Zeus automatically tunes the batch size and GPU power limit of a recurring DNN training job.
Zeus can optimize the batch size of recurring jobs, i.e. training jobs that re-run multiple times over time. However, Zeus can still optimize the GPU power limit even if your jobs does not recur.
Zeus currently supports single GPU training and single node data parallel training. Support for distributed data parallel training will be added soon.
The GPU power limit can be profiled and optimized quickly for any training job.
After going through the prerequisites, integrate
ZeusDataLoader into your training script.
from zeus.run import ZeusDataLoader # The one instantiated with max_epochs becomes the train dataloader train_loader = ZeusDataLoader(train_set, batch_size=256, max_epochs=100) eval_loader = ZeusDataLoader(eval_set, batch_size=256) for epoch_number in train_loader.epochs(): for batch in train_loader: # Learn from batch for batch in eval_loader: # Evaluate on batch train_loader.report_metric(validation_metric)
Data parallel with multi-GPU on a single-node
Zeus assumes that exactly one process manages one GPU, and hence
one instance of
for each GPU.
Users can integrate Zeus into existing data parallel training scripts with five specific steps, which are noted below in the comments.
Please refer to our integration example with ImageNet for a complete example.
import torch import torch.distributed as dist import torchvision from zeus.run import ZeusDataLoader # Step 1: Initialize the default process group. # This should be done before instantiating `ZeusDataLoader`. dist.init_process_group( backend=args.dist_backend, init_method=args.dist_url, ) # Step 2: Create a model and wrap it with `DistributedDataParallel`. model = torchvision.models.resnet18() torch.cuda.set_device(local_rank) model.cuda(local_rank) # Zeus assumes that exactly one process manages one GPU. If you are doing data # parallel training, please use `DistributedDataParallel` for model replication # and specify the `device_ids` and `output_device` as below: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[local_rank], output_device=local_rank, ) # Step 3: Create instances of `DistributedSampler` to partition the dataset # across the GPUs. train_sampler = torch.utils.data.distributed.DistributedSampler(train_set) eval_sampler = torch.utils.data.distributed.DistributedSampler(eval_set) # Step 4: Instantiate `ZeusDataLoader`. # `distributed="dp"` tells `ZeusDataLoader` to operate in data parallel mode. # The one instantiated with `max_epochs` becomes the train dataloader. train_loader = ZeusDataLoader(train_set, batch_size=256, max_epochs=100, sampler=train_sampler, distributed="dp") eval_loader = ZeusDataLoader(eval_set, batch_size=256, sampler=eval_sampler, distributed="dp") # Step 5: Training loop. # Use the train dataloader's `epochs` generator to allow Zeus to early-stop # based on the training cost. Use `report_metric` to let Zeus know the current # validation metric. for epoch_number in train_loader.epochs(): for batch in train_loader: # Learn from batch for batch in eval_loader: # Evaluate on batch # Make sure you all-reduce the validation metric across all GPUs, # since Zeus expects the final validation metric. val_metric_tensor = torch.tensor([validation_metric], device="cuda") dist.all_reduce(val_metric_tensor, async_op=False) train_loader.report_metric(val_metric_tensor.item())
The following examples will help:
- Integrating Zeus with computer vision
- Integrating Zeus with NLP
We plan to integrate
ZeusMaster with an MLOps platform like KubeFlow.
Let us know about your preferences, use cases, and expectations by posting an issue!
The cost-optimal batch size is located across multiple job runs using a Multi-Armed Bandit algorithm.
First, go through the steps for non-recurring jobs.
ZeusDataLoader will transparently optimize the GPU power limit for any given batch size.
Then, you can use
ZeusMaster to drive recurring jobs and batch size optimization.
This example will come in handy: