DLRover: An Automatic Distributed Deep Learning System

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DLRover makes the distributed training of large AI models easy, stable, fast and green. It can automatically train the Deep Learning model on the distributed cluster. It helps model developers to focus on model architecture, without taking care of any engineering stuff, say, hardware acceleration, distributed running, etc. Now, it provides automated operation and maintenance for deep learning training jobs on K8s/Ray. Major features as - **Full-Scene**: Support deep learning full-scene distributed training computation implementation. - **Fault-Tolerance**: The distributed training can continue running in the event of failures. - **Auto-Scaling**: The distributed training can scale up/down resources to improve the stability, throughput and resource utilization. - **Others**: - **XPU Timer Integration**: With runtime xpu-timer integration, can possess stronger runtime diagnostics and fault tolerance capabilities. - **Flash Checkpoint**: The distributed training can recover failures from the in-memory checkpoint in seconds. Furthermore, DLRover offers extension libraries for PyTorch and TensorFlow to expedite training. These are also open-source projects available in our [GitHub repositories](https://github.com/intelligent-machine-learning). - [ATorch](https://github.com/intelligent-machine-learning/atorch): an extension library of PyTorch to Speed Up Training of Large LLM. - [TFPlus](https://github.com/intelligent-machine-learning/tfplus)(K8S platform only): an extension library of TensorFlow to Speed Up Training of Search, Recommendation and Advertisement. ## Latest News - [2025/12] [DLRover on Ray's new architecture achieves its first official release.](docs/blogs/dlrover_on_ray.md) - [2025/08] [Practice: Gang Scheduling with DLRover.](docs/tutorial/gang_scheduling.md) - [2025/03] [Paper: How XPU-Timer working](https://arxiv.org/pdf/2502.05413) - [2025/01] [EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models, ICLR'25.](https://arxiv.org/abs/2412.07210) - [2024/06] [DLRover-RM has been accepted by VLDB'24.](docs/blogs/dlrover_rm.md) - [2024/04] [Flash Checkpoint Supports HuggingFace transformers.Trainer to Asynchronously persist checkpoints.](docs/blogs/flash_checkpoint.md#huggingface-transformerstrainer) - [2024/02] [Flash Checkpoint Saves the Megatron-LM Checkpoint in Seconds.](docs/blogs/megatron_flash_checkpoint.md) - [2024/01] [Flash Checkpoint to Recover Large Model Training From Failure in Seconds.](docs/blogs/flash_checkpoint.md) - [2023/11] [ATorch supporting efficient and easy-to-use model training is released.](https://github.com/intelligent-machine-learning/atorch) - [2023/10] [AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference as Preconditioning Matrix, NeurIPS'24.](https://github.com/intelligent-machine-learning/atorch/blob/main/docs/README-AGD.md) - [2023/09] [Weighted Sharpness-Aware Minimization (WSAM) has been accepted by KDD'23.](https://github.com/intelligent-machine-learning/atorch/blob/main/docs/README-WSAM.md) - [2023/08] [DLRover improves the stability of pre-trained model training over thousands of GPUs.](docs/blogs/stabilize_llm_training_cn.md) - [2023/04] [DLRover auto-scales nodes of a DeepRec distributed training job.](docs/blogs/deeprec_autoscale_cn.md) ## Why DLRover? ### Fault Tolerance to Reduce the Downtime of a Large Scale Training Job DLRover can restore the training when the process fails without stopping the training job. The actions to restore training in DLRover are: 1. Automatically diagnose the failure reason. 2. Restart the process not the node due to software errors. 3. Restart the failed nodes due to hardward errors. For detail, we can see the [blog of fault-tolerance and elasticity](docs/blogs/stabilize_llm_training_cn.md). **With fault tolerance, the goodput of GLM-65B training on thousands of GPUs increased from 69% to 95%**. The goodput is the time spent computing useful new steps over the elapsed time of the training job. The downtime details are shown:

#### Fault Tolerance and Flash Checkpoint to Reduce Downtime of PyTorch Training In addition to fault tolerance, DLRover provides the [flash checkpoint](docs/blogs/flash_checkpoint.md) to save/load checkpoint in seconds. With flash checkpoint, the training can frequently save checkpoints and reduce the roll-back step to resume training from the latest checkpoint when a failure happens. The features of flash checkpoint are: 1. Asynchronously persist the checkpoint to the storage. 2. Persist the checkpoint to the storage once the training process fails. 3. Load the checkpoint from the host memory after the training process restarts. 4. APIs for DDP, FSDP, DeepSpeed and Megatron-LM([cb995d5](https://github.com/NVIDIA/Megatron-LM/tree/cb995d571faea19d01a1bf55ed0fd89523b9ce64)).

The Performance of DLRover Flash Checkpoint to Save/Load GPT2-1.5B.

The figure illustrates that the I/O time of different DL frameworks to read checkpoint files when resuming training processes. With DLRover Flash Checkpoint, recovery could be completed in the order of seconds by loading checkpoints directly from shared memory, which is much faster compared to loading checkpoints from SSD and NAS. #### Fault Tolerance Improves the Stability of TensorFlow PS Training DLRover can recover failed parameter servers and workers to resume training. 1. DLRover can automatically launch a Pod with more memory to recover the OOM node. 2. DLRover can reassign the training data of a failed worker to other workers. 3. DLRover can automatically scale up the parameter servers to fit the model size. In AntGroup, DLRover manages hundreds of DL training jobs every day on the customized Kubernetes cluster in AntGroup. Except for the failed job resulting from code errors, **the rate of completed jobs increase from 89% with tf-operator in KubeFlow to 95%**. Other unrecoverable failure reasons of a job are data error, NaN loss of the model, network breakdown, and so on.

### Auto-Scaling to Improve Training Performance and Resource Utilization DLRover automatically scales up/down resources (for parameter servers or workers) at the runtime of a training job. By monitoring the workload of nodes and throughput, DLRover can diagnose the bottleneck of the resource configuration. The common bottleneck contains node straggler, the unbalanced workload of PS, insufficient CPU cores of nodes, and the insufficient number of nodes. DLRover can improve the training performance by dynamic resource adjustment. In order to improve the training througphput, users prefer to configure their jobs with over-provision resources to avoid any potential risk from insufficient resources. This usually ends up in huge resource waste. DLRover Auto-Scaling can allocate resources by the demand of model training to reduce the waste of resources.

### Dynamic Data Sharding For Elasticity and Fault-tolerance Dynamic data sharding splits the dataset into many small shards and each shard only contains a few batches of training samples. The worker will get a shard only when it using up samples of the last one. With the dynaic sharding, DLRover can - recover the shard if the worker fails before using up samples of the shard. - mitigate the worker straggler by assigning more shards to the fast worker. ### Integration to Offline and Online Deep Learning With the data source transparency provided by dynamic data sharding, DLRover can be integrated with offline training which consumes batch data, and also supports online learning with real-time streaming data. (fed with a message queue like RocketMQ/Kafka/Pulsar/..., or executed as a training sink node inside Flink/Spark/Ray/...) By practice, DLRover is an ideal component to build an end-to-end industrial online learning system, [estimator.md](docs/tutorial/estimator.md) provides a detailed example implemented with `tf.estimator.Estimator`. ## How to Use DLRover to Train Your Models? ### Under Kubernetes #### Train a PyTorch Model We can use `dlrover-run` to run the training script which `torchrun` or `torch.distributed.run` can run. ```bash pip install dlrover[k8s, torch] dlrover-run --nnodes=1 --nproc_per_node=$NUM_TRAINERS train_scripts.py ``` The more detail tutorials are: - [Elastic scheduling tutorial](docs/tutorial/torch_elasticjob_on_k8s.md) to support elasticity and fault tolerance of Pod on k8s. - [Node detection tutorial](docs/tutorial/check_node_health.md) to check the fault or slow node in a distributed job. - [Flash Checkpoint](docs/blogs/flash_checkpoint.md) to speed up checkpoint during training. #### Train a TensorFlow Model We can use DLRover to train a TensorFlow by the following steps: - Use TensorFlow estimator to develop the TensorFlow model. - Define the input of `tf.dataset` in a training configuration of DLRover. - Define your reader to read samples from the dataset file. We can refer to the [estimator.md](docs/tutorial/estimator.md) to train a model with DLRover. ### Under Ray Different from using Kubernetes, in the architecture based on Ray, not only can PyTorch/TensorFlow be used for training, but more complex heterogeneous computing scenarios are also supported. Users are free to explore and implement more general and complex deep learning computations. The differences between the two architectures(kubernetes <-> ray) can be found here: [overview](docs/design/dlrover-overview.md) More detail tutorials please refer to: [unified tutorial](docs/tutorial/unified/README.md) ## What's Next? - Continuous evolution and improvement of large model training stability capabilities. - Ongoing iteration and deep integration of XPU-TIMER. - Full stack solution for Online Deep Learning based on Ray. - ... ## Contributing Please refer to the [DEVELOPMENT](docs/developer_guide.md) ## Quick Start [Train a GPT Model on Ray.](examples/unified/elastic/nanogpt/README.md) [RL with OpenRLHF-PPO on Ray.](examples/unified/rl/openrlhf/ppo/run.sh) [An Example of Flash Checkpoint.](examples/pytorch/fcp_demo.py) [Train a PyTorch Model on Kubernetes.](docs/tutorial/torch_elasticjob_on_k8s.md) [Train a GPT Model on Kubernetes.](docs/tutorial/torch_nanogpt.md) [Train a TensorFlow Estimator on Kubernetes.](docs/tutorial/tf_elasticjob_on_k8s.md) ## Community Welcome to scan the DingTalk QR or search "AI Infra" in WeChat(微信) to join DLRover group. The DingTalk QR is: