> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/running-zingg/build-and-save-the-model.md).
# Build and Save the Model
Run the training phase to build Zingg's matching model from your labeled training data and save it for reuse.
The training phase builds up the Zingg models using the training data from your label sessions and writes them to `zinggDir/modelId` as specified in your config.
Model
Path
Blocking model
zinggDir/modelId/model/block/
Similarity model
zinggDir/modelId/model/zingg.block
Both models
Written during the train phase. Present before any match, link, or incremental run can proceed.
Once saved, reuse the same `modelId` in all subsequent phases - `match`, `link`, and `runIncremental` to apply the trained models to your data.
If you retrain with a new model, use `Compare Model Results (diff phase)` in Enterprise to benchmark the new model against the current one before deploying. Zingg shows you exactly which clusters changed, merged, or split between the two models.
{% hint style="success" icon="right-long" %}
**Read more**:
{% endhint %}
{% hint style="success" icon="right-long" %}
Model saved to: `zinggDir/modelId`
Use the same `modelId` when running `match`, `link`, or `runIncremental` to apply this trained model.
{% endhint %}
{% tabs %}
{% tab title="Community" %}
### Python
```python
options = ClientOptions([ClientOptions.PHASE, "train"])
zingg = Zingg(args, options)
zingg.initAndExecute()
```
### CLI
```bash
./scripts/zingg.sh --phase train --conf config.json
```
{% endtab %}
{% tab title="Enterprise" %}
### Python
```python
options = ClientOptions([ClientOptions.PHASE, "train"])
zingg = EZingg(args, options)
zingg.initAndExecute()
```
### CLI
```bash
./scripts/zingg.sh --phase train --conf config.json
```
{% endtab %}
{% tab title="Enterprise Snowflake" %}
### Run train
Zingg on Snowflake can be run either from a local terminal via the CLI, or natively inside Snowflake as a job service.
#### CLI (local terminal)
```bash
./scripts/zingg.sh --phase train --conf config.json \
--properties-file
```
#### Snowflake (job service)
Run the phase as an asynchronous job service inside your Snowflake compute pool.
```sql
EXECUTE JOB SERVICE
IN COMPUTE POOL CONTAINER_ZINGG_POOL
NAME = ZINGG_TRAIN_ASYNC_JOB_SERVICE
ASYNC = true
EXTERNAL_ACCESS_INTEGRATIONS = (ALLOW_ALL_EAI)
FROM @specs SPECIFICATION_TEMPLATE_FILE = ''
USING (PHASE => 'train', CONFIG => '');
```
`` defines the container and job service configuration Zingg needs to run inside Snowpark Container Services. It is maintained by your administrator and referenced from your `@specs` stage.
`` is the name of your Zingg configuration json file.
**Monitor the job**
Retrieve the service logs to monitor execution and debug any failures.
```sql
SELECT SYSTEM$GET_SERVICE_LOGS('ZINGG_TRAIN_ASYNC_JOB_SERVICE', 0, 'zingg-async-job-container');
```
`zingg-async-job-container` refers to the container name defined in the job specification.
**Stop the compute pool**
Once the job completes, stop the compute pool to release resources.
```sql
ALTER COMPUTE POOL CONTAINER_ZINGG_POOL STOP ALL;
```
{% endtab %}
{% endtabs %}
---
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