Deepspeed

Deepspeed Skill

Comprehensive assistance with deepspeed development, generated from official documentation.

When to Use This Skill

This skill should be triggered when:

• Working with deepspeed
• Asking about deepspeed features or APIs
• Implementing deepspeed solutions
• Debugging deepspeed code
• Learning deepspeed best practices

Quick Reference

Common Patterns

Pattern 1: DeepNVMe Contents Requirements Creating DeepNVMe Handles Using DeepNVMe Handles Blocking File Write Non-Blocking File Write Parallel File Write Pinned Tensors Putting it together Acknowledgements Appendix Advanced Handle Creation Performance Tuning DeepNVMe APIs General I/O APIs GDS-specific APIs Handle Settings APIs This tutorial will show how to use DeepNVMe for data transfers between persistent storage and tensors residing in host or device memory. DeepNVMe improves the performance and efficiency of I/O operations in Deep Learning applications through powerful optimizations built on Non-Volatile Memory Express (NVMe) Solid State Drives (SSDs), Linux Asynchronous I/O (libaio), and NVIDIA Magnum IOTM GPUDirect® Storage (GDS). Requirements Ensure your environment is properly configured to use DeepNVMe. First, you need to install DeepSpeed version >= 0.15.0. Next, ensure that the DeepNVMe operators are available in the DeepSpeed installation. The async_io operator is required for any DeepNVMe functionality, while the gds operator is required only for GDS functionality. You can confirm availability of each operator by inspecting the output of ds_report to check that compatible status is [OKAY]. Below is a snippet of ds_report output confirming the availability of both async_io and gds operators. If async_io operator is unavailable, you will need to install the appropriate libaio library binaries for your Linux flavor. For example, Ubuntu users will need to run apt install libaio-dev. In general, you should carefully inspect ds_report output for helpful tips such as the following: [WARNING] async_io requires the dev libaio .so object and headers but these were not found. [WARNING] async_io: please install the libaio-dev package with apt [WARNING] If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found. To enable gds operator, you will need to install NVIDIA GDS by consulting the appropriate guide for bare-metal systems or Azure VMs (coming soon). Creating DeepNVMe Handles DeepNVMe functionality can be accessed through two abstractions: aio_handle and gds_handle. The aio_handle is usable on both host and device tensors. while gds_handle works only on CUDA tensors, but is more efficient. The first step to use DeepNVMe is to create a desired handle. aio_handle requires async_io operator, while gds_handle requires both async_io and gds operators. The following snippets illustrate aio_handle and gds_handle creation respectively. ### Create aio_handle from deepspeed.ops.op_builder import AsyncIOBuilder aio_handle = AsyncIOBuilder().load().aio_handle() ### Create gds_handle from deepspeed.ops.op_builder import GDSBuilder gds_handle = GDSBuilder().load().gds_handle() For simplicity, the above examples illustrate handle creation using default parameters. We expect that handles created with default parameters to provide good performance in most environments. However, you can see below for advanced handle creation. Using DeepNVMe Handles aio_handle and gds_handle provide identical APIs for storing tensors to files or loading tensors from files. A common feature of these APIs is that they take a tensor and a file path as arguments for the desired I/O operation. For best performance, pinned device or host tensors should be used for I/O operations (see here for details). For brevity, this tutorial will use aio_handle for illustration, but keep in mind that gds_handle works similarly. You can see the available APIs in a Python shell via tab completion on an aio_handle object . This is illustrated using tab completion of h.. >python Python 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle() >>> h. h.async_pread( h.free_cpu_locked_tensor( h.get_overlap_events( h.get_single_submit( h.new_cpu_locked_tensor( h.pwrite( h.sync_pread( h.wait( h.async_pwrite( h.get_block_size( h.get_queue_depth( h.get_intra_op_parallelism( h.pread( h.read( h.sync_pwrite( h.write( The APIs of interest for performing I/O operations are those named with pread and pwrite substrings. For brevity, we will focus on the file write APIs, namely sync_pwrite, async_pwrite, and pwrite. We will discuss only sync_pwrite and async_pwrite below because they are specializations of pwrite. Blocking File Write sync_pwrite provides the standard blocking semantics of Python file write. The example below illustrates using sync_pwrite to store a 1GB CUDA tensor to a local NVMe file. >>> import os >>> os.path.isfile('/local_nvme/test_1GB.pt') False >>> import torch >>> t=torch.empty(10243, dtype=torch.uint8).cuda() >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle() >>> h.sync_pwrite(t,'/local_nvme/test_1GB.pt') >>> os.path.isfile('/local_nvme/test_1GB.pt') True >>> os.path.getsize('/local_nvme/test_1GB.pt') 1073741824 Non-Blocking File Write An important DeepNVMe optimization is the non-blocking I/O semantics which enables Python threads to overlap computations with I/O operations. async_pwrite provides the non-blocking semantics for file writes. The Python thread can later use wait() to synchronize with the I/O operation. async_write can also be used to submit multiple back-to-back non-blocking I/O operations, of which can then be later blocked on using a single wait(). The example below illustrates using async_pwrite to store a 1GB CUDA tensor to a local NVMe file. >>> import os >>> os.path.isfile('/local_nvme/test_1GB.pt') False >>> import torch >>> t=torch.empty(10243, dtype=torch.uint8).cuda() >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle() >>> h.async_pwrite(t,'/local_nvme/test_1GB.pt') >>> h.wait() 1 >>> os.path.isfile('/local_nvme/test_1GB.pt') True >>> os.path.getsize('/local_nvme/test_1GB.pt') 1073741824 Warning for non-blocking I/O operations: To avoid data races and corruptions, .wait() must be carefully used to serialize the writing of source tensors, and the reading of destination tensors. For example, the following update of t during a non-blocking file write is unsafe and could corrupt /local_nvme/test_1GB.pt. >>> t=torch.empty(10243, dtype=torch.uint8).cuda() >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle() >>> h.async_pwrite(t,'/local_nvme/test_1GB.pt') >>> t += 1 # <--- Data race; avoid by preceding with h.wait() Similar safety problems apply to reading the destination tensor of a non-blocking file read without .wait() synchronization. Parallel File Write An important DeepNVMe optimization is the ability to parallelize individual I/O operations. This optimization is enabled by specifying the desired parallelism degree when constructing a DeepNVMe handle. Subsequent I/O operations with that handle are automatically parallelized over the requested number of host or device threads, as appropriate. I/O parallelism is composable with either the blocking or non-blocking I/O APIs. The example below illustrates 4-way parallelism of a file write using async_pwrite. Note the use of intra_op_parallelism argument to specify the desired parallelism degree in handle creation. >>> import os >>> os.path.isfile('/local_nvme/test_1GB.pt') False >>> import torch >>> t=torch.empty(10243, dtype=torch.uint8).cuda() >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle(intra_op_parallelism=4) >>> h.async_pwrite(t,'/local_nvme/test_1GB.pt') >>> h.wait() 1 >>> os.path.isfile('/local_nvme/test_1GB.pt') True >>> os.path.getsize('/local_nvme/test_1GB.pt') 1073741824 Pinned Tensors A key part of DeepNVMe optimizations is using direct memory access (DMA) for I/O operations, which requires that the host or device tensor be pinned. To pin host tensors, you can use mechanisms provided by Pytorch or DeepSpeed Accelerators. The following example illustrates writing a pinned CPU tensor to a local NVMe file. >>> import os >>> os.path.isfile('/local_nvme/test_1GB.pt') False >>> import torch >>> t=torch.empty(10243, dtype=torch.uint8).pin_memory() >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle() >>> h.async_pwrite(t,'/local_nvme/test_1GB.pt') >>> h.wait() 1 >>> os.path.isfile('/local_nvme/test_1GB.pt') True >>> os.path.getsize('/local_nvme/test_1GB.pt') 1073741824 On the other hand,gds_handle provides new_pinned_device_tensor() and pin_device_tensor() functions for pinning CUDA tensors. The following example illustrates writing a pinned CUDA tensor to a local NVMe file. >>> import os >>> os.path.isfile('/local_nvme/test_1GB.pt') False >>> import torch >>> t=torch.empty(10243, dtype=torch.uint8).cuda() >>> from deepspeed.ops.op_builder import GDSBuilder >>> h = GDSBuilder().load().gds_handle() >>> h.pin_device_tensor(t) >>> h.async_pwrite(t,'/local_nvme/test_1GB.pt') >>> h.wait() 1 >>> os.path.isfile('/local_nvme/test_1GB.pt') True >>> os.path.getsize('/local_nvme/test_1GB.pt') 1073741824 >>> h.unpin_device_tensor(t) Putting it together We hope that the above material helps you to get started with DeepNVMe. You can also use the following links to see DeepNVMe usage in real-world Deep Learning applications. Parameter swapper in ZeRO-Inference and ZeRO-Infinity. Optimizer swapper in ZeRO-Infinity. Gradient swapper in ZeRO-Infinity. Simple file read and write operations. Acknowledgements This tutorial has been significantly improved by feedback from Guanhua Wang, Masahiro Tanaka, and Stas Bekman. Appendix Advanced Handle Creation Achieving peak I/O performance with DeepNVMe requires careful configuration of handle creation. In particular, the parameters of aio_handle and gds_handle constructors are performance-critical because they determine how efficiently DeepNVMe interacts with the underlying storage subsystem (i.e., libaio, GDS, PCIe, and SSD). For convenience we make it possible to create handles using default parameter values which will provide decent performance in most scenarios. However, squeezing out every available performance in your environment will likely require tuning the constructor parameters, namely block_size, queue_depth, single_submit, overlap_events, and intra_op_parallelism. The aio_handle constructor parameters and default values are illustrated below: >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> help(AsyncIOBuilder().load().aio_handle()) Help on aio_handle in module async_io object: class aio_handle(pybind11_builtins.pybind11_object) | Method resolution order: | aio_handle | pybind11_builtins.pybind11_object | builtins.object | | Methods defined here: | | init(...) | init(self: async_io.aio_handle, block_size: int = 1048576, queue_depth: int = 128, single_submit: bool = False, overlap_events: bool = False, intra_op_parallelism: int = 1) -> None | | AIO handle constructor Performance Tuning As discussed earlier, achieving peak DeepNVMe performance for a target workload or environment requires using optimally configured aio_handle or gds_handle handles. For configuration convenience, we provide a utility called ds_nvme_tune to automate the discovery of optimal DeepNVMe configurations. ds_nvme_tune automatically explores a user-specified or default configuration space and recommends the option that provides the best read and write performance. Below is an example usage of ds_nvme_tune to tune aio_handle data transfers between GPU memory and a local NVVMe SSD mounted on /local_nvme. This example used the default configuration space of ds_nvme_tune for tuning. $ ds_nvme_tune --nvme_dir /local_nvme --gpu Running DeepNVMe performance tuning on ['/local_nvme/'] Best performance (GB/sec): read = 3.69, write = 3.18 { "aio": { "single_submit": "false", "overlap_events": "true", "intra_op_parallelism": 8, "queue_depth": 32, "block_size": 1048576 } } The above tuning was executed on a Lambda workstation equipped with two NVIDIA A6000-48GB GPUs, 252GB of DRAM, and a CS3040 NVMe 2TB SDD with peak read and write speeds of 5.6 GB/s and 4.3 GB/s respectively. The tuning required about four and half minutes. Based on the results, one can expect to achieve read and write transfer speeds of 3.69 GB/sec and 3.18 GB/sec respectively by using an aio_handle configured as below. >>> from deepspeed.ops.op_builder import AsyncIOBuilder >>> h = AsyncIOBuilder().load().aio_handle(block_size=1048576, queue_depth=32, single_submit=False, overlap_events=True, intra_op_parallelism=8) The full command line options of ds_nvme_tune can be obtained via the normal -h or --help. usage: ds_nvme_tune [-h] --nvme_dir NVME_DIR [NVME_DIR ...] [--sweep_config SWEEP_CONFIG] [--no_read] [--no_write] [--io_size IO_SIZE] [--gpu] [--gds] [--flush_page_cache] [--log_dir LOG_DIR] [--loops LOOPS] [--verbose] options: -h, --help show this help message and exit --nvme_dir NVME_DIR [NVME_DIR ...] Directory in which to perform I/O tests. A writeable directory on a NVMe device. --sweep_config SWEEP_CONFIG Performance sweep configuration json file. --no_read Disable read performance measurements. --no_write Disable write performance measurements. --io_size IO_SIZE Number of I/O bytes to read/write for performance measurements. --gpu Test tensor transfers between GPU device and NVME device. --gds Run the sweep over NVIDIA GPUDirectStorage operator --flush_page_cache Page cache will not be flushed and reported read speeds may be higher than actual Requires sudo access. --log_dir LOG_DIR Output directory for performance log files. Default is ./_aio_bench_logs --loops LOOPS Count of operation repetitions --verbose Print debugging information. DeepNVMe APIs For convenience, we provide listing and brief descriptions of the DeepNVMe APIs. General I/O APIs The following functions are used for I/O operations with both aio_handle and gds_handle. Function Description async_pread Non-blocking file read into tensor sync_pread Blocking file read into tensor pread File read with blocking and non-blocking options async_pwrite Non-blocking file write from tensor sync_pwrite Blocking file write from tensor pwrite File write with blocking and non-blocking options wait Wait for non-blocking I/O operations to complete GDS-specific APIs The following functions are available only for gds_handle Function Description new_pinned_device_tensor Allocate and pin a device tensor free_pinned_device_tensor Unpin and free a device tensor pin_device_tensor Pin a device tensor unpin_device_tensor unpin a device tensor Handle Settings APIs The following APIs can be used to probe handle configuration. Function Description get_queue_depth Return queue depth setting get_single_submit Return whether single_submit is enabled get_intra_op_parallelism Return I/O parallelism degree get_block_size Return I/O block size setting get_overlap_events Return whether overlap_event is enabled Updated: November 5, 2025 Previous Next

libaio

Pattern 2: Mixture of Experts for NLG models Contents 1. Installation 2. Training NLG+MoE models 2.1. Changes to the model 2.2. Pre-training the Standard MoE model 2.3. Pre-training the PR-MoE model 2.4. Training MoS with reduced model size In this tutorial, we introduce how to apply DeepSpeed Mixture of Experts (MoE) to NLG models, which reduces the training cost by 5 times and reduce the MoE model size by 3 times (details in our Blog). We use the GPT-3 like models in Megatron-LM framework as the example. Before reading this tutorial, we recommend to first read the tutorials about Mixture of Experts and Megatron-LM GPT pre-training. 1. Installation You would need to install DeepSpeed v0.6.0 or higher to use the MoE feature. The MoE for NLG model examples are in the Megatron-DeepSpeed repo under the MoE folder. 2. Training NLG+MoE models 2.1. Changes to the model To apply MoE to the GPT-style model, we made several changes in Megatron framework, mostly in megatron/model/ where we add the MoE layers into the model. 2.2. Pre-training the Standard MoE model We provide example training scripts under examples_deepspeed/MoE which we used to perform the experiments in our Blog. There are a few new hyperparameters for standard MoE model: --num-experts: the number of experts per MoE layer. In our experiments we set it to 128. Larger number of experts tend to provide better convergence, but it’s a diminishing return. --moe-expert-parallel-size: degree of the MoE expert parallelism. In other words, there will be num-experts/moe-expert-parallel-size experts on each GPU. Thus --moe-expert-parallel-size should be no more than both number of GPUs, and --num-experts. --moe-loss-coeff: scaling coefficient for adding MoE loss to model loss. In our experiments we find that 0.01 is a good setting. --moe-train-capacity-factor, --moe-eval-capacity-factor, --moe-min-capacity: these configs determine how many tokens can a single expert handle. Larger numbers could lead to better convergence, but would also lead to slower training since the load would be more unbalanced on different experts. --disable-moe-token-dropping: this will completely remove the limitation of how many tokens can a single expert handle. For the same reason as above, we only recommend using this during inference/eval. 2.3. Pre-training the PR-MoE model PR-MoE is a new designed MoE models, standing for Pyramid-Residual-MoE, which improves the parameter efficiency up to 3x as compared to standard MoE. Please see our Blog for more details. We provide example training scripts under examples_deepspeed/MoE. There are a few different hyperparameters for PR-MoE model compared to standard MoE: --num-experts: Instead of providing a single number, to enable Pyramid-MoE, you need to provide a list, whose length is the same as the number of MoE layers. We suggest to use more experts in the latter stage (close to output) of the model. --mlp-type: chosen from [standard, residual]. When it is residual, Residual-MoE is enabled. In addition to the new hyperparameters above for standard MoE and PR-MoE, for NLG+MoE models we found that it’s helpful to lower the learning rate and increase the learning rate decay duration compared to the base dense model. Details of our tuning can be found in the example training scripts. Regarding training data, we are not able to release our internal data but any public data for Megatron-LM pre-training can be directly used to train MoE models (with the caveat that it might not provide the exact same model quality as in our experiments). For example, we evaluated The Pile dataset (pile.eleuther.ai, github.com/EleutherAI/the-pile) for both dense and MoE models. Table 1 below shows that this public data provides similar evaluation results as our internal data. Model size LAMBADA: completion prediction PIQA: commonsense reasoning BoolQ: reading comprehension RACE-h: reading comprehension TriviaQA: question answering WebQs: question answering Dense NLG: 350M, internal data 0.5203 0.6931 0.5364 0.3177 0.0321 0.0157 350M, public Pile 0.5106 0.6589 0.5933 0.3196 0.0257 0.0064 Standard MoE NLG: 350M+MoE-128, internal data 0.6270 0.7459 0.6046 0.3560 0.1658 0.0517 350M+MoE-128, public Pile 0.6128 0.7323 0.6040 0.3349 0.1111 0.0335 PR-MoE NLG: 350M+MoE-128, internal data 0.6365 0.7399 0.5988 0.3569 0.1630 0.0473 PR-MoE + MoS NLG: 350M+MoE-128, internal data 0.6346 0.7334 0.5807 0.3483 0.1369 0.0522 Table 1: Zero-shot evaluation results (last six columns) for different dense and MoE NLG models. All zero-shot evaluation results use the accuracy metric. 2.4. Training MoS with reduced model size MoS, standing for Mixture-of-Students, is a staged distillation-based technique for compressing large MoE models. MoS further reduces the model size by 12.5%, leading to up 3.7x model size reduction when combined with PR-MoE over the standard MoE. The reduced model size helps reduce the latency and cost during inference. To train an MoS model, one needs to specify a few additional parameters. We will use PR-MoE as an example: --mos: This would enable Mixture-of-Students via knowledge distillation. --load-teacher: This specifies the path to the teacher model checkpoint. This is a mandatory argument for using MoS and the teacher model checkpoint can be obtained by either training a standard MoE or the PR-MoE. num-layers-teacher, --hidden-size-teacher, --hidden-size-teacher, --num-experts-teacher: In addition to the teacher model checkpoint path, we also need to specify the model architecture of the teacher model such as its number of layers, hidden dimension size, and the number of experts per MoE layer. In the case of PR-MoE, we need to also provide a list of experts for the teacher model, where we remove a few expert layers from the teacher model. In addition to the new parameters above, we observe that using the teacher PR-MoE during the entire training process may adversely impact the final student model accuracy. In our experiments, we use a staged distillation method by stopping distillation early in the training process (e.g., after 400K steps) and perform optimization only against the standard language modeling loss for the rest of the training. We provide example training scripts under examples_deepspeed/MoE. Details of our parameter settings can be found in the example training scripts. The performance results of MoS can be seen from our blog post and our paper. Updated: November 5, 2025 Previous Next

megatron/model/

Pattern 3: MoS, standing for Mixture-of-Students, is a staged distillation-based technique for compressing large MoE models. MoS further reduces the model size by 12.5%, leading to up 3.7x model size reduction when combined with PR-MoE over the standard MoE. The reduced model size helps reduce the latency and cost during inference. To train an MoS model, one needs to specify a few additional parameters. We will use PR-MoE as an example:

--mos

Pattern 4: Learning Rate Range Test Contents Learning Rate Range Test (LRRT) Prerequisites LRRT Parameters Required Model Configuration Changes PyTorch Example: Tuning for Large Batch Sizes This tutorial shows how to use to perform Learning Rate range tests in PyTorch. Learning Rate Range Test (LRRT) Learning rate range test ( LRRT ) is a method for discovering the largest learning rate values that can be used to train a model without divergence. Data scientists are often interested in this information because large learning rates lead to faster model convergence than a small learning rates. Moreover, large learning rates are crucial in learning rate schedules such as CLR and 1Cycle, which are used to train effectively with large batch sizes. DeepSpeed provides LRRT for model training in PyTorch frameworks. Prerequisites To use DeepSpeed’s LRRT, you must satisfy the following two conditions: Integrate DeepSpeed into your training script using the Getting Started guide. Add the parameters to configure LRRT to the parameters of your model. The LRRT parameters are defined below. LRRT Parameters LRRT works by linearly increasing the learning rate by a predefined amount, at predefined intervals. Thus, LRRT is a form of learning rate schedule because it defines how and when the learning rate should change during model training. To configure LRRT, you will need to set these parameters: lr_range_test_min_lr : The initial learning rate for training (float) lr_range_test_step_size: The interval for scaling up learning rate, defined in training steps (integer) lr_range_test_step_rate: The scaling factor for increasing learning rate (float) lr_range_test_staircase: If true, learning rate is changed every lr_range_test_step_size training steps, otherwise learning rate is changed at every training step (boolean) Required Model Configuration Changes We will illustrate the required model configuration changes an example LRRT schedule that: Starts training with an initial learning rate of 0.0001 Uses a scaling rate of 5 Uses a scaling interval of 200 training steps Scales learning rate at every training step, i.e., does not use staircase PyTorch For PyTorch models, LRRT is implemented as a learning rate scheduler, a feature that is available in PyTorch versions 1.0.1 and newer. Thus, you can add a "scheduler" entry of type "LRRangeTest" into your model configuration as illustrated below: "scheduler": { "type": "LRRangeTest", "params": { "lr_range_test_min_lr": 0.0001, "lr_range_test_step_size": 200, "lr_range_test_step_rate": 5, "lr_range_test_staircase": false } } Example: Tuning for Large Batch Sizes We illustrate how LRRT can benefit data scientists with a snippet of our experience of tuning an internal production model to converge efficiently on larger batch sizes, as we scaled from one GPU (batch size 512) to four GPUs (batch size 2048). Our goal was to train the model with the larger batch size to match the performance of the smaller batch size using the same amount of data samples. The challenge here is the well known problem of slow convergence of large batch size training. Our approach was to use a 1Cycle schedule in DeepSpeed to tackle this problem, and we used LRRT to configure the schedule. In the plots below, we illustrate using LRRT to discover the maximum learning rates for effective training with batch size 2048. The plot on the left shows the impact of large learning rates on validation loss over the first 9000 batches of training. The plot on the right shows the learning rate values during the same period of training. Using grid search we discover that the best fixed learning rate for the batch size 2048 is 0.0002. The blue line (lr=0.0002) represents training with this fixed learning rate. We compare the two LRRT schedules with this fixed learning rate. The orange (lr_range_test_step_rate=5) and gray (lr_range_test_step_rate=50) lines represent training with similar LRRT schedules that differ only in lr_range_test_step_rate values. Although the LRRT schedules start from the same base learning rate, the gray line’s learning rate grows about 10 times faster than the orange line. Also, the learning rates of the LRRT schedules had grown larger than that of the blue line in the presented data points. We subsequently refer to the gray line as “fast growing”, and the orange line as “slow growing” LRRT schedules respectively. We make the following observations from this small example. Larger learning rates clearly benefit model performance, up to some point. The fast growing LRRT schedule achieves validation loss of 0.46 after 3000 batches, which the fixed learning rate does not achieve with 9000 batches. The slow growing LRRT does not match that score until after 6000 batches, however it maintains an increasing performance advantage over the fixed learning rate. There is an upper bound on learning rate values that are useful for training the model. The fast growing LRRT schedule hits this boundary quickly and diverges, while the slow growing LRRT will later diverge for the same reason. LRRT helped us discover these boundaries quickly, using less than 2% of the training data. These boundaries are useful information for constructing learning rate schedules. These observations from LRRT helped us to configure the learning rate boundaries and the cycle span for a 1Cycle schedule that solves the problem, as shown below. "OneCycle": { "cycle_min_lr": 0.002, "cycle_max_lr": 0.005, "cycle_first_step_size": 2000, "cycle_second_step_size": 2000, ... } In our experience these are four most critical parameters of 1Cycle schedules. We chose to use the slower LRRT schedule (lr_range_test_step_rate=5) to set cycle_min_lr because it achieves the best loss and the faster schedule diverges fairly quickly. We set cycle_max_lr to 0.005 even though the plot shows that performance was still improving at slightly higher learning rate. This is because we observed that if we wait till the maximum learning rate, the model could be at the point of divergence and impossible to recover. Since it takes 8000 batches for the learning rate to become 0.005, we set cycle_first_step_size and (cycle_second_step_size) to 2000 which is the number of steps that it takes for four GPUs to process 8000 batches. We hope this brief example sparks your imagination on using LRRT for your own unique tuning challenges. Updated: November 5, 2025 Previous Next

lr_range_test_min_lr

Pattern 5: Training Overview and Features Contents Overview Distributed, Effective, and Efficient Training with Ease Speed Memory efficiency Scalability Communication efficiency Data efficiency Supporting long sequence length Fast convergence for effectiveness Good Usability Features Distributed Training with Mixed Precision Mixed Precision Training Single-GPU, Multi-GPU, and Multi-Node Training Pipeline Parallelism Model Parallelism Support for Custom Model Parallelism Integration with Megatron-LM The Zero Redundancy Optimizer Optimizer State and Gradient Partitioning Activation Partitioning Constant Buffer Optimization (CBO) Contiguous Memory Optimization (CMO) ZeRO-Offload Additional Memory and Bandwidth Optimizations Smart Gradient Accumulation Communication Overlapping Training Features Simplified training API Activation Checkpointing API Gradient Clipping Automatic loss scaling with mixed precision Training Optimizers 1-bit Adam, 0/1 Adam and 1-bit LAMB optimizers with up to 26x less communication Fused Adam optimizer and arbitrary torch.optim.Optimizer CPU-Adam: High-Performance vectorized implementation of Adam Memory bandwidth optimized FP16 Optimizer Large Batch Training with LAMB Optimizer Memory-Efficient Training with ZeRO Optimizer Training Agnostic Checkpointing Advanced parameter search Learning Rate Range Test 1Cycle Learning Rate Schedule Simplified Data Loader Data Efficiency Curriculum Learning Performance Analysis and Debugging Wall Clock Breakdown Timing Activation Checkpoint Functions Flops Profiler Autotuning Monitor Communication Logging Sparse Attention Mixture of Experts (MoE) Overview Training advanced deep learning models is challenging. Beyond model design, model scientists also need to set up the state-of-the-art training techniques such as distributed training, mixed precision, gradient accumulation, and checkpointing. Yet still, scientists may not achieve the desired system performance and convergence rate. Large model sizes are even more challenging: a large model easily runs out of memory with pure data parallelism and it is difficult to use model parallelism. DeepSpeed addresses these challenges to accelerate model development and training. Distributed, Effective, and Efficient Training with Ease The DeepSpeed API is a lightweight wrapper on PyTorch. This means that you can use everything you love in PyTorch and without learning a new platform. In addition, DeepSpeed manages all of the boilerplate state-of-the-art training techniques, such as distributed training, mixed precision, gradient accumulation, and checkpoints so that you can focus on your model development. Most importantly, you can leverage the distinctive efficiency and effectiveness benefit of DeepSpeed to boost speed and scale with just a few lines of code changes to your PyTorch models. Speed DeepSpeed achieves high performance and fast convergence through a combination of efficiency optimizations on compute/communication/memory/IO and effectiveness optimizations on advanced hyperparameter tuning and optimizers. For example: DeepSpeed trains BERT-large to parity in 44 mins using 1024 V100 GPUs (64 DGX-2 boxes) and in 2.4 hours using 256 GPUs (16 DGX-2 boxes). BERT-large Training Times Devices Source Training Time 1024 V100 GPUs DeepSpeed 44 min 256 V100 GPUs DeepSpeed 2.4 hr 64 V100 GPUs DeepSpeed 8.68 hr 16 V100 GPUs DeepSpeed 33.22 hr BERT code and tutorials will be available soon. DeepSpeed trains GPT2 (1.5 billion parameters) 3.75x faster than state-of-art, NVIDIA Megatron on Azure GPUs. Read more: GPT tutorial Memory efficiency DeepSpeed provides memory-efficient data parallelism and enables training models without model parallelism. For example, DeepSpeed can train models with up to 13 billion parameters on a single GPU. In comparison, existing frameworks (e.g., PyTorch’s Distributed Data Parallel) run out of memory with 1.4 billion parameter models. DeepSpeed reduces the training memory footprint through a novel solution called Zero Redundancy Optimizer (ZeRO). Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states and gradients to save significant memory. Furthermore, it also reduces activation memory and fragmented memory. The current implementation (ZeRO-2) reduces memory by up to 8x relative to the state-of-art. You can read more about ZeRO in our paper, and in our blog posts related to ZeRO-1 and ZeRO-2. With this impressive memory reduction, early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category. For model scientists with limited GPU resources, ZeRO-Offload leverages both CPU and GPU memory for training large models. Using a machine with a single GPU, our users can run models of up to 13 billion parameters without running out of memory, 10x bigger than the existing approaches, while obtaining competitive throughput. This feature democratizes multi-billion-parameter model training and opens the window for many deep learning practitioners to explore bigger and better models. Scalability DeepSpeed supports efficient data parallelism, model parallelism, pipeline parallelism and their combinations, which we call 3D parallelism. 3D parallelism of DeepSpeed provides system support to run models with trillions of parameters, read more in our press-release and tutorial. DeepSpeed can run large models more efficiently, up to 10x faster for models with various sizes spanning 1.5B to hundred billion. More specifically, the data parallelism powered by ZeRO is complementary and can be combined with different types of model parallelism. It allows DeepSpeed to fit models using lower degree of model parallelism and higher batch size, offering significant performance gains compared to using model parallelism alone. Read more: ZeRO paper, and GPT tutorial. The figure depicts system throughput improvements of DeepSpeed (combining ZeRO-powered data parallelism with model parallelism of NVIDIA Megatron-LM) over using Megatron-LM alone. Communication efficiency Pipeline parallelism of DeepSpeed reduce communication volume during distributed training, which allows users to train multi-billion-parameter models 2–7x faster on clusters with limited network bandwidth. 1-bit Adam, 0/1 Adam and 1-bit LAMB reduce communication volume by up to 26x while achieving similar convergence efficiency to Adam, allowing for scaling to different types of GPU clusters and networks. 1-bit Adam blog post, 1-bit Adam tutorial, 0/1 Adam tutorial, 1-bit LAMB tutorial. Data efficiency DeepSpeed Data Efficiency Library provides efficient data sampling via curriculum learning and efficient data routing via random layerwise token dropping. The composed solution enables up to 2x data and 2x time saving during GPT-3/BERT pretraining and GPT/ViT finetuning, or further improve model quality under the same data/time. See more in the tutorial. Supporting long sequence length DeepSpeed offers sparse attention kernels—an instrumental technology to support long sequences of model inputs, whether for text, image, or sound. Compared with the classic dense Transformers, it powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution with comparable accuracy. It also outperforms state-of-the-art sparse implementations with 1.5–3x faster execution. Furthermore, our sparse kernels support efficient execution of flexible sparse format and empower users to innovate on their custom sparse structures. Read more here. Fast convergence for effectiveness DeepSpeed supports advanced hyperparameter tuning and large batch size optimizers such as LAMB. These improve the effectiveness of model training and reduce the number of samples required to convergence to desired accuracy. Read more: Tuning tutorial. Good Usability Only a few lines of code changes are needed to enable a PyTorch model to use DeepSpeed and ZeRO. Compared to current model parallelism libraries, DeepSpeed does not require a code redesign or model refactoring. It also does not put limitations on model dimensions (such as number of attention heads, hidden sizes, and others), batch size, or any other training parameters. For models of up to 13 billion parameters, you can use ZeRO-powered data parallelism conveniently without requiring model parallelism, while in contrast, standard data parallelism will run out of memory for models with more than 1.4 billion parameters. In addition, DeepSpeed conveniently supports flexible combination of ZeRO-powered data parallelism with custom model parallelisms, such as tensor slicing of NVIDIA’s Megatron-LM. Features Below we provide a brief feature list, see our detailed feature overview for descriptions and usage. Distributed Training with Mixed Precision 16-bit mixed precision Single-GPU/Multi-GPU/Multi-Node Model Parallelism Support for Custom Model Parallelism Integration with Megatron-LM Pipeline Parallelism 3D Parallelism The Zero Redundancy Optimizer Optimizer State and Gradient Partitioning Activation Partitioning Constant Buffer Optimization Contiguous Memory Optimization ZeRO-Offload Leverage both CPU/GPU memory for model training Support 10B model training on a single GPU Ultra-fast dense transformer kernels Sparse attention Memory- and compute-efficient sparse kernels Support 10x long sequences than dense Flexible support to different sparse structures 1-bit Adam, 0/1 Adam and 1-bit LAMB Custom communication collective Up to 26x communication volume saving Additional Memory and Bandwidth Optimizations Smart Gradient Accumulation Communication/Computation Overlap Training Features Simplified training API Gradient Clipping Automatic loss scaling with mixed precision Training Optimizers Fused Adam optimizer and arbitrary torch.optim.Optimizer Memory bandwidth optimized FP16 Optimizer Large Batch Training with LAMB Optimizer Memory efficient Training with ZeRO Optimizer CPU-Adam Training Agnostic Checkpointing Advanced Parameter Search Learning Rate Range Test 1Cycle Learning Rate Schedule Simplified Data Loader Data Efficiency Efficient data sampling via curriculum learning and efficient data routing via random layerwise token dropping Up to 2x data and 2x time saving during GPT-3/BERT pretraining and GPT/ViT finetuning Or further improve model quality under the same data/time Curriculum Learning A curriculum learning-based data pipeline that presents easier or simpler examples earlier during training Stable and 3.3x faster GPT-2 pre-training with 8x/4x larger batch size/learning rate while maintaining token-wise convergence speed Complementary to many other DeepSpeed features Note that the Data Efficiency Library above provides more general curriculum learning support. This legacy curriculum learning feature is still supported but we recommend to use the Data Efficiency Library. Progressive Layer Dropping Efficient and robust compressed training Up to 2.5x convergence speedup for pre-training Performance An

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