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@ -52,7 +52,7 @@ your experimentation logs, outputs, and data to one centralized server.
* Seamless integration (including version control) with **Jupyter Notebook** * Seamless integration (including version control) with **Jupyter Notebook**
and [*PyCharm* remote debugging](https://github.com/allegroai/trains-pycharm-plugin) and [*PyCharm* remote debugging](https://github.com/allegroai/trains-pycharm-plugin)
**Additionally, log data explicitly using [Trains Explicit Logging](https://github.com/allegroai/trains/blob/master/docs/logger.md).** **Additionally, log data explicitly using [Trains Explicit Logging](https://allegro.ai/docs/examples/reporting/scatter_hist_confusion_mat_reporting/).**
## Using Trains <a name="using-trains"></a> ## Using Trains <a name="using-trains"></a>
@ -93,7 +93,7 @@ Trains is a two part solution:
- Open the link and view your experiment parameters, model and tensorboard metrics - Open the link and view your experiment parameters, model and tensorboard metrics
**See examples [here](https://github.com/allegroai/trains/tree/master/examples)** **See examples [here](https://allegro.ai/docs/examples/examples_overview/)**
2. [Trains Server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web)) 2. [Trains Server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))