Model's evaluation in DDP training is using only one GPU

Model's evaluation in DDP training is using only one GPU - Beginners

Hello! :smiley: I am training an HF model with torch DDP using the following command line: python -m torch.distributed.launch ...

Model evaluation after DDP training - distributed - PyTorch Forums

This is maybe a more general question, but I cannot find information about this anywhere. There are a lot of tutorials how to train your ...

Model not copied to multiple GPUs when using DDP (using trainer)

When I check the compute and memory usage on GPUs using nvidia-smi, I see that only one GPU is being used for compute. There is small memory ...

Distributed evaluation with DDP - PyTorch Forums

When scaling training from a single worker to multiple workers (say, multiple GPUs on the same machine), DDP provides abstractions so that I do ...

DDP - Worse performance with 2 GPUs compared to 1. · Issue #7233

I am running a model with multiple optimizers using DDP and automatic optimization. When I run it on two GPUs (with the same effective batch size), the model ...

Will I get a speed up by using distributed training (DDP) even if my ...

It seems like the primary purpose of DDP is for cases where the model + batch size is too big to fit on a single GPU. However, I'm curious about using it for ...

Turn off ddp_sharded during evaluation #8534 - GitHub

So is there anyway I can use ddp_sharded during training, but turn it off for evaluation only on a single GPU? ... model is a simple pytorch classifier using ...

A Comprehensive Tutorial to Pytorch DistributedDataParallel - Medium

Note that I only introduce DDP on one machine with multiple gpus, which is the most general case (Otherwise, we should use model parallel as ...

Running test calculations in DDP mode with multiple GPUs with ...

You need to synchronize metric and collect to rank==0 gpu to compute evaluation metric on entire dataset. torch.distributed.reduce : This method ...

Distributed Data Parallel Training on AMD GPU with ROCm

(DDP) enables model training across multiple GPUs or nodes by implementing data parallelism at the module level. In DDP training, multiple ...

Log distributed training experiments

The proceeding examples demonstrate how to track metrics with W&B using PyTorch DDP on two GPUs on a single machine. PyTorch DDP ( DistributedDataParallel in ...

PyTorch Distributed Evaluation - Lei Mao's Log Book

In my previous post “PyTorch Distributed Training”, we have discussed how to run PyTorch distributed training to accelerate model training, but ...

Multi-GPU Training in PyTorch with Code (Part 3): Distributed Data ...

DDP is more intrusive into your code than DP, so we need to modify multiple parts of the single-GPU example in Part 1. DDP initialization. Rank ...

Model Parallel GPU Training - Lightning AI

Unlike DistributedDataParallel (DDP) where the maximum trainable model size and batch size do not change with respect to the number of GPUs, memory-optimized ...

PyTorch DistributedDataParallel Example In Azure ML - Multi-Node ...

There is a number of steps that needs to be done to transform a single-process model training into a distributed training using ...

Distributed Training — RecBole 1.2.0 documentation

If you train your model on one node with multi gpus, you only need to specify the number of processes on the command line. In above example, you should run the ...

Evaluation — PyTorch Lightning 1.6.5 documentation

To run the test set after training completes, use this method. ... It is recommended to test with Trainer(devices=1) since distributed strategies such as DDP use ...

Distributed training with TensorFlow

It creates one replica per GPU device. Each variable in the model is mirrored across all the replicas. Together, these variables form a single ...

Distributed Training — lightly 1.5.13 documentation

Distributed training is done with DDP using Pytorch Lightning and the batch size is divided by the number of GPUs. For distributed training we also evaluate ...

Single Machine Multi-GPU Minibatch Graph Classification - DGL Docs

We follow the common practice to train with multiple GPUs and evaluate with a single GPU, thus only set use_ddp to True in the GraphDataLoader() for the ...