1. Problems Facing Distributed Training Estimating training resources is difficult and cannot be automated How much compute, how much time, how much bandwidth, how many CPUs, how much memory — without enough accumulated experience it is hard to estimate accurately. The result is over-requesting and over-allocation, causing enormous resource waste.

1. Data Parallelism Training steps: The master device loads the model and copies the model parameters to every worker device The master device splits the training data along the batch dimension and passes each batch to every worker device Each worker device performs training The master device aggregates the gradients from each worker device and updates the model parameters The master device broadcasts the model parameters to every worker device, preparing for the next batch of training Core idea:

1. Interview Process Establish hiring criteria Align on hiring criteria Resume analysis Opening small talk Competency assessment Values assessment Verifying authenticity Motivation assessment Attracting strong candidates Closing the interview Filling out the interview evaluation form Assisting with onboarding follow-up 2. Before the Interview 2.1 Establish Hiring Criteria why Evaluate candidates with a unified assessment standard

Subtitle: New adult-training methods that make the learning process active and enjoyable Author: Harold D. Stolovitch, Erica J. Keeps (US) Publisher: Enterprise Management Publishing House Publication year: 2012-1 ISBN: 9787802558861 Note: It mainly discusses how interactive training can achieve very good training results, analyzes the training and learning process and psychology, and proposes four training methods along with some suggestions.