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TRAINS FAQ

General Information

Configuration

Models

Experiments

Graphs and Logs

GIT and Storage

Jupyter

scikit-learn

TRAINS API

General Information

How do I know a new version came out?

Starting v0.9.3 TRAINS notifies on a new version release.

For example, when a new client version available the notification is:

TRAINS new package available: UPGRADE to vX.Y.Z is recommended!

For example, when new server version available the notification is:

TRAINS-SERVER new version available: upgrade to vX.Y is recommended!

Configuration

How can I change the location of TRAINS configuration file?

Set TRAINS_CONFIG_FILE OS environment variable to override the default configuration file location.

export TRAINS_CONFIG_FILE="/home/user/mytrains.conf"

How can I override TRAINS credentials from the OS environment?

Set the OS environment variables below, in order to override the configuration file / defaults.

export TRAINS_API_ACCESS_KEY="key_here"
export TRAINS_API_SECRET_KEY="secret_here"
export TRAINS_API_HOST="http://localhost:8008"

How can I track OS environment variables with experiments?

​Set the OS environment variable TRAINS_LOG_ENVIRONMENT with the variables you need track. See Specifying Environment Variables to Track.

Models

How can I sort models by a certain metric?

Models are associated with the experiments that created them. In order to sort experiments by a specific metric, add a custom column in the experiments table,

Can I store more information on the models?

For example, can I store enumeration of classes?

Yes! Use the Task.set_model_label_enumeration() method:

Task.current_task().set_model_label_enumeration( {"label": int(0), } )

Can I store the model configuration file as well?

Yes! Use the Task.set_model_config() method:

Task.current_task().set_model_config("a very long text with the configuration file's content")

I am training multiple models at the same time, but I only see one of them. What happened?

All models can be found under the project's Models tab, that said, currently in the Experiment's information panel TRAINS shows only the last associated model.

This will be fixed in a future version.

Can I log input and output models manually?

Yes! For example:

input_model = InputModel.import_model(link_to_initial_model_file)
Task.current_task().connect(input_model)

OutputModel(Task.current_task()).update_weights(link_to_new_model_file_here)

See InputModel and OutputModel for more information.

Experiments

I noticed I keep getting the message warning: uncommitted code. What does it mean?

TRAINS not only detects your current repository and git commit, but also warns you if you are using uncommitted code. TRAINS does this because uncommitted code means this experiment will be difficult to reproduce.

If you still don't care, just ignore this message - it is merely a warning.

I do not use Argparser for hyper-parameters. Do you have a solution?

Yes! TRAINS supports logging an experiment parameter dictionary.

I noticed that all of my experiments appear as Training. Are there other options?

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:

task = Task.init(project_name, task_name, Task.TaskTypes.testing)

If you feel we should add a few more, let us know in the issues section.

Sometimes I see experiments as running when in fact they are not. What's going on?

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?

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?

Your firewall may be preventing the connection. Try one of the following solutons:

  1. Disable certificate verification (for security reasons, this is not recommended):

    1. Upgrade TRAINS to the current version:

      pip install -U trains

    2. Create a new trains.conf configuration file (sample file here), containing:

      api { verify_certificate = False }

    3. Copy the new trains.conf file to ~/trains.conf (on Windows: C:\Users\your_username\trains.conf)

How do I modify experiment names once they have been created?

An experiments' name is a user controlled property which can be accessed via the Task.name variable.
This allows you to use meaningful naming schemes for to easily filter and compare different experiments.

For example, to distinguish between different experiments you can append the task Id to the task name:

task = Task.init('examples', 'train')
task.name += ' {}'.format(task.id)

Or, even for post-execution:

tasks = Task.get_tasks(project_name='examples', task_name='train')
for t in tasks:
    t.name += ' {}'.format(task.id)

Another example - To append a specific hyperparameter and its value to each task's name:

tasks = Task.get_tasks(project_name='examples', task_name='my_automl_experiment')
for t in tasks:
    params = t.get_parameters()
    if 'my_secret_parameter' in params:
        t.name += ' my_secret_parameter={}'.format(params['my_secret_parameter'])

Use it also when creating automation pipelines with a naming convention, see our random search automation example.

Graphs and Logs

The first log lines are missing from the experiment log tab. Where did they go?

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?

Yes, you can manually create a plot with a single point X-axis for the hyper-parameter value, and Y-Axis for the accuracy. For example:

number_layers = 10
accuracy = 0.95
Task.current_task().get_logger().report_scatter2d(
    "performance", "accuracy", iteration=0, 
    mode='markers', scatter=[(number_layers, accuracy)])

Assuming the hyper-parameter is "number_layers" with current value 10, and the accuracy for the trained model is 0.95. Then, the experiment comparison graph shows:

Another option is a histogram chart:

number_layers = 10
accuracy = 0.95
Task.current_task().get_logger().report_vector(
    "performance", "accuracy", iteration=0, labels=['accuracy'],
    values=[accuracy], xlabels=['number_layers %d' % number_layers])

I want to add more graphs, not just with Tensorboard. Is this supported?

Yes! Use the Logger module. For more information, see TRAINS Explicit Logging.

Git and Storage

Is there something TRAINS can do about uncommitted code running?

Yes! TRAINS currently stores the git diff as part of the experiment's information. The Web-App will soon present the git diff as well. This is coming very soon!

I read there is a feature for centralized model storage. How do I use it?

When calling Task.init(), providing the output_uri parameter allows you to specify the location in which model snapshots will be stored.

For example, calling:

task = Task.init(project_name, task_name, output_uri="/mnt/shared/folder")

Will tell TRAINS to copy all stored snapshots into a sub-folder under /mnt/shared/folder. The sub-folder's name will contain the experiment's ID. Assuming the experiment's ID in this example is 6ea4f0b56d994320a713aeaf13a86d9d, the following folder will be used:

/mnt/shared/folder/task_6ea4f0b56d994320a713aeaf13a86d9d/models/

TRAINS supports more storage types for output_uri:

# AWS S3 bucket
task = Task.init(project_name, task_name, output_uri="s3://bucket-name/folder")

# Google Cloud Storage bucket
taks = Task.init(project_name, task_name, output_uri="gs://bucket-name/folder")

NOTE: These require configuring the storage credentials in ~/trains.conf. For a more detailed example, see here.

When using PyCharm to remotely debug a machine, the git repo is not detected. Do you have a solution?

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 repository for instructions and latest release.

Jupyter Notebooks

I am using Jupyter Notebook. Is this supported?

Yes! You can run TRAINS in Jupyter Notebooks.

  • Option 1: Install trains on your Jupyter Notebook host machine
  • Option 2: Install trains in your Jupyter Notebook and connect using trains credentials

Option 1: Install trains on your Jupyter host machine

  1. Connect to your Juypter host machine.

  2. Install the trains Python package.

     pip install trains

  3. Run the trains initialize wizard.

     trains-init

  4. In your Jupyter Notebook, you can now use trains.

Option 2: Install trains in your Jupyter Notebook

  1. In the trains Web-App, Profile page, create credentials and copy your access key and secret key. These are required in the Step 3.

  2. Install the trains Python package.

     # install trains
 !pip install trains

  3. Use the Task.set_credentials() method to specify the host, port, access key and secret key (see step 1). Notice: host is NOT the web server (default port 8080) but the API server (default port 8008)

     # Set your credentials using the **trains** apiserver URI and port, access_key, and secret_key.
 Task.set_credentials(host='http://localhost:8008',key='<access_key>', secret='<secret_key>')

  4. You can now use trains.

     # create a task and start training
 task = Task.init('jupyer project', 'my notebook')

Git is not well supported in Jupyter, so we just gave up on committing our code. Do you have a solution?

Yes! Check our 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.

scikit-learn

Can I use TRAINS with scikit-learn?

Yes! scikit-learn is supported. Everything you do is logged.

Models are automatically logged when stored using joblib.

# from sklearn.externals import joblib
import joblib
joblib.dump(model, 'model.pkl')
loaded_model = joblib.load('model.pkl')

TRAINS API

How can I use the TRAINS API to fetch data?

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
  2. send a request for all projects named examples using the projects service GetAllRequest method
  3. from the response, get the Ids of all those projects named examples
  4. send a request for all experiments (tasks) with those project Ids using the tasks service GetAllRequest method
  5. from the response, get the data for the experiment (task) Id 11 and print the experiment name
  6. send a request for a metrics histogram for experiment (task) Id 11 using the events service ScalarMetricsIterHistogramRequest method and print the histogram
# 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(
    task=task.id,
))
scalars = res.response_data
print('scalars {}'.format(scalars))