How It Works
Models enable you to store a job's model directory permanently to reuse as the starting point for the model directory for subsequent jobs. Models only will incur storage charges for their size, which is also included in the 50 GB free storage option. When a model is used in a job, the job's working directory space must be sufficient to support the model size.
Models are immutable once created. When a model is used in a job, the contents can be modified while that job worker is running, but any changes do not affect the original model or any other jobs using that model. In order to save the modifications, you must save the job as a new model. The maximum size of any model is 50 GB, but you can have unlimited models.
Creating a Model
Models can be created from three different sources: external, notebooks, and training jobs.
External Model Source
To create a model from an external sources, navigate to the Models Dashboard from the side navigation and click the
Create button. Fill out the form with the necessary information and click
Create. Once the model changes to the status
ready, it can be used on new jobs.
To create a model from an existing notebook, select the notebook from the Notebook Dashboard and click
Copy button is only enabled when a single notebook is selected and that notebook is either
Save as Model as the
Copy Type. Enter the name for the new model in the
Name field and click
Copy. You will be automatically navigated to the models dashboard where you can monitor the progress of the model creation. The model will then be populated from the current contents of the
/opt/trainml/models directory inside the notebook instance.
Training jobs can be configured to send their output to a trainML model instead of an external source. To create a model from a training job, select
trainML as the
Output Type in the data section of the job form. When this option is selected, the
TRAINML_OUTPUT_PATH environment variable is redirected to the same location as
/opt/trainml/models). Once each worker in the training job finished, it will save the entire directory structure of
/opt/trainml/models to a new model with the name
Job - <job name> if there is one worker or
Job - <job name> Worker <worker number> if there are multiple workers.
Using a Model
Models can be used by selecting
trainML from the
Model Type field in the
Model section of the job form. Select the desired model from the list and create the job. Once the job is running you can access the model in the
/opt/trainml/models directory, or using the
TRAINML_MODEL_PATH environment variable.