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        "author": "Bojan Karlas",
        "author_email": "bojan.karlas@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# Ease.ml Client\n\nThis is the Python implementation of the ease.ml client.\n\n## Installation\n\nThis package is available on PyPI.\n\n```bash\npip install easemlclient\n```\n\n## Example usage\n\n### Establishing a connection\n\nTo 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.\n\n```python\nfrom easemlclient.model import Connection\n\nconnection = Connection(host=\"localhost:8080\", api_key=\"some-api-key\")\n```\n\n### Querying Collections\n\nThen 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.\n\n```python\nfrom easemlclient.model import JobQuery\n\nquery = JobQuery(status=\"completed\")\nresult, next_query = query.run(connection)\n```\n\nThe 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:\n\n```python\nfrom easemlclient.model import JobQuery\n\nresult, query = [], JobQuery(status=\"completed\")\n\nnext_result, next_query = [], query\nwhile next_query is not None:\n    next_result, next_query = query.run(connection)\n    result.extend(next_result)\n```\n\nWe can take the first completed job and get a list of its tasks.\n\n```python\njob = result[0]\ntasks = job.tasks\n```\n\nThe `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.\n\n```python\ntask = tasks[0].get(connection)\n```\n\n### Querying Specific Objects\n\nThe `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.\n\n```python\nfrom easemlclient.model import Task\n\ntask = Task(id=\"some-task-id\").get(connection)\n```\n\n### Creating Objects\n\nWe 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.\n\n```python\n\nfrom easemlclient.model import Dataset, DatasetSource, DatasetStatus\n\ndataset = Dataset.create(id=\"test_dataset_1\", source=DatasetSource.UPLOAD, name=\"Test Dataset 1\").post(connection)\n\nwith open(\"test_dataset_1.tar\", \"rb\") as f:\n    dataset.upload(connection=connection, data=f)\n\n# Once the dataset upload finishes, we need to update the status of the dataset to \"transferred\".\ndataset.status = DatasetStatus.TRANSFERRED\n\n# Once we assign values to fields, we use the patch command\n# to apply updates to the dataset object on the server.\ndataset.patch(connection)\n```\n\n### Starting a new training Job and monitoring it\n\nHere we show a slightly more complex example that demonstrates how to start a model selection and tuning job given a previously uploaded dataset.\n\nWe will first fetch the dataset object in order to be able to access its schema.\n\n```python\nfrom easemlclient.model import Dataset\n\ndataset = Dataset(id=\"test_dataset_1\").get(connection)\n```\n\nThen we query all models that are applicable to the given dataset. We use the `ModuleQuery` class for this.\n\n```python\n\nfrom easemlclient.model import ModuleQuery, ModuleType\n\nquery = ModuleQuery(type=ModuleType.MODEL, status=ModuleStatus.ACTIVE,\n                    schema_in=dataset.schema_in, schema_out=dataset.schema_out)\n\n# We assume that the result does not contain more than one page.\nmodels, _ = query.run(connection)\n```\n\nWe do the same for objectives.\n\n```python\n\nfrom easemlclient.model import ModuleQuery, ModuleType\n\nquery = ModuleQuery(type=ModuleType.OBJECTIVE, status=ModuleStatus.ACTIVE,\n                    schema_in=dataset.schema_in, schema_out=dataset.schema_out)\nobjectives, _ = query.run(connection)\n\n# We will simply pick the first objective here.\nobjective = objectives[0]\n```\n\nThen we are ready to create a job.\n\n```python\nfrom easemlclient.model import Job\n\njob = Job(dataset=dataset, objective=objective, models=models, max_tasks=20).post(connection)\n```\n\nWith `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.\n\n```python\nfrom time import sleep\nfrom easemlclient.model import JobStatus\n\nwhile job.get(connection).status != JobStatus.COMPLETED:\n    time.sleep(10)\n```\n\nOnce the job is completed, we can get the task with the best result.\n\n```python\nfrom easemlclient.model import TaskQuery, ApiQueryOrder\n\ntasks, _ = TaskQuery(job=job, order_by=\"quality\", order=ApiQueryOrder.DESC).run(connection)\n\nbest_task = tasks[0].get(connection)\n```\n\nFinally, we can download the Docker image of the best task and save it as a tar file.\n\n```python\nimage = best_task.get_image(connection)\nopen(\"/output/path/to/image.tar\", \"wb\").write(image)\n```\n\n\n",
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