## Retrieve an AutoScientist run

`autoscientist.get(strexperiment_id)  -> AutoscientistRun`

**get** `/api/v1/autoscientist/{experiment_id}`

Retrieve an AutoScientist run

### Parameters

- `experiment_id: str`

### Returns

- `class AutoscientistRun: …`

  - `id: str`

    AutoScientist run id.

  - `dataset_id: Optional[object]`

    Public id of the dataset the run trained on.

  - `model: Optional[object]`

    Resolved base model id used, including an automatically selected model.

  - `status: Literal["pending", "running", "succeeded", 2 more]`

    - `"pending"`

    - `"running"`

    - `"succeeded"`

    - `"failed"`

    - `"cancelled"`

  - `created_at: datetime`

  - `completed_at: Optional[object]`

  - `iterations_completed: float`

    Number of completed iterations.

  - `max_iterations: float`

    Resolved maximum number of iterations, including the model-specific default.

  - `target_win_rate: float`

    Resolved target win rate for the AutoScientist loop, including the model-specific default.

  - `best_win_rate: Optional[object]`

    Best win rate achieved so far.

  - `best_hyperparams: Optional[BestHyperparams]`

    Hyperparameters that produced the best iteration so far, and the ones the downloadable artifact was trained with. Null until an iteration completes.

    - `n_epochs: Optional[float]`

    - `batch_size: Optional[Union[Literal["max"], int, null]]`

      Either the literal 'max' or an integer of 1 or more. No fixed upper bound: the ceiling depends on the model and hardware, and a larger value is rejected at launch. Defaults to 'max'.

      - `Literal["max"]`

        - `"max"`

      - `int`

    - `learning_rate: Optional[float]`

    - `lora_r: Optional[float]`

    - `lora_alpha: Optional[float]`

      Must be 1× or 2× lora_r. Only checked when you supply lora_r in the same request; otherwise it is validated against the platform default.

    - `lora_dropout: Optional[float]`

    - `lora_trainable_modules: Optional[str]`

      'all-linear' or comma-separated module list (e.g. 'q_proj,v_proj').

    - `lr_scheduler_type: Optional[Literal["linear", "cosine", "constant"]]`

      - `"linear"`

      - `"cosine"`

      - `"constant"`

    - `min_lr_ratio: Optional[float]`

    - `scheduler_num_cycles: Optional[float]`

      Number of cosine decay cycles across training. Defaults to 0.5, a single half-cycle decay from the peak learning rate down to the floor.

    - `warmup_ratio: Optional[float]`

    - `max_grad_norm: Optional[float]`

    - `weight_decay: Optional[float]`

    - `train_on_inputs: Optional[bool]`

      Whether training loss is computed over the prompt tokens as well as the completion. `true` trains on the full sequence, `false` trains only on the completion. Omit to let the platform decide per example, which is the default.

    - `training_type: Optional[Literal["lora", "full"]]`

      Training strategy override. AutoScientist optimizes this by default; set it only when strictly necessary.

      - `"lora"`

      - `"full"`

  - `column_mapping: Optional[ColumnMapping]`

    Column mapping the run trained with, echoing what was sent on create. Null when the mapping was left to the platform to infer.

    - `prompt: Optional[str]`

      Source column holding the prompt/instruction text.

    - `completion: Optional[str]`

      Source column holding the target completion/response. Used for supervised (sft) training.

    - `reasoning_trace: Optional[str]`

      Optional source column holding a reasoning trace to train on.

  - `download_available: bool`

    True when the best trained artifact can be downloaded.

  - `error: Optional[object]`

    Why the run failed. Null unless `status` is `failed`.

### Example

```python
import os
from adaption import Adaption

client = Adaption(
    api_key=os.environ.get("ADAPTION_API_KEY"),  # This is the default and can be omitted
)
autoscientist_run = client.autoscientist.get(
    "experiment_id",
)
print(autoscientist_run.id)
```

#### Response

```json
{
  "id": "id",
  "dataset_id": {},
  "model": {},
  "status": "pending",
  "created_at": "2019-12-27T18:11:19.117Z",
  "completed_at": {},
  "iterations_completed": 0,
  "max_iterations": 0,
  "target_win_rate": 0,
  "best_win_rate": {},
  "best_hyperparams": {
    "n_epochs": 1,
    "batch_size": "max",
    "learning_rate": 0.00005,
    "lora_r": 8,
    "lora_alpha": 0,
    "lora_dropout": 0,
    "lora_trainable_modules": "q_proj,v_proj",
    "lr_scheduler_type": "linear",
    "min_lr_ratio": 0.1,
    "scheduler_num_cycles": 0.5,
    "warmup_ratio": 0.03,
    "max_grad_norm": 2,
    "weight_decay": 0,
    "train_on_inputs": false,
    "training_type": "lora"
  },
  "column_mapping": {
    "prompt": "prompt",
    "completion": "completion",
    "reasoning_trace": "reasoning_trace"
  },
  "download_available": true,
  "error": {}
}
```