![]() ![]() DeepSpeed ConfigurationĭeepSpeed features can be enabled, disabled, or configured using a config JSONįile that should be specified as epspeed_config. Waiting to synchronize with other processes if it’s called just for the process with rank 0. It is becauseĮach process needs to save its master weights and scheduler+optimizer states. Important: all processes must call this method and not just the process with rank 0. The step value is stored as part of the client_sd. Retrieved from load_checkpoint as a return argument. To support these items, save_checkpointĪccepts a client state dictionary client_sd for saving. However, the user may want to save additional data that are Learning rate scheduler states while hiding away these details from the user. save_dir, ckpt_id, client_sd = client_sd )ĭeepSpeed can automatically save and restore the model, optimizer, and the save_interval : client_sd = step ckpt_id = loss. ckpt_id ) step = client_sd #advance data loader to ckpt stepĭataloader_to_step ( data_loader, step + 1 ) for step, batch in enumerate ( data_loader ): #forward() method ![]() In the following code snippet, we use the loss value as the checkpoint identifier. ckpt_id: an identifier that uniquely identifies a checkpoint in the directory.ckpt_dir: the directory where checkpoints will be saved.Load_checkpoint API in DeepSpeed which takes two arguments to uniquely Saving and loading the training state is handled via the save_checkpoint and if the schedule is supposed to execute at any other interval (e.g., training epochs), then the user should NOT pass the scheduler to DeepSpeed during initialization and must manage it explicitly.if the schedule is supposed to execute at every training step, then the user can pass the scheduler to deepspeed.initialize when initializing the DeepSpeed engine and let DeepSpeed manage it for update or save/restore.When not using DeepSpeed’s learning rate scheduler: Learning Rate Scheduler: when using a DeepSpeed’s learning rate scheduler (specified in the ds_config.json file), DeepSpeed calls the step() method of the scheduler at every training step (when model_engine.step() is executed). Loss Scaling: in FP16/mixed precision training, the DeepSpeedĮngine automatically handles scaling the loss to avoid precision loss in the Gradient Averaging: in distributed data parallel training, backwardĮnsures that gradients are averaged across data parallel processes after Required for distributed data parallel training, in mixed precision, with a Under the hood, DeepSpeed automatically performs the necessary operations Loss = model_engine ( batch ) #runs backpropagation Please see the tutorials for detailedįor step, batch in enumerate ( data_loader ): #forward() method The engineĬan wrap any arbitrary model of type torch.nn.module and has a minimal set of APIsįor training and checkpointing the model. DeepSpeed on AMD can be used via our ROCm images, e.g., docker pull deepspeed/rocm501:ds060_pytorch110.ĭeepSpeed model training is accomplished using the DeepSpeed engine.PyTorch Lightning provides easy access to DeepSpeed through the Lightning Trainer See more details. HuggingFace Transformers users can now easily accelerate their models with DeepSpeed through a simple -deepspeed flag + config file See more details. DeepSpeed has direct integrations with HuggingFace Transformers and PyTorch Lightning.To get started with DeepSpeed on AzureML, please see the AzureML Examples GitHub.Installing is as simple as pip install deepspeed, see more details. ![]()
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