Ease.ml Client

This is the Python implementation of the ease.ml client.

Installation

This package is available on PyPI.

pip install easeml

Example usage

Establishing a connection

To use the client API we first need to create a connection object that we will be using to target the running easeml instance. The connection must be inialized with a host name (here we use localhost) and either the API key or a username and password.

from easeml.model import Connection

connection = Connection(host="localhost:8080", api_key="some-api-key")

Querying Collections

Then we can query all the running jobs. To do that we need to create a JobQuery instance which we use to specify the parameters of our query. For example, we can query all completed jobs. To get the result we call the run() method of the query object and pass the connection instance.

from easeml.model import JobQuery

query = JobQuery(status="completed")
result, next_query = query.run(connection)

The result will contain a list of Job objects taht satisfy our query. Results are paginated to limit the size of each request. If there are more pages to be loaded, then the next_query variable will contain a JobQuery instance that we can run and return the next page. The full pattern for loading all jobs is the following:

from easeml.model import JobQuery

result, query = [], JobQuery(status="completed")

next_result, next_query = [], query
while next_query is not None:
    next_result, next_query = query.run(connection)
    result.extend(next_result)

We can take the first completed job and get a list of its tasks.

job = result[0]
tasks = job.tasks

The tasks list actually contains "shallow" instances of the Task class. This means that each instance contains only the task's id field and no other fields. This is normal because the Job object has only references to tasks, not entire tasks. To get a full version of a task given a "shallow" instance, we use the get() method.

task = tasks[0].get(connection)

Querying Specific Objects

The Task object can also be used to query tasks by their ID. We simply create a new "shallow" instance using a task ID and call the get() method.

from easeml.model import Task

task = Task(id="some-task-id").get(connection)

Creating Objects

We have the ability to create certain objects, such as Dataset, Module and Job. We do this by initializing an instance of that object, assigning values to relevant fields and calling the post() method. Here is an example of creating a dataset object along with uploading of a dataset.

from easeml.model import Dataset, DatasetSource, DatasetStatus

dataset = Dataset.create(id="test_dataset_1", source=DatasetSource.UPLOAD, name="Test Dataset 1").post(connection)

with open("test_dataset_1.tar", "rb") as f:
    dataset.upload(connection=connection, data=f)

# Once the dataset upload finishes, we need to update the status of the dataset to "transferred".
dataset.status = DatasetStatus.TRANSFERRED

# Once we assign values to fields, we use the patch command
# to apply updates to the dataset object on the server.
dataset.patch(connection)

Starting a new training Job and monitoring it

Here we show a slightly more complex example that demonstrates how to start a model selection and tuning job given a previously uploaded dataset.

We will first fetch the dataset object in order to be able to access its schema.

from easeml.model import Dataset

dataset = Dataset(id="test_dataset_1").get(connection)

Then we query all models that are applicable to the given dataset. We use the ModuleQuery class for this.

from easeml.model import ModuleQuery, ModuleType

query = ModuleQuery(type=ModuleType.MODEL, status=ModuleStatus.ACTIVE,
                    schema_in=dataset.schema_in, schema_out=dataset.schema_out)

# We assume that the result does not contain more than one page.
models, _ = query.run(connection)

We do the same for objectives.

from easeml.model import ModuleQuery, ModuleType

query = ModuleQuery(type=ModuleType.OBJECTIVE, status=ModuleStatus.ACTIVE,
                    schema_in=dataset.schema_in, schema_out=dataset.schema_out)
objectives, _ = query.run(connection)

# We will simply pick the first objective here.
objective = objectives[0]

Then we are ready to create a job.

from easeml.model import Job

job = Job(dataset=dataset, objective=objective, models=models, max_tasks=20).post(connection)

With max_tasks we specify the number of tasks to run before a job's status will become completed. We can keep querying the job to check the status.

from time import sleep
from easeml.model import JobStatus

while job.get(connection).status != JobStatus.COMPLETED:
    time.sleep(10)

Once the job is completed, we can get the task with the best result.

from easeml.model import TaskQuery, ApiQueryOrder

tasks, _ = TaskQuery(job=job, order_by="quality", order=ApiQueryOrder.DESC).run(connection)

best_task = tasks[0].get(connection)

Finally, we can download the Docker image of the best task and save it as a tar file.

image = best_task.get_image(connection)
open("/output/path/to/image.tar", "wb").write(image)