Set the OS environment variable `TRAINS_LOG_ENVIRONMENT` with the variables you need track. See [Specifying Environment Variables to Track](https://github.com/allegroai/trains/blob/master/docs/logger.md#specifying-environment-variables-to-track).
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.
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 the [Logger](https://github.com/allegroai/trains/blob/master/trains/logger.py) module. For more information, see [TRAINS Explicit Logging](https://github.com/allegroai/trains/blob/master/docs/logger.md).
### 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).
### Git is not well supported in Jupyter, so we just gave up on committing our code. Do you have a solution? <a name="commit-git-in-jupyter"></a>
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.
### How can I use the TRAINS API to fetch data? <a name="api"></a>
To fetch data using the **TRAINS** API, create an authenticated session and send requests for data using **TRAINS API** services and methods.
The responses to the requests contain your data.
For example, to get the metrics for an experiment and print metrics as a histogram:
1. start an authenticated session
1. send a request for all projects named `examples` using the `projects` service `GetAllRequest` method
1. from the response, get the Ids of all those projects named `examples`
1. send a request for all experiments (tasks) with those project Ids using the `tasks` service `GetAllRequest` method
1. from the response, get the data for the experiment (task) Id `11` and print the experiment name
1. send a request for a metrics histogram for experiment (task) Id `11` using the `events` service `ScalarMetricsIterHistogramRequest` method and print the histogram
```python
# Import Session from the trains backend_api
from trains.backend_api import Session
# Import the services for tasks, events, and projects
from trains.backend_api.services import tasks, events, projects
# Create an authenticated session
session = Session()
# Get projects matching the project name 'examples'
res = session.send(projects.GetAllRequest(name='examples'))
# Get all the project Ids matching the project name 'examples"
projects_id = [p.id for p in res.response.projects]
print('project ids: {}'.format(projects_id))
# Get all the experiments/tasks
res = session.send(tasks.GetAllRequest(project=projects_id))
# Do your work
# For example, get the experiment whose Id is '11'
task = res.response.tasks[11]
print('task name: {}'.format(task.name))
# For example, for experiment Id '11', get the experiment metric values
res = session.send(events.ScalarMetricsIterHistogramRequest(
