Python client for NCAPI
This repository contains a python client for the NCAPI, and a
ncs that allows using this client.
All the methods for non-interactive tasks (e.g. dataset management,
training, batch prediciton) are contained in the
class, and all the methods for the interactive ones are contained in
You can install the client by running:
pip install .
For development purposes, you probably want to install a "editable" version, which creates symlinks instead of actually copying stuff in the directories:
pip install -e .
There are several sets of commands and utils for managing your data
and models, which are exposed in all of the clients. Those are
described in more detail below. For cli interface,
ncs, you can see
the overall list of in the help doc:
Commands: auth Authenticate with your username and password. dataset Dataset management. evaluate Batch evaluation. model Model management. predict Batch prediction. session Interactive sessions management. train Training jobs management.
In order to connect to the API you should first authenticate with your
username and password, which will save your settings in
The easiest way to do this is to run
ncs auth Please enter your username: ncadmin Please enter your password: Please enter API url [http://0.0.0.0:5000]:
You can then use the python client and ncs, which will load and update your settings when necessary.
For python, you can also create an instance of
ncapi_client.Client and pass
it credentials and API url (optional if you have custom installation):
NCAPI_URL = 'https://cloud.neuralconcept.com:5000' NCAPI_USERNAME = 'ncuser' NCAPI_PASSWORD = 'ncpassword' c = Client(NCAPI_URL, NCAPI_USERNAME, NCAPI_PASSWORD)
You can add (upload) datasets in one of the supported formats (formats are described in the corresponding docs), and then use your datasets for training and testing
# adding a dataset d = c.dataset_add(name="name-of-the-dataset", path="/path/to/the/data") # getting a dataset d = c.dataset_get("name-of-some-dataset") # deleting the dataset c.dataset_delete(d.uuid) # listing the datasets for d in c.datasets: print(d.name, d.uuid, d.status) # listing the files in the dataset f = d.dataset_files_list() # getting a sample from the dataset s = d.dataset_samples_get(d.uuid, "<sample-id>")
ncs dataset COMMAND
add Add a new NAME dataset located at local PATH. delete Deletes the dataset. sam files Dataset file management. get Get verbose info about the dataset. list List all the datasets
For models, you can choose from a rich set of pre-defined model templates, which you can easily customise by simply editing a yaml-based config file. A typical config would look as follows:
# training parameters train: batch_size: 1 tag: alpha num_steps: 1500000 optimizer: init_lr: 1.0e-4 min_lr: 1.0e-6 decay_rate: 0.7 decay_every: 30000 loss: loss_fn_name: null # model-specific parameters model: name: name-of-your-model # this is one of the standard model types class_name: ncs.models.NormalizedRegressor characteristic_len: 0.4 num_blocks: 8 block_width: 64
There are two main blocks:
train - which contains customizable
training parameters such as the step size schedule,
model - which
contains the definition of the model, i.e. it's name, the class name
(type of the model), and model-specific settings, such as number of
blocks or number of parameters per block (those will really depend on
a particular model type).
# listing available models for m in c.models: print(m.uuid, m.name) # adding a model m = c.model_add('/path-to-your-model-config.yml'') # deleting a model c.model_delete(m.uuid) # getting verbose info about the model print(c.model_get(m.uuid))
ncs model COMMAND:
Commands: add Add a model with yaml config at PATH. delete Delete a model by UUID. get Get verbose info about the model. list List available models.
Managing training jobs
With NCAPI you can run large-scale training jobs on a cluster (cloud based or custom), without a need to worry about managing computational resources: our backend does it automatically.
# list the trainings for t in sorted(c.trainings, key=lambda v: v.uuid): print(t.uuid, t.status) # stop one of the trainings c.training_stop(c.trainings.uuid) # restart one of the previously stopped trainings c.training_restart(c.trainings.uuid) # submit a new training job = c.training_submit(c.models.uuid, c.datasets.uuid) # you can also provide additional user config for the training # which overrides the model config, e.g. if you want to train # with different training step size schedule or try different loss # function job = c.training_submit(model, dataset, "/path-to-custom-config"")
Commands: delete Delete a training. get Get verbose information about a training. list List available trainings. restart Restart a training. stop Stop a training. submit Submit a training to the queue.
Managing prediction jobs
TODO: finish the python docs
For CLI, run
ncs predict COMMAND:
Commands: delete Delete a prediction. get Get results of the prediction with UUID and save them in DST. stop Stop a prediction job. submit Submit a prediction job for a given MODEL and DATASET.
Managing evaluation jobs
TODO: finish the python docs
For CLI, run
ncs evaluate COMMAND:
Commands: delete Delete an evaluation job. get Get summary metrics of the evaluation. restart Restart an evaluation job. stop Stop an evaluation job. submit Submit an evaluation job for a given MODEL and DATASET.
Managing interactive sessions
# list running sessions for s in c.sessions: print(s) session = c.session_start(model.id, dataset.id, sample.id) session = c.session_get(session.id) print("waiting while the session starts...") while session.status != "RUNNING": session = c.session_get(session.id) time.sleep(1.0) print("it has started!") # start a session client and interactively play with your model sc = SessionClient(session, client=c) # get the current mesh (if supported by the model) verts, faces = sc.mesh # get the predictions (if supported by the model) preds = kc.predict(input_scalars=dict(angle=0.8, speed=0.5)) # do some post-processing on field values fields = np.clip(preds['fields'], 0, 10) # choosing some random control points and applying a random deformation control_points = verts[np.random.choice(verts.shape, size=2)] cp_deformations = np.random.normal(scale=0.1, size=control_points.shape) sc.apply_deformation( control_points=control_points, cp_deformations=cp_deformations, ) # get updated mesh and predictions verts, faces = sc.mesh preds = sc.predict(input_scalars=dict(angle=0.8, speed=0.5))
In CLI, you can run some of the high-level commands via
ncs session COMMAND:
Commands: delete Delete an existing session. list List of interactive sessions. start Start an interactive session with a given model and dataset
To update the html docs, you will need sphinx >= 2.0. You can then go in ncapi-python-client root directory and run
sphinx-build -b html docs/ docs/_build/html/
Corresponding docs will be created in the docs/_build/html folder. If you added new modules, you might want to create new *.rst files in the docs/ folder for these modules.
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