In this example, we are showcasing how to use the SDK to create an end-to-end pipeline. We'll train a model, watch the state transitions, stream the logs, inspect it's accuracy, and then deploy it as a RESTful API endpoint behind a multi-instance, load-balanced GPU cluster.
The SDK is bundled with the Gradient CLI. You'll need the latest version which you can download by adding --pre when .
NOTE: We'll be adding a Jupyter Notebook template that you can run on your own. For now, you can download the file here which can be imported into a Gradient Notebook instance.
Getting Started
#Import the SDK Client from the gradient package
from gradient import sdk_client, ProjectsClient
#create a API Key
api_key = "API_KEY_HERE"
deployment_client = sdk_client.DeploymentsClient(api_key)
models_client = sdk_client.ModelsClient(api_key)
jobs_client = sdk_client.JobsClient(api_key)
projects_client = ProjectsClient(api_key)
experiment_client = sdk_client.ExperimentsClient(api_key)
#or access them all from a single client
#client = sdk_client.SdkClient(api_key)
Print list of projects
projects_list = projects_client.list()
for project in projects_list:
print("project_id: "+project.id +" project_name: "+ project.name)
Create project
#create a new project
project_id = projects_client.create("sample_tutorials")
print("project_id: "+proj_id)
#list experiments in the project
print(experiment_client.list([project_id])
#create single node experiment
experiment_id = experiment_client.create_single_node(**single_node_parameters)
#get single node experiment object
from gradient import constants
#experiment state, created but not started
state = experiment_client.get(experiment_id).state
print("state: "+constants.ExperimentState.get_state_str(state))
Start experiment
experiment_client.start(experiment_id)
print("PAPERSPACE LINK FOR EXPERIMENT")
print("_______EXPERIMENT STARTING_____")
print()
print("https://www.paperspace.com/console/projects/"+ project_id+"/experiments/"+experiment_id)
Watching the state transitions of an experiment
print("Watching state of experiment")
state = "created"
while state != "running" or state != "stopped":
new_state = constants.ExperimentState.get_state_str(experiment_client.get(experiment_id).state)
if new_state != state:
print("state: "+state + " new state: "+new_state)
state = new_state
if state == "running":
print("state: "+state + " new state: "+new_state)
break
Create a log stream & print all logs for the duration of experiment
#create a log stream & print all logs for the duration of experiment
log_stream = experiment_client.yield_logs(experiment_id)
print("Streaming logs of experiment")
try:
while True:
print(log_stream.send(None))
except:
print("done streaming logs")
Get model created by experiment
#get model we just trained
model = models_client.list(experiment_id = experiment_id)
#print model
print(model)
View Model Accuracy
if len(model) > 0:
print("model accuracy: "+model[0]['accuracy'])
