---
title: "DeepSpeed Configuration JSON"
---

### Batch Size Related Parameters

**Note:** configuring ***train\_batch\_size*** is required.
{: .notice--warning}

***train\_batch\_size***: [integer]

| Value                                                        | Example |
| ------------------------------------------------------------ | ------- |
| The effective training batch size. This is the amount of data samples that leads to one step of model update. ***train\_batch\_size*** is aggregated by the batch size that a single GPU processes in one forward/backward pass (a.k.a., ***train\_step\_batch\_size***),  the gradient accumulation steps (a.k.a., ***gradient\_accumulation\_steps***), and the number of GPUs. | `32`      |


***train\_micro\_batch\_size\_per\_gpu***: [integer]

| Description                                                  | Default                      |
| ------------------------------------------------------------ | ---------------------------- |
| Batch size to be processed by one GPU in one step (without gradient accumulation). When specified, ***gradient\_accumulation\_steps*** is automatically calculated using ***train\_batch\_size*** and number of GPUs. Should not be concurrently specified with ***gradient\_accumulation\_steps*** in the configuration JSON. | ***train\_batch\_size*** value |

***gradient\_accumulation\_steps***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Number of training steps to accumulate gradients before averaging and applying them. This feature is sometimes useful to improve scalability since it results in less frequent communication of gradients between steps. Another impact of this feature is the ability to train with larger batch sizes per GPU. When specified, ***train\_step\_batch\_size*** is automatically calculated using ***train\_batch\_size*** and number of GPUs. Should not be concurrently specified with ***train\_step\_batch\_size*** in the configuration JSON. | `1`       |



### Optimizer Parameters

***optimizer***: [dictionary]

| Fields | Value                                                        | Example                        |
| ------ | ------------------------------------------------------------ | ------------------------------ |
| type   | The optimizer name. DeepSpeed natively supports Adam and LAMB optimizers and will import other optimizers from [torch](https://pytorch.org/docs/stable/optim.html). | `"Adam"`                         |
| params | Dictionary of parameters to instantiate optimizer. The parameter names must match the optimizer constructor signature (e.g., for [Adam](https://pytorch.org/docs/stable/optim.html#torch.optim.Adam)). | `{"lr": 0.001, "eps": 1e-8}` |

  Example of ***optimizer***

```json
"optimizer": {
    "type": "Adam",
    "params": {
      "lr": 0.001,
      "betas": [
        0.8,
        0.999
      ],
      "eps": 1e-8,
      "weight_decay": 3e-7
    }
  }
```

### Scheduler Parameters

***scheduler***: [dictionary]

| Fields | Value                                                        | Example                        |
| ------ | ------------------------------------------------------------ | ------------------------------ |
| type   | The scheduler name. See [here](https://deepspeed.readthedocs.io/en/latest/deepspeed.pt.html) for list of support schedulers. | `"1Cycle"`                      |
| params | Dictionary of parameters to instantiate scheduler. The parameter names should match scheduler constructor signature. | `{"lr": 0.001, "eps": 1e-8}` |

Example of ***scheduler***

```json
 "scheduler": {
      "type": "WarmupLR",
      "params": {
          "warmup_min_lr": 0,
          "warmup_max_lr": 0.001,
          "warmup_num_steps": 1000
      }
  }  
```

### Communication options

***fp32\_allreduce***: [boolean]

| Description                          | Default |
| ------------------------------------ | ------- |
| During gradient averaging perform allreduce with 32 bit values | `false`   |

***prescale\_gradients***: [boolean]

| Description                            | Default |
| -------------------------------------- | ------- |
| Scale gradients before doing allreduce | `false`   |

***gradient_predivide_factor***: [float]

| Description                  | Default |
| ---------------------------- | ------- |
| Before gradient averaging predivide gradients by a specified factor, can sometimes help with fp16 stability when scaling to large numbers of GPUs | `1.0`

***sparse\_gradients***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Enable sparse compression of [torch.nn.Embedding](https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding) gradients. | `false`    |

### FP16 training options

***fp16***: [dictionary]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Configuration for using mixed precision/FP16 training that leverages [NVIDIA's Apex package](https://nvidia.github.io/apex/). An example, including the available dictionary keys is illustrated below. | None    |

```json
"fp16": {
    "enabled": true,
    "loss_scale": 0,
    "initial_scale_power": 32,
    "loss_scale_window": 1000,
    "hysteresis": 2,
 	"min_loss_scale": 1
}
```

***fp16:enabled***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| ***enabled*** is a **fp16** parameter indicating whether or not FP16 training enabled. | `false`   |

***fp16:loss\_scale***: [float]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| ***loss\_scale*** is a ***fp16*** parameter representing the loss scaling value for FP16 training. The default value of 0.0 results in dynamic loss scaling, otherwise the value will be used for static fixed loss scaling. | `0.0`     |

***fp16:initial\_scale\_power***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| ***initial\_loss\_scale\_power*** is a **fp16** parameter representing the power of the initial dynamic loss scale value. The actual loss scale is computed as 2<sup>***initial\_loss\_scale\_power***</sup>. | `32`      |

***fp16:loss\_scale\_window***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| ***loss\_scale\_window*** is a **fp16** parameter representing the window over which to raise/lower the dynamic loss scale value. | `1000`    |

***fp16:hysteresis***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| ***hysteresis*** is a **fp16** parameter representing the delay shift in dynamic loss scaling. | `2`       |

***fp16:min\_loss\_scale***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| ***min\_loss\_scale*** is  a **fp16** parameter representing the minimum dynamic loss scale value. | `1000`    |

### Gradient Clipping

***gradient\_clipping***: [float]

| Description                         | Default |
| ----------------------------------- | ------- |
| Enable gradient clipping with value | `0`      |



### ZeRO Optimizations for FP16 Training

Enabling and configure ZeRO memory optimizations
```json
  "zero_optimization": {
    "stage": [0|1|2],
    "allgather_partitions": [true|false],
    "allgather_bucket_size": 500000000,
    "reduce_scatter": [true|false],
    "reduce_bucket_size": 500000000,
    "contiguous_gradients" : [true|false]
    }
```

***zero\_optimization***: [dictionary]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Enable ZeRO memory optimization wrapper for FP16 Training. Currently compatible only with Adam optimizer. | `false`   |

***stage***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Chooses different stages of ZeRO Optimizer. Stage 0, 1, and 2 refer to disabled, optimizer state partitioning, and optimizer+gradient state partitiong, respectively. | `0`   |

***allgather_partitions***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Chooses between allgather collective or a series of broadcast collectives to gather updated parameters from all the GPUs at the end of each step  | `true`   |

***allgather_bucket_size***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Number of elements allgathered at a time. Limits the memory required for the allgather for large model sizes   | `500000000`   |

***reduce_scatter***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Uses reduce or reduce scatter instead of allreduce to average gradients   | `true`   |

***reduce_bucket_size***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Number of elements reduced/allreduced at a time. Limits the memory required for the allgather for large model sizes   | `500000000`   |

***contiguous_gradients***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Copies the gradients to a contiguous buffer as they are produced. Avoids memory fragmentation during backward pass. Only useful when running very large models.   | `False`   |



### Logging

***steps\_per\_print***: [integer]

| Description | Default |
| ----------- | ------- |
| Print train loss every N steps | `10` |

***wall\_clock\_breakdown***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Enable timing of the latency of forward/backward/update training phases | `false`   |

***dump_state***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Print out state information of DeepSpeed object after initialization | `false`   |

### Activation Checkpointing
```json
  "activation_checkpointing": {
    "partition_activations": false,
    "cpu_checkpointing": false,
    "contiguous_memory_optimization": false,
    "number_checkpoints": null,
    "synchronize_checkpoint_boundary": false,
    "profile": false
    }
```
***partition\_activations***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Enables partition activation when used with model parallelism | `false`   |

***cpu\_checkpointing***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Offloads partitioned activations to CPU if partition_activations is enabled| `false`   |


***contiguous\_memory\_optimization***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Copies partitioned activations so that they are contiguous in memory | `false`   |

***number_checkpoints***: [integer]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Total number of activation checkpoints used to allocate memory buffer for contiguous_memoty_optimization | `None`   |

***synchronize\_checkpoint\_boundary***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Inserts torch.cuda.synchronize() at each checkpoint boundary. | `false`   |


***profile***: [boolean]

| Description                                                  | Default |
| ------------------------------------------------------------ | ------- |
| Logs the forward and backward time for each checkpoint function | `false`   |
