See [InputModel](https://github.com/allegroai/trains/blob/master/trains/model.py#L319) and [OutputModel](https://github.com/allegroai/trains/blob/master/trains/model.py#L539) for more information.
From this point onward, not only are the dictionary key/value pairs stored as part of the experiment, but any changes to the dictionary will be automatically updated in the task's information.
### I noticed that all of my experiments appear as `Training`. Are there other options? <a name="other-experiment-types"></a>
Yes! When creating experiments and calling `Task.init`, you can provide an experiment type.
The currently supported types are `Task.TaskTypes.training` and `Task.TaskTypes.testing`. For example:
If you feel we should add a few more, let us know in the [issues](https://github.com/allegroai/trains/issues) section.
### Sometimes I see experiments as running when in fact they are not. What's going on? <a name="experiment-running-but-stopped"></a>
TRAINS monitors your Python process. When the process exits in an orderly fashion, TRAINS closes the experiment.
When the process crashes and terminates abnormally, the stop signal is sometimes missed. In such a case, you can safely right click the experiment in the Web-App and stop it.
## My code throws an exception, but my experiment status is not "Failed". What happened? <a name="exception-not-failed"></a>
This issue was resolved in v0.9.2. Upgrade TRAINS:
```pip install -U trains```
## When I run my experiment, I get an SSL Connection error [CERTIFICATE_VERIFY_FAILED]. Do you have a solution? <a name="ssl-connection-error"></a>
Your firewall may be preventing the connection. Try one of the following solutons:
* Direct python "requests" to use the enterprise certificate file by setting the OS environment variables CURL_CA_BUNDLE or REQUESTS_CA_BUNDLE.
You can see a detailed discussion at [https://stackoverflow.com/questions/48391750/disable-python-requests-ssl-validation-for-an-imported-module](https://stackoverflow.com/questions/48391750/disable-python-requests-ssl-validation-for-an-imported-module).
2. Disable certificate verification (for security reasons, this is not recommended):
1. Upgrade TRAINS to the current version:
```pip install -U trains```
1. Create a new **trains.conf** configuration file (sample file [here](https://github.com/allegroai/trains/blob/master/docs/trains.conf)), containing:
```api { verify_certificate = False }```
1. Copy the new **trains.conf** file to ~/trains.conf (on Windows: C:\Users\your_username\trains.conf)
## Graphs and Logs
### The first log lines are missing from the experiment log tab. Where did they go? <a name="first-log-lines-missing"></a>
Due to speed/optimization issues, we opted to display only the last several hundred log lines.
You can always downloaded the full log as a file using the Web-App.
### Can I create a graph comparing hyper-parameters vs model accuracy? <a name="compare-graph-parameters"></a>
Yes, you can manually create a plot with a single point X-axis for the hyper-parameter value,
Yes! Use a [Logger](https://github.com/allegroai/trains/blob/master/trains/logger.py) object. An instance can be always be retrieved using the `Task.current_task().get_logger()` method:
### When using PyCharm to remotely debug a machine, the git repo is not detected. Do you have a solution? <a name="pycharm-remote-debug-detect-git"></a>
Yes! Since this is such a common occurrence, we created a PyCharm plugin that allows a remote debugger to grab your local repository / commit ID. See our [TRAINS PyCharm Plugin](https://github.com/allegroai/trains-pycharm-plugin) repository for instructions and [latest release](https://github.com/allegroai/trains-pycharm-plugin/releases).
Yes! Check our [TRAINS Jupyter Plugin](https://github.com/allegroai/trains-jupyter-plugin). This plugin allows you to commit your notebook directly from Jupyter. It also saves the Python version of your code and creates an updated `requirements.txt` so you know which packages you were using.
**NOTE**: Models are not automatically logged because in most cases, scikit-learn will simply pickle the object to files so there is no underlying frame we can connect to.