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docs/api/trainingclient.md
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@ -18,8 +18,8 @@ Key methods:
- save_weights_and_get_sampling_client() - export trained model for inference
Args:
holder: Internal client managing HTTP connections and async operations
model_id: Unique identifier for the model to train. Required for training operations.
- `holder`: Internal client managing HTTP connections and async operations
- `model_id`: Unique identifier for the model to train. Required for training operations.
Example:
```python
@ -45,12 +45,12 @@ def forward(
Compute forward pass without gradients.
Args:
data: List of training data samples
loss_fn: Loss function type (e.g., "cross_entropy")
loss_fn_config: Optional configuration for the loss function
- `data`: List of training data samples
- `loss_fn`: Loss function type (e.g., "cross_entropy")
- `loss_fn_config`: Optional configuration for the loss function
Returns:
APIFuture containing the forward pass outputs and loss
- `APIFuture` containing the forward pass outputs and loss
Example:
```python
@ -89,12 +89,12 @@ def forward_backward(
Compute forward pass and backward pass to calculate gradients.
Args:
data: List of training data samples
loss_fn: Loss function type (e.g., "cross_entropy")
loss_fn_config: Optional configuration for the loss function
- `data`: List of training data samples
- `loss_fn`: Loss function type (e.g., "cross_entropy")
- `loss_fn_config`: Optional configuration for the loss function
Returns:
APIFuture containing the forward/backward outputs, loss, and gradients
- `APIFuture` containing the forward/backward outputs, loss, and gradients
Example:
```python
@ -143,11 +143,11 @@ Allows you to define custom loss functions that operate on log probabilities.
The custom function receives logprobs and computes loss and gradients.
Args:
data: List of training data samples
loss_fn: Custom loss function that takes (data, logprobs) and returns (loss, metrics)
- `data`: List of training data samples
- `loss_fn`: Custom loss function that takes (data, logprobs) and returns (loss, metrics)
Returns:
APIFuture containing the forward/backward outputs with custom loss
- `APIFuture` containing the forward/backward outputs with custom loss
Example:
```python
@ -185,10 +185,10 @@ def optim_step(
Update model parameters using Adam optimizer.
Args:
adam_params: Adam optimizer parameters (learning_rate, betas, eps, weight_decay)
- `adam_params`: Adam optimizer parameters (learning_rate, betas, eps, weight_decay)
Returns:
APIFuture containing optimizer step response
- `APIFuture` containing optimizer step response
Example:
```python
@ -227,10 +227,10 @@ def save_state(name: str) -> APIFuture[types.SaveWeightsResponse]
Save model weights to persistent storage.
Args:
name: Name for the saved checkpoint
- `name`: Name for the saved checkpoint
Returns:
APIFuture containing the save response with checkpoint path
- `APIFuture` containing the save response with checkpoint path
Example:
```python
@ -258,10 +258,10 @@ def load_state(path: str) -> APIFuture[types.LoadWeightsResponse]
Load model weights from a saved checkpoint.
Args:
path: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
- `path`: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
Returns:
APIFuture containing the load response
- `APIFuture` containing the load response
Example:
```python
@ -290,10 +290,10 @@ def load_state_with_optimizer(
Load model weights and optimizer state from a checkpoint.
Args:
path: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
- `path`: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
Returns:
APIFuture containing the load response
- `APIFuture` containing the load response
Example:
```python
@ -325,10 +325,10 @@ def save_weights_for_sampler(
Save model weights for use with a SamplingClient.
Args:
name: Name for the saved sampler weights
- `name`: Name for the saved sampler weights
Returns:
APIFuture containing the save response with sampler path
- `APIFuture` containing the save response with sampler path
Example:
```python
@ -363,7 +363,7 @@ def get_info() -> types.GetInfoResponse
Get information about the current model.
Returns:
GetInfoResponse with model configuration and metadata
- `GetInfoResponse` with model configuration and metadata
Example:
```python
@ -393,7 +393,7 @@ def get_tokenizer() -> PreTrainedTokenizer
Get the tokenizer for the current model.
Returns:
PreTrainedTokenizer compatible with the model
- `PreTrainedTokenizer` compatible with the model
Example:
```python
@ -414,11 +414,11 @@ def create_sampling_client(
Create a SamplingClient from saved weights.
Args:
model_path: Tinker path to saved weights
retry_config: Optional configuration for retrying failed requests
- `model_path`: Tinker path to saved weights
- `retry_config`: Optional configuration for retrying failed requests
Returns:
SamplingClient configured with the specified weights
- `SamplingClient` configured with the specified weights
Example:
```python
@ -451,11 +451,11 @@ def save_weights_and_get_sampling_client(
Save current weights and create a SamplingClient for inference.
Args:
name: Optional name for the saved weights (currently ignored for ephemeral saves)
retry_config: Optional configuration for retrying failed requests
- `name`: Optional name for the saved weights (currently ignored for ephemeral saves)
- `retry_config`: Optional configuration for retrying failed requests
Returns:
SamplingClient configured with the current model weights
- `SamplingClient` configured with the current model weights
Example:
```python