Models
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Updated at March 26, 2024
While using Yandex DataSphere, a VM's memory stores the interpreter state, as well as computing and training results. You can save these computations to a separate resource named model.
In DataSphere, there are two types of models available:
- Models trained in projects.
- Foundation models tuned based on the Fine-tuning method.
Supported variable types
You can create a model based on different library types supported by serialzy
Library | Types | Data format |
---|---|---|
CatBoost |
CatBoostRegressor |
cbm |
CatBoost |
Pool |
quantized pool |
Tensorflow.Keras |
Sequential |
tf_keras |
TensorFlow |
Checkpoint |
tf_pure |
LightGBM |
LGBMClassifier |
lgbm |
XGBoost |
XGBClassifier |
xgb |
Torch |
Module |
pt |
ONNX |
ModelProto |
onnx |
Information about models as a resource
All information about models created in a project is available under Resources and in the JupyterLab right-hand menu in the Models tab.
The following information is stored about each model:
- Name.
- Name of the notebook the model was created in.
- Name of the variable the model was created from.
- Model size in bytes.
- Name of the user who created the model.
- Dataset creation date in UTC
format, e.g.,July 18, 2023, 14:23
.
To view model details, click its name in the project's model list.