Principle:Togethercomputer Together python Fine Tuning Job Monitoring
| Attribute | Value |
|---|---|
| Principle Name | Fine_Tuning_Job_Monitoring |
| Overview | Pattern for tracking progress and status of fine-tuning jobs through polling and event retrieval. |
| Domain | MLOps, Fine_Tuning |
| Repository | togethercomputer/together-python |
| Last Updated | 2026-02-15 16:00 GMT |
Description
Job monitoring provides a set of APIs to observe and manage the lifecycle of fine-tuning jobs after creation. Together AI fine-tuning jobs are asynchronous operations that can take minutes to hours depending on model size, dataset size, and training configuration. The monitoring capabilities include:
Job Status Retrieval
The retrieve() method fetches the current state of a fine-tuning job, returning detailed information about the job configuration and its execution status. Job statuses progress through states such as "pending", "queued", "running", "completed", "failed", and "cancelled".
Training Event Streaming
The list_events() method returns the sequence of training events for a job, which typically include per-step metrics such as training loss, learning rate, and gradient norms. These events enable:
- Progress tracking -- Monitoring how many training steps have completed.
- Quality assessment -- Observing the loss curve to detect convergence, divergence, or overfitting.
- Debugging -- Identifying training issues such as exploding gradients or learning rate problems.
Checkpoint Listing
The list_checkpoints() method returns all available checkpoints for a job, including both intermediate checkpoints saved during training and the final checkpoint. Each checkpoint has a name (in "ft-id:step" format for intermediate checkpoints), a type, and a timestamp. Checkpoints are sorted by timestamp (most recent first).
Job Cancellation
The cancel() method terminates a running fine-tuning job. This is useful for stopping jobs that show poor training dynamics (e.g., loss divergence) to avoid unnecessary compute costs.
Job Listing and Deletion
The list() method provides a history of all fine-tuning jobs, while delete() removes a job record with an optional force flag.
Usage
Use this principle after creating a fine-tuning job to track its progress and detect completion or failure. A typical monitoring workflow:
- Create a fine-tuning job and capture the job ID.
- Poll
retrieve()periodically to check the job status. - Use
list_events()to inspect training metrics. - When the job completes, use the output model name for inference or download.
- If the job shows poor convergence, use
cancel()to stop it early.
For jobs with multiple checkpoints, list_checkpoints() enables selecting specific intermediate checkpoints for download or continued training.
Theoretical Basis
Monitoring is essential in the MLOps lifecycle because fine-tuning outcomes are not guaranteed. Training loss curves reveal whether the model is:
- Converging -- Loss consistently decreases, indicating successful learning.
- Overfitting -- Training loss decreases but validation loss increases, suggesting the model is memorizing rather than generalizing.
- Diverging -- Loss increases or oscillates wildly, indicating learning rate or data issues.
Checkpoint-based monitoring enables early stopping -- a practitioner can cancel a job that has plateaued and use the best intermediate checkpoint rather than waiting for training to complete.
The event-based approach (rather than streaming) follows a pull-based architecture where clients request the current state on demand. This is simpler and more reliable than long-lived streaming connections, though it means clients must implement their own polling loops for real-time monitoring.