> ## Documentation Index > Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt > Use this file to discover all available pages before exploring further. # Cookbook Reference > Configuration classes, checkpoint utilities, and gradient accumulation normalization for cookbook recipes. ## InfraConfig GPU, region, and training shape settings. Wraps `TrainerJobConfig` fields: ```python theme={null} from training.utils import InfraConfig infra = InfraConfig( training_shape_id="accounts/fireworks/trainingShapes/qwen3-8b-128k-h200", ref_training_shape_id="accounts/fireworks/trainingShapes/qwen3-8b-128k-h200-forward", ) ``` Use `training_shape_id` for every launched trainer in the cookbook. In normal usage, this is the only shape-specific value you set. In most cases, pass the full shared path `accounts/fireworks/trainingShapes/`. The `fireworks` account is the public shared shape catalog. Add `ref_training_shape_id` when the recipe also launches a reference trainer. | Field | Type | Default | Description | | ----------------------- | ------------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `training_shape_id` | `str \| None` | `None` | Required full training-shape ID for the policy trainer, typically `accounts/fireworks/trainingShapes/`. The cookbook resolves the versioned reference for you and auto-populates shape-owned infra. | | `ref_training_shape_id` | `str \| None` | `None` | Optional full training-shape ID for the reference trainer, also typically under `accounts/fireworks/trainingShapes/`. When unset, `rl_loop` skips reference-model provisioning. | | `region` | `str \| None` | `None` | Region override | | `trainer_timeout_s` | `float` | `3600` | Timeout for trainer provisioning / readiness waits | | `extra_args` | `list[str] \| None` | `None` | Extra trainer arguments | `accelerator_type`, `accelerator_count`, `node_count`, and `custom_image_tag` are internal development fields automatically configured by the training shape. They are not user-configurable. ## DeployConfig Deployment settings for sampling and weight sync. Wraps `DeploymentConfig` fields: ```python theme={null} from training.utils import DeployConfig deploy_cfg = DeployConfig( deployment_id="grpo-serving", tokenizer_model="Qwen/Qwen3-8B", ) ``` When `deployment_shape` is set (the recommended path), the shape owns deployment hardware and serving configuration. | Field | Type | Default | Description | | ------------------------------ | ------------------------ | ------------- | ---------------------------------------------------------------------------------------------------------------------------- | | `deployment_id` | `str \| None` | `None` | Deployment identifier. If unset, the cookbook auto-derives one from the base model name. | | `tokenizer_model` | `str \| None` | `None` | HuggingFace model name for client-side tokenization. Required for RL sampling. | | `deployment_shape` | `str \| None` | `None` | Deployment shape resource name. When set, the shape owns GPU type and serving config. | | `deployment_region` | `str \| None` | `None` | Region override for the deployment | | `hot_load_bucket_type` | `str` | `"FW_HOSTED"` | Weight-sync storage backend | | `hot_load_trainer_job` | `str \| None` | `None` | Trainer job name whose weight-sync bucket this deployment should use. Format: `accounts/{account}/rlorTrainerJobs/{job_id}`. | | `deployment_timeout_s` | `float` | `5400` | Timeout for deployment provisioning / readiness waits | | `deployment_extra_args` | `list[str] \| None` | `None` | Extra serving arguments | | `sample_timeout` | `int` | `600` | HTTP read timeout for sampling completions | | `disable_speculative_decoding` | `bool` | `True` | Disable speculative decoding for weight-sync compatibility | | `extra_values` | `dict[str, str] \| None` | `None` | Extra deployment Helm values | | `replica_count` | `int \| None` | `None` | If set, pin the deployment to a fixed replica count (sets both min and max). | `deployment_accelerator_type` is an internal development field automatically configured by the deployment shape. It is not user-configurable. ## WeightSyncConfig Checkpoint and weight-sync intervals: ```python theme={null} from training.utils import WeightSyncConfig weight_sync = WeightSyncConfig( weight_sync_interval=1, dcp_save_interval=10, ) ``` `dcp_save_interval` defaults to `0` (off). Without setting it to a positive value, **no DCP checkpoints are saved and training cannot be resumed**. If you need checkpoint-based resume, explicitly set `dcp_save_interval` (e.g. `dcp_save_interval=50`). | Field | Type | Default | Description | | ----------------------------- | ------ | -------- | -------------------------------------------------------------------------------------------------------------------- | | `dcp_save_interval` | `int` | `0` | Save DCP checkpoints for resume every N steps. `0` disables DCP saves. **Set to a positive value to enable resume.** | | `weight_sync_interval` | `int` | `1` | Save + sync sampler weights every N optimizer steps. `0` disables weight sync. | | `dcp_timeout` | `int` | `2700` | Timeout for DCP save/load operations | | `first_checkpoint_type` | `str` | `"base"` | First sampler checkpoint type passed to `WeightSyncer` | | `weight_sync_before_training` | `bool` | `False` | Save a base checkpoint and sync it to the deployment before the first training step | | `weight_sync_timeout` | `int` | `600` | Timeout for each weight-sync operation | ## WandBConfig Weights & Biases logging settings: ```python theme={null} from training.utils import WandBConfig wandb = WandBConfig( entity="my-team", project="grpo-experiment", run_name="qwen3-8b-v1", ) ``` | Field | Type | Default | Description | | ---------- | ------------- | ------- | ------------------------------------ | | `entity` | `str \| None` | `None` | W\&B team or user name | | `project` | `str \| None` | `None` | W\&B project name | | `run_name` | `str \| None` | `None` | Run name (auto-generated if omitted) | ## ReconnectableClient Blocking convenience wrapper around `FiretitanTrainingClient`. All cookbook recipes use this as their training client — it dispatches each call and blocks until the result is ready or the timeout expires. Failures propagate to the caller so the training loop can crash cleanly and resume from the last DCP checkpoint. ```python theme={null} from training.utils import ReconnectableClient client = ReconnectableClient( rlor_mgr=rlor_mgr, job_id=endpoint.job_id, base_model="accounts/fireworks/models/qwen3-8b", lora_rank=0, fw_api_key=api_key, ) result = client.forward_backward_custom(datums, loss_fn) client.optim_step(tinker.AdamParams(...)) ``` | Parameter | Type | Default | Description | | ----------------- | -------------------------------- | ------- | ------------------------------------------------------------- | | `rlor_mgr` | `TrainerJobManager` | — | Manager used to connect to the trainer | | `job_id` | `str` | — | RLOR trainer job ID | | `base_model` | `str` | — | Base model name | | `lora_rank` | `int` | `0` | LoRA rank (`0` for full-parameter) | | `fw_api_key` | `str \| None` | `None` | Fireworks API key (falls back to `FIREWORKS_API_KEY` env var) | | `default_timeout` | `int` | `600` | Timeout in seconds for forward/backward/optim calls | | `endpoint` | `TrainerServiceEndpoint \| None` | `None` | Pre-resolved endpoint (skips `wait_for_existing` on init) | **Properties:** | Property | Type | Description | | ---------- | ------------------------- | ------------------------------------------------------------- | | `inner` | `FiretitanTrainingClient` | The underlying API client (for advanced use) | | `endpoint` | `TrainerServiceEndpoint` | The resolved trainer endpoint (base\_url, job\_id, job\_name) | | `job_id` | `str` | The trainer job ID | **Methods:** | Method | Description | | -------------------------------------------------------------- | -------------------------------------------- | | `forward(data, loss_fn)` | Forward pass, blocks until complete | | `forward_backward(data, loss_fn, loss_fn_config)` | Forward + backward pass | | `forward_backward_custom(data, loss_fn)` | Forward + backward with custom loss function | | `optim_step(params, grad_accumulation_normalization)` | Optimizer step | | `save_state(name, timeout)` | Save DCP checkpoint (default timeout: 2700s) | | `load_state_with_optimizer(path, timeout)` | Load DCP checkpoint (default timeout: 2700s) | | `save_weights_for_sampler_ext(name, checkpoint_type, timeout)` | Save sampler checkpoint for promotion | | `resolve_checkpoint_path(name, source_job_id)` | Resolve cross-job checkpoint path | | `list_checkpoints()` | List available DCP checkpoints | ## Checkpoint utilities For checkpointing, resume, and promote — see the dedicated [Checkpoints and Resume](/fine-tuning/training-api/cookbook/checkpoints) page. ## Gradient accumulation normalization Recipe configs expose `grad_accumulation_normalization`, which is passed to `optim_step(...)`: ```python theme={null} client.optim_step(adam_params, grad_accumulation_normalization="num_loss_tokens") ``` See [Loss Functions](/fine-tuning/training-api/loss-functions#gradient-accumulation-normalization) for how to choose the mode and avoid double-normalization. ### Recipe defaults | Recipe | Default | Rationale | | --------- | ------------------- | ------------------------------------------------------------- | | SFT | `None` | The SFT loss is already normalized client-side. | | GRPO / RL | `"num_loss_tokens"` | RL losses use server-side per-token normalization by default. | | DPO | `None` | The DPO loss is already normalized client-side. | | ORPO | `None` | The ORPO loss is already normalized client-side. | The cookbook reference documents the config surface and defaults. The conceptual guidance for loss reduction vs. server-side normalization now lives in [Loss Functions](/fine-tuning/training-api/loss-functions#gradient-accumulation-normalization).