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This commit is contained in:
Daniel Xu
parent
097e108902
commit
937c36e9b1
6 changed files with 109 additions and 106 deletions
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@ -25,7 +25,7 @@ result = api_future.result() # Blocks until complete
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```
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Args:
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T: The type of the result value
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- `T`: The type of the result value
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Example:
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```python
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@ -48,10 +48,10 @@ async def result_async(timeout: float | None = None) -> T
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Get the result asynchronously with optional timeout.
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Args:
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timeout: Maximum time to wait in seconds. None means wait indefinitely.
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- `timeout`: Maximum time to wait in seconds. None means wait indefinitely.
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Returns:
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The result value of type T
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- The result value of type `T`
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Raises:
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TimeoutError: If timeout is exceeded
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@ -66,10 +66,10 @@ def result(timeout: float | None = None) -> T
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Get the result synchronously with optional timeout.
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Args:
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timeout: Maximum time to wait in seconds. None means wait indefinitely.
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- `timeout`: Maximum time to wait in seconds. None means wait indefinitely.
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Returns:
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The result value of type T
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- The result value of type `T`
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Raises:
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TimeoutError: If timeout is exceeded
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@ -87,7 +87,7 @@ standard Future to be used in async contexts. It's commonly returned by
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Tinker API methods to provide both sync and async access patterns.
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Args:
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future: A concurrent.futures.Future to wrap
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- `future`: A concurrent.futures.Future to wrap
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Example:
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```python
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@ -111,10 +111,10 @@ def result(timeout: float | None = None) -> T
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Get the result synchronously with optional timeout.
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Args:
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timeout: Maximum time to wait in seconds. None means wait indefinitely.
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- `timeout`: Maximum time to wait in seconds. None means wait indefinitely.
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Returns:
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The result value of type T
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- The result value of type `T`
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Raises:
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TimeoutError: If timeout is exceeded
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@ -143,7 +143,7 @@ def future() -> ConcurrentFuture[T]
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Get the underlying concurrent.futures.Future.
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Returns:
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The wrapped ConcurrentFuture object
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- The wrapped `ConcurrentFuture` object
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Example:
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```python
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@ -24,7 +24,7 @@ Key methods:
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- unpublish_checkpoint_from_tinker_path() - unpublish a checkpoint to make it private
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Args:
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holder: Internal client managing HTTP connections and async operations
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- `holder`: Internal client managing HTTP connections and async operations
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Example:
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```python
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@ -49,10 +49,10 @@ def get_training_run(
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Get training run info.
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Args:
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training_run_id: The training run ID to get information for
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- `training_run_id`: The training run ID to get information for
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Returns:
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A Future containing the training run information
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- A `Future` containing the training run information
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Example:
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```python
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@ -83,10 +83,10 @@ def get_training_run_by_tinker_path(
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Get training run info.
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Args:
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tinker_path: The tinker path to the checkpoint
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- `tinker_path`: The tinker path to the checkpoint
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Returns:
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A Future containing the training run information
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- A `Future` containing the training run information
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Example:
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```python
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@ -116,10 +116,10 @@ def get_weights_info_by_tinker_path(
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Get checkpoint information from a tinker path.
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Args:
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tinker_path: The tinker path to the checkpoint
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- `tinker_path`: The tinker path to the checkpoint
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Returns:
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An APIFuture containing the checkpoint information. The future is awaitable.
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- An `APIFuture` containing the checkpoint information. The future is awaitable.
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Example:
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```python
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@ -141,11 +141,11 @@ def list_training_runs(
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List training runs with pagination support.
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Args:
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limit: Maximum number of training runs to return (default 20)
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offset: Offset for pagination (default 0)
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- `limit`: Maximum number of training runs to return (default 20)
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- `offset`: Offset for pagination (default 0)
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Returns:
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A Future containing the TrainingRunsResponse with training runs and cursor info
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- A `Future` containing the `TrainingRunsResponse` with training runs and cursor info
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Example:
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```python
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@ -181,10 +181,10 @@ def list_checkpoints(
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List available checkpoints (both training and sampler).
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Args:
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training_run_id: The training run ID to list checkpoints for
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- `training_run_id`: The training run ID to list checkpoints for
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Returns:
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A Future containing the CheckpointsListResponse with available checkpoints
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- A `Future` containing the `CheckpointsListResponse` with available checkpoints
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Example:
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```python
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@ -220,11 +220,11 @@ def get_checkpoint_archive_url(
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Get signed URL to download checkpoint archive.
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Args:
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training_run_id: The training run ID to download weights for
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checkpoint_id: The checkpoint ID to download
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- `training_run_id`: The training run ID to download weights for
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- `checkpoint_id`: The checkpoint ID to download
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Returns:
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A Future containing the CheckpointArchiveUrlResponse with signed URL and expiration
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- A `Future` containing the `CheckpointArchiveUrlResponse` with signed URL and expiration
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Example:
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```python
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@ -300,10 +300,10 @@ def get_checkpoint_archive_url_from_tinker_path(
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Get signed URL to download checkpoint archive.
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Args:
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tinker_path: The tinker path to the checkpoint
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- `tinker_path`: The tinker path to the checkpoint
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Returns:
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A Future containing the CheckpointArchiveUrlResponse with signed URL and expiration
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- A `Future` containing the `CheckpointArchiveUrlResponse` with signed URL and expiration
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#### `get_checkpoint_archive_url_from_tinker_path_async`
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@ -330,10 +330,10 @@ Only the exact owner of the training run can publish checkpoints.
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Published checkpoints can be unpublished using the unpublish_checkpoint_from_tinker_path method.
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Args:
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tinker_path: The tinker path to the checkpoint (e.g., "tinker://run-id/weights/0001")
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- `tinker_path`: The tinker path to the checkpoint (e.g., "tinker://run-id/weights/0001")
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Returns:
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A Future that completes when the checkpoint is published
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- A `Future` that completes when the checkpoint is published
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Raises:
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HTTPException: 400 if checkpoint identifier is invalid
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@ -372,10 +372,10 @@ Only the exact owner of the training run can unpublish checkpoints.
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This reverses the effect of publishing a checkpoint.
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Args:
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tinker_path: The tinker path to the checkpoint (e.g., "tinker://run-id/weights/0001")
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- `tinker_path`: The tinker path to the checkpoint (e.g., "tinker://run-id/weights/0001")
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Returns:
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A Future that completes when the checkpoint is unpublished
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- A `Future` that completes when the checkpoint is unpublished
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Raises:
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HTTPException: 400 if checkpoint identifier is invalid
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@ -417,11 +417,11 @@ sorted by time (newest first). It supports pagination for efficiently handling l
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numbers of checkpoints.
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Args:
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limit: Maximum number of checkpoints to return (default 100)
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offset: Offset for pagination (default 0)
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- `limit`: Maximum number of checkpoints to return (default 100)
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- `offset`: Offset for pagination (default 0)
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Returns:
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A Future containing the CheckpointsListResponse with checkpoints and cursor info
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- A `Future` containing the `CheckpointsListResponse` with checkpoints and cursor info
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Example:
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```python
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@ -458,10 +458,10 @@ def get_session(session_id: str) -> ConcurrentFuture[types.GetSessionResponse]
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Get session information including all training runs and samplers.
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Args:
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session_id: The session ID to get information for
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- `session_id`: The session ID to get information for
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Returns:
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A Future containing the GetSessionResponse with training_run_ids and sampler_ids
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- A `Future` containing the `GetSessionResponse` with training_run_ids and sampler_ids
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Example:
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```python
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@ -493,11 +493,11 @@ def list_sessions(
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List sessions with pagination support.
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Args:
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limit: Maximum number of sessions to return (default 20)
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offset: Offset for pagination (default 0)
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- `limit`: Maximum number of sessions to return (default 20)
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- `offset`: Offset for pagination (default 0)
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Returns:
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A Future containing the ListSessionsResponse with list of session IDs
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- A `Future` containing the `ListSessionsResponse` with list of session IDs
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Example:
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```python
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@ -528,10 +528,10 @@ def get_sampler(sampler_id: str) -> APIFuture[types.GetSamplerResponse]
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Get sampler information.
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Args:
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sampler_id: The sampler ID (sampling_session_id) to get information for
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- `sampler_id`: The sampler ID (sampling_session_id) to get information for
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Returns:
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An APIFuture containing the GetSamplerResponse with sampler details
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- An `APIFuture` containing the `GetSamplerResponse` with sampler details
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Example:
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```python
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@ -18,10 +18,10 @@ Key methods:
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- compute_logprobs() - get log probabilities for prompt tokens
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Args:
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holder: Internal client managing HTTP connections and async operations
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model_path: Path to saved model weights (starts with 'tinker://')
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base_model: Name of base model to use for inference
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retry_config: Configuration for retrying failed requests
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- `holder`: Internal client managing HTTP connections and async operations
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- `model_path`: Path to saved model weights (starts with 'tinker://')
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- `base_model`: Name of base model to use for inference
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- `retry_config`: Configuration for retrying failed requests
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Example:
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```python
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@ -48,14 +48,14 @@ def sample(
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Generate text completions from the model.
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Args:
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prompt: The input tokens as ModelInput
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num_samples: Number of independent samples to generate
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sampling_params: Parameters controlling generation (temperature, max_tokens, etc.)
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include_prompt_logprobs: Whether to include log probabilities for prompt tokens
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topk_prompt_logprobs: Number of top token log probabilities to return per position
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- `prompt`: The input tokens as ModelInput
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- `num_samples`: Number of independent samples to generate
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- `sampling_params`: Parameters controlling generation (temperature, max_tokens, etc.)
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- `include_prompt_logprobs`: Whether to include log probabilities for prompt tokens
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- `topk_prompt_logprobs`: Number of top token log probabilities to return per position
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Returns:
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A Future containing the SampleResponse with generated text
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- A `Future` containing the `SampleResponse` with generated text
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Example:
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```python
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@ -90,10 +90,10 @@ def compute_logprobs(
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Compute log probabilities for prompt tokens.
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Args:
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prompt: The input tokens as ModelInput
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- `prompt`: The input tokens as ModelInput
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Returns:
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A Future containing a list of log probabilities for each token in the prompt.
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- A `Future` containing a list of log probabilities for each token in the prompt.
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None values indicate tokens where log probabilities couldn't be computed.
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Example:
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@ -20,14 +20,17 @@ Args:
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Example:
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```python
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# Near instant
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client = ServiceClient()
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# ^^^ near-instant
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# Takes a moment as we initialize the model and assign resources
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training_client = client.create_lora_training_client(base_model="Qwen/Qwen3-8B")
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# ^^^ takes a moment as we initialize the model and assign resources
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# Near-instant
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sampling_client = client.create_sampling_client(base_model="Qwen/Qwen3-8B")
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# ^^^ near-instant
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# Near-instant
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rest_client = client.create_rest_client()
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# ^^^ near-instant
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```
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#### `get_server_capabilities`
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@ -41,7 +44,7 @@ def get_server_capabilities() -> types.GetServerCapabilitiesResponse
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Query the server's supported features and capabilities.
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Returns:
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GetServerCapabilitiesResponse with available models, features, and limits
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- `GetServerCapabilitiesResponse` with available models, features, and limits
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Example:
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```python
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@ -78,16 +81,16 @@ def create_lora_training_client(
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Create a TrainingClient for LoRA fine-tuning.
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Args:
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base_model: Name of the base model to fine-tune (e.g., "Qwen/Qwen2.5-7B")
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rank: LoRA rank controlling the size of adaptation matrices (default 32)
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seed: Random seed for initialization. None means random seed.
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train_mlp: Whether to train MLP layers (default True)
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train_attn: Whether to train attention layers (default True)
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train_unembed: Whether to train unembedding layers (default True)
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user_metadata: Optional metadata to attach to the training run
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- `base_model`: Name of the base model to fine-tune (e.g., "Qwen/Qwen2.5-7B")
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- `rank`: LoRA rank controlling the size of adaptation matrices (default 32)
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- `seed`: Random seed for initialization. None means random seed.
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- `train_mlp`: Whether to train MLP layers (default True)
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- `train_attn`: Whether to train attention layers (default True)
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- `train_unembed`: Whether to train unembedding layers (default True)
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- `user_metadata`: Optional metadata to attach to the training run
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Returns:
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TrainingClient configured for LoRA training
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- `TrainingClient` configured for LoRA training
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Example:
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```python
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@ -129,11 +132,11 @@ def create_training_client_from_state(
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Create a TrainingClient from saved model weights.
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Args:
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path: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
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user_metadata: Optional metadata to attach to the new training run
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- `path`: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
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- `user_metadata`: Optional metadata to attach to the new training run
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Returns:
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TrainingClient loaded with the specified weights
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- `TrainingClient` loaded with the specified weights
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Example:
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```python
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@ -168,12 +171,12 @@ def create_sampling_client(
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Create a SamplingClient for text generation.
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Args:
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model_path: Path to saved model weights (e.g., "tinker://run-id/weights/checkpoint-001")
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base_model: Name of base model to use (e.g., "Qwen/Qwen2.5-7B")
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retry_config: Optional configuration for retrying failed requests
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- `model_path`: Path to saved model weights (e.g., "tinker://run-id/weights/checkpoint-001")
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- `base_model`: Name of base model to use (e.g., "Qwen/Qwen2.5-7B")
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- `retry_config`: Optional configuration for retrying failed requests
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Returns:
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SamplingClient configured for text generation
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- `SamplingClient` configured for text generation
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Raises:
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ValueError: If neither model_path nor base_model is provided
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@ -216,7 +219,7 @@ The RestClient provides access to various REST endpoints for querying
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model information, checkpoints, sessions, and managing checkpoint visibility.
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Returns:
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RestClient for accessing REST API endpoints
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- `RestClient` for accessing REST API endpoints
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Example:
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```python
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@ -18,8 +18,8 @@ Key methods:
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- save_weights_and_get_sampling_client() - export trained model for inference
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Args:
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holder: Internal client managing HTTP connections and async operations
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model_id: Unique identifier for the model to train. Required for training operations.
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- `holder`: Internal client managing HTTP connections and async operations
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- `model_id`: Unique identifier for the model to train. Required for training operations.
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Example:
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```python
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@ -45,12 +45,12 @@ def forward(
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Compute forward pass without gradients.
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Args:
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data: List of training data samples
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loss_fn: Loss function type (e.g., "cross_entropy")
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loss_fn_config: Optional configuration for the loss function
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- `data`: List of training data samples
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- `loss_fn`: Loss function type (e.g., "cross_entropy")
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- `loss_fn_config`: Optional configuration for the loss function
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Returns:
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APIFuture containing the forward pass outputs and loss
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- `APIFuture` containing the forward pass outputs and loss
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Example:
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```python
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@ -89,12 +89,12 @@ def forward_backward(
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Compute forward pass and backward pass to calculate gradients.
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Args:
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data: List of training data samples
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loss_fn: Loss function type (e.g., "cross_entropy")
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loss_fn_config: Optional configuration for the loss function
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- `data`: List of training data samples
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- `loss_fn`: Loss function type (e.g., "cross_entropy")
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- `loss_fn_config`: Optional configuration for the loss function
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Returns:
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APIFuture containing the forward/backward outputs, loss, and gradients
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- `APIFuture` containing the forward/backward outputs, loss, and gradients
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Example:
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```python
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@ -143,11 +143,11 @@ Allows you to define custom loss functions that operate on log probabilities.
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The custom function receives logprobs and computes loss and gradients.
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Args:
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data: List of training data samples
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loss_fn: Custom loss function that takes (data, logprobs) and returns (loss, metrics)
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- `data`: List of training data samples
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- `loss_fn`: Custom loss function that takes (data, logprobs) and returns (loss, metrics)
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Returns:
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APIFuture containing the forward/backward outputs with custom loss
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- `APIFuture` containing the forward/backward outputs with custom loss
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Example:
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```python
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@ -185,10 +185,10 @@ def optim_step(
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Update model parameters using Adam optimizer.
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||||
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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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue