Skip to main content

How to Install telnet on Linux CLI instance

# yum install telnet
Loaded plugins: priorities, update-motd, upgrade-helper
amzn-main/latest                                                                                                                                          | 2.1 kB     00:00
amzn-updates/latest                                                                                                                                       | 2.3 kB     00:00
Resolving Dependencies
--> Running transaction check
---> Package telnet.x86_64 1:0.17-47.7.amzn1 will be installed
--> Finished Dependency Resolution

Dependencies Resolved

=================================================================================================================================================================================
 Package                                Arch                                   Version                                           Repository                                 Size
=================================================================================================================================================================================
Installing:
 telnet                                 x86_64                                 1:0.17-47.7.amzn1                                 amzn-main                                  63 k

Transaction Summary
=================================================================================================================================================================================
Install  1 Package

Total download size: 63 k
Installed size: 109 k
Is this ok [y/d/N]: y
Downloading packages:
telnet-0.17-47.7.amzn1.x86_64.rpm                                                                                                                         |  63 kB     00:00
Running transaction check
Running transaction test
Transaction test succeeded
Running transaction
  Installing : 1:telnet-0.17-47.7.amzn1.x86_64                                                                                                                               1/1
  Verifying  : 1:telnet-0.17-47.7.amzn1.x86_64                                                                                                                               1/1

Installed:
  telnet.x86_64 1:0.17-47.7.amzn1

Complete!

Labels

Labels:

Comments

Post a Comment

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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.
Post a Comment
Read more