You are training a custom language model for your company using a large dataset. You plan to use the Reduction Server strategy on Vertex AI. You need to configure the worker pools of the distributed training job. What should you do?
A.
Configure the machines of the first two worker pools to have GPUs, and to use a container image where your training code runs. Configure the third worker pool to have GPUs, and use the reductionserver container image.
B.
Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
C.
Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs. Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
D.
Configure the machines of the first two pools to have TPUs, and to use a container image where your training code runs. Configure the third pool to have TPUs, and use the reductionserver container image.
Reduction server strategy:
1. Only GPUs
2. You do not use GPUs for the Reduction Server nodes.
https://cloud.google.com/vertex-ai/docs/training/distributed-training
B is the right answer because:
- Reduction Server strategy is generally implemented with GPUs, not TPUs.
- First 2 pools' replicas perform model training; need GPUs for faster processing
- Container image should contain your custom training code.
- 3rd pool contains reduction server, no GPU is needed here; prioritize network bandwidth instead!
GPUs for Training: Configure the first two worker pools with GPUs to leverage the hardware acceleration capabilities for your custom language model training code.
Reduction Server without GPUs: The third worker pool should use the reductionserver container image. This image is pre-configured for Reduction Server functionality and doesn't require GPUs.
High-Bandwidth CPU: Choose a machine type with high bandwidth for the third pool since Reduction Server focuses on communication and gradient reduction.
A. GPUs for Reduction Server: Reduction Server itself doesn't require or benefit from GPUs. It focuses on communication and reduction of gradients. It's better to use a CPU-based machine type for the third pool.
C. TPUs instead of GPUs: While TPUs can be used for training some language models, Reduction Server specifically works with GPUs using the NCCL library. Configure your first two pools with GPUs for your training code.
D. TPUs in Reduction Server pool: Similar to option A, Reduction Server doesn't benefit from TPUs. It's best to use a CPU with high bandwidth for the third pool.
https://cloud.google.com/blog/topics/developers-practitioners/optimize-training-performance-reduction-server-vertex-ai
In this article, we introduce Reduction Server, a new Vertex AI feature that optimizes bandwidth and latency of multi-node distributed training on NVIDIA GPUs for synchronous data parallel algorithms.
Worker Pools 1 and 2:
These pools are responsible for the actual model training tasks.
They require GPUs (or TPUs, if applicable to your model) to accelerate model computations.
They run the container image containing your training code.
Worker Pool 3:
This pool is dedicated to the reduction server.
It doesn't require accelerators (GPUs or TPUs) for gradient aggregation.
Prioritize machines with high network bandwidth to optimize gradient exchange.
Use the specific reductionserver
A voting comment increases the vote count for the chosen answer by one.
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one.
So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
Pau1234
4 months, 2 weeks agolunalongo
4 months, 3 weeks agowences
7 months, 1 week agofitri001
1 year agofitri001
1 year agopinimichele01
1 year agoshadz10
1 year, 3 months agowinston9
1 year, 3 months agopikachu007
1 year, 3 months ago