

   # yum search package

Example:

   # yum search httpd

5) Find information about a package:

   # yum info package

Example:

   # yum info httpd

6) List packages containing a certain term:

   # yum list term

Example:

   # yum list httpd

7) List available updates:

   # yum list updates

Example:

   # yum list updates

8) Find what package provides a particular file:

   # yum whatprovides 'path/filename'

Example:

   # yum whatprovides 'etc/httpd.conf'

   # yum whatprovides '*/libXp.so.6'

9) Update all installed packages with kernel package :

   # yum update

Example:

   # yum update

10) To update a specific package:

   # yum update

Example:

   # yum update openssh-server

11) To update a specific package and a specific version:

yum update-to packagename-ver-rel

Example:

   # yum update-to gcc-4.1.2-54.el5


Comments

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...

Training LLM model requires more GPU RAM than storing same LLM

Storing an LLM model and training the same model both require memory, but the memory requirements for training are typically higher than just storing the model. Let's dive into the details: Memory Requirement for Storing the Model: When you store an LLM model, you need to save the weights of the model parameters. Each parameter is typically represented by a 32-bit float (4 bytes). The memory requirement for storing the model weights is calculated by multiplying the number of parameters by 4 bytes. For example, if you have a model with 1 billion parameters, the memory requirement for storing the model weights alone would be 4 GB (4 bytes * 1 billion parameters). Memory Requirement for Training: During the training process, additional components use GPU memory in addition to the model weights. These components include optimizer states, gradients, activations, and temporary variables needed by the training process. These components can require additional memory beyond just storing th...

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.