Getting Started


  • Installing is as simple as pip install deepspeed, see more details.
  • Please see our Azure tutorial to get started with DeepSpeed on Azure!
  • If you’re not on Azure, we recommend using our docker image via docker pull deepspeed/deepspeed:latest which contains a pre-installed version of DeepSpeed and all the necessary dependencies.

Writing DeepSpeed Models

DeepSpeed model training is accomplished using the DeepSpeed engine. The engine can wrap any arbitrary model of type torch.nn.module and has a minimal set of APIs for training and checkpointing the model. Please see the tutorials for detailed examples.

To initialize the DeepSpeed engine:

model_engine, optimizer, _, _ = deepspeed.initialize(args=cmd_args,

deepspeed.initialize ensures that all of the necessary setup required for distributed data parallel or mixed precision training are done appropriately under the hood. In addition to wrapping the model, DeepSpeed can construct and manage the training optimizer, data loader, and the learning rate scheduler based on the parameters passed to deepspeed.initialize and the DeepSpeed configuration file.

If you already have a distributed environment setup, you’d need to replace:




The default is to use the NCCL backend, which DeepSpeed has been thoroughly tested with, but you can also override the default.

But if you don’t need the distributed environment setup until after deepspeed.initialize() you don’t have to use this function, as DeepSpeed will automatically initialize the distributed environment during its initialize. Regardless, you will need to remove torch.distributed.init_process_group if you already had it in place.


Once the DeepSpeed engine has been initialized, it can be used to train the model using three simple APIs for forward propagation (callable object), backward propagation (backward), and weight updates (step).

for step, batch in enumerate(data_loader):
    #forward() method
    loss = model_engine(batch)

    #runs backpropagation

    #weight update

Under the hood, DeepSpeed automatically performs the necessary operations required for distributed data parallel training, in mixed precision, with a pre-defined learning rate schedule:

  • Gradient Averaging: in distributed data parallel training, backward ensures that gradients are averaged across data parallel processes after training on an train_batch_size.

  • Loss Scaling: in FP16/mixed precision training, the DeepSpeed engine automatically handles scaling the loss to avoid precision loss in the gradients.

  • Learning Rate Schedule: if using DeepSpeed’s learning rate schedule, then DeepSpeed automatically handles any updates to the learning rate when step is executed.

Model Checkpointing

Saving and loading the training state is handled via the save_checkpoint and load_checkpoint API in DeepSpeed which takes two arguments to uniquely identify a checkpoint:

  • ckpt_dir: the directory where checkpoints will be saved.
  • ckpt_id: an identifier that uniquely identifies a checkpoint in the directory. In the following code snippet, we use the loss value as the checkpoint identifier.
#load checkpoint
_, client_sd = model_engine.load_checkpoint(args.load_dir, args.ckpt_id)
step = client_sd['step']

#advance data loader to ckpt step
dataloader_to_step(data_loader, step + 1)

for step, batch in enumerate(data_loader):

    #forward() method
    loss = model_engine(batch)

    #runs backpropagation

    #weight update

    #save checkpoint
    if step % args.save_interval:
        client_sd['step'] = step
        ckpt_id = loss.item()
        model_engine.save_checkpoint(args.save_dir, ckpt_id, client_sd = client_sd)

DeepSpeed can automatically save and restore the model, optimizer, and the learning rate scheduler states while hiding away these details from the user. However, the user may want to save other data in addition to these that are unique to a given model training. To support these items, save_checkpoint accepts a client state dictionary client_sd for saving. These items can be retrieved from load_checkpoint as a return argument. In the example above, the step value is stored as part of the client_sd.

Important: all processes must call this method and not just the process with rank 0. It is because each process needs to save its master weights and scheduler+optimizer states. This method will hang waiting to synchronize with other processes if it’s called just for the process with rank 0.

DeepSpeed Configuration

DeepSpeed features can be enabled, disabled, or configured using a config JSON file that should be specified as args.deepspeed_config. A sample config file is shown below. For a full set of features see API doc.

  "train_batch_size": 8,
  "gradient_accumulation_steps": 1,
  "optimizer": {
    "type": "Adam",
    "params": {
      "lr": 0.00015
  "fp16": {
    "enabled": true
  "zero_optimization": true

Launching DeepSpeed Training

DeepSpeed installs the entry point deepspeed to launch distributed training. We illustrate an example usage of DeepSpeed with the following assumptions:

  1. You have already integrated DeepSpeed into your model
  2. is the entry script for your model
  3. client args is the argparse command line arguments
  4. ds_config.json is the configuration file for DeepSpeed

Resource Configuration (multi-node)

DeepSpeed configures multi-node compute resources with hostfiles that are compatible with OpenMPI and Horovod. A hostfile is a list of hostnames (or SSH aliases), which are machines accessible via passwordless SSH, and slot counts, which specify the number of GPUs available on the system. For example,

worker-1 slots=4
worker-2 slots=4

specifies that two machines named worker-1 and worker-2 each have four GPUs to use for training.

Hostfiles are specified with the --hostfile command line option. If no hostfile is specified, DeepSpeed searches for /job/hostfile. If no hostfile is specified or found, DeepSpeed queries the number of GPUs on the local machine to discover the number of local slots available.

The following command launches a PyTorch training job across all available nodes and GPUs specified in myhostfile:

deepspeed <> <client args> \
  --deepspeed --deepspeed_config ds_config.json --hostfile=myhostfile

Alternatively, DeepSpeed allows you to restrict distributed training of your model to a subset of the available nodes and GPUs. This feature is enabled through two command line arguments: --num_nodes and --num_gpus. For example, distributed training can be restricted to use only two nodes with the following command:

deepspeed --num_nodes=2 \
	<> <client args> \
	--deepspeed --deepspeed_config ds_config.json

You can instead include or exclude specific resources using the --include and --exclude flags. For example, to use all available resources except GPU 0 on node worker-2 and GPUs 0 and 1 on worker-3:

deepspeed --exclude="worker-2:0@worker-3:0,1" \
	<> <client args> \
	--deepspeed --deepspeed_config ds_config.json

Similarly, you can use only GPUs 0 and 1 on worker-2:

deepspeed --include="worker-2:0,1" \
	<> <client args> \
	--deepspeed --deepspeed_config ds_config.json

Multi-Node Environment Variables

When training across multiple nodes we have found it useful to support propagating user-defined environment variables. By default DeepSpeed will propagate all NCCL and PYTHON related environment variables that are set. If you would like to propagate additional variables you can specify them in a dot-file named .deepspeed_env that contains a new-line separated list of VAR=VAL entries. The DeepSpeed launcher will look in the local path you are executing from and also in your home directory (~/).

As a concrete example, some clusters require special NCCL variables to set prior to training. The user can simply add these variables to a .deepspeed_env file in their home directory that looks like this:


DeepSpeed will then make sure that these environment variables are set when launching each process on every node across their training job.

MPI and AzureML Compatibility

As described above, DeepSpeed provides its own parallel launcher to help launch multi-node/multi-gpu training jobs. If you prefer to launch your training job using MPI (e.g., mpirun), we provide support for this. It should be noted that DeepSpeed will still use the torch distributed NCCL backend and not the MPI backend.

To launch your training job with mpirun + DeepSpeed or with AzureML (which uses mpirun as a launcher backend) you simply need to install the mpi4py python package. DeepSpeed will use this to discover the MPI environment and pass the necessary state (e.g., world size, rank) to the torch distributed backend.

If you are using model parallelism, pipeline parallelism, or otherwise require torch.distributed calls before calling deepspeed.initialize(..) we provide the same MPI support with an additional DeepSpeed API call. Replace your initial torch.distributed.init_process_group(..) call with:


Resource Configuration (single-node)

In the case that we are only running on a single node (with one or more GPUs) DeepSpeed does not require a hostfile as described above. If a hostfile is not detected or passed in then DeepSpeed will query the number of GPUs on the local machine to discover the number of slots available. The --include and --exclude arguments work as normal, but the user should specify ‘localhost’ as the hostname.