Multiple advanced distributed training algorithms including: Bagua ¶īagua is a deep learning training acceleration framework which supports See the official Horovod documentation for details Horovodrun -np 8 -H hostname1:4,hostname2:4 python train.py # run training with 8 GPUs on two machines (4 GPUs each) # run training with 4 GPUs on a single machine Runs a forward and backward pass using DP. DDP2 does the following:Ĭopies a subset of the data to each node. In this case, we can use DDP2 which behaves like DP in a machine and DDP across nodes. In certain cases, it’s advantageous to use all batches on the same machine instead of a subset.įor instance, you might want to compute a NCE loss where it pays to have more negative samples. In these situations you should use dp or ddp_spawn instead. You have a nested script without a root package Jupyter Notebook, Google COLAB, Kaggle, etc. There are cases in which it is NOT possible to use DDP. spawn() trains the model in subprocesses, the model on the main process does not get updated.ĭataloader(num_workers=N), where N is large, bottlenecks training with DDP… ie: it will be VERY slow or won’t work at all. We use DDP this way because ddp_spawn has a few limitations (due to Python and PyTorch): MASTER_ADDR =localhost MASTER_PORT =random () WORLD_SIZE = 3 NODE_RANK = 2 LOCAL_RANK = 0 python my_file.py -gpus 3 -etc MASTER_ADDR =localhost MASTER_PORT =random () WORLD_SIZE = 3 NODE_RANK = 1 LOCAL_RANK = 0 python my_file.py -gpus 3 -etc # example for 3 GPUs DDP MASTER_ADDR =localhost MASTER_PORT =random () WORLD_SIZE = 3 NODE_RANK = 0 LOCAL_RANK = 0 python my_file.py -gpus 3 -etc Note if you use any built in metrics or custom metrics that use TorchMetrics, these do not need to be updated and are automatically handled for you. The sync_dist option can also be used in logging calls during the step methods, but be aware that this can lead to significant communication overhead and slow down your training. This ensures that each GPU worker has the same behaviour when tracking model checkpoints, which is important for later downstream tasks such as testing the best checkpoint across all workers. This is done by adding sync_dist=True to all self.log calls in the validation and test step. When running in distributed mode, we have to ensure that the validation and test step logging calls are synchronized across processes. Synchronize validation and test logging ¶ See replace_sampler_ddp for more information. eye ( 3 )) # you can now access self.sigma anywhere in your module Remove samplers ¶ĭistributedSampler is automatically handled by Lightning. Finetune Transformers Models with PyTorch LightningĬlass LitModel ( LightningModule ): def _init_ ( self ).PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.GPU and batched data augmentation with Kornia and PyTorch-Lightning.Tutorial 13: Self-Supervised Contrastive Learning with SimCLR.Tutorial 12: Meta-Learning - Learning to Learn.Tutorial 10: Autoregressive Image Modeling.Tutorial 9: Normalizing Flows for Image Modeling.Tutorial 7: Deep Energy-Based Generative Models.Tutorial 6: Basics of Graph Neural Networks.Tutorial 5: Transformers and Multi-Head Attention.Tutorial 4: Inception, ResNet and DenseNet.Tutorial 3: Initialization and Optimization.LightningLite - Stepping Stone to Lightning.
0 kommentar(er)
