# subscribe to the websocket channel.
taskNamespace = socket.define(TasksNamespace, "/tasks")
taskNamespace.emit("join_room", f"collaboration_{collaboration_id}")
# input for the dsummary Docker image (algorithm)
input_ = {
"master": "true",
"method": "master",
"args": [],
"kwargs": {
#"functions": ["min", "max"],
"columns": [{
"variable": "age",
"table": "records",
"functions": ["min", "max"]
}, {
"variable": "weight",
"table": "records"
}]
}
}
# post the task to the server
task = client.post_task(name="summary",
image="pcmateus/v6-summary-rdb:1.0.0",
collaboration_id=collaboration_id,
input_=input_)
socket.wait(seconds=100)
#"functions": ["min", "max"],
"columns": [{
"variable": "age",
"table": "records",
"functions": ["min", "max"]
}, {
"variable": "weight",
"table": "records"
}]
}
}
# 3. post the task to the server
task = client.post_task(
name="summary",
image="pcmateus/v6-summary-rdb:1.0.0",
collaboration_id=3,
organization_ids=[3], # specify where the central container should run!
input_=input_)
# 4. poll if central container is finished
task_id = task.get("id")
print(f"task id={task_id}")
task = client.request(f"task/{task_id}")
while not task.get("complete"):
task = client.request(f"task/{task_id}")
print("Waiting for results...")
time.sleep(1)
# 5. obtain the finished results
results = client.get_results(task_id=task.get("id"))
"seperator": ";",
"columns": {
"patient_id": "Int64",
"age": "Int64",
"weight": "float64",
"stage": "category",
"cat": "category",
"hot_encoded": "Int64"
}
}
}
#
# # 3. post the task to the server
task = client.post_task(
name="summary",
image="docker-registry.distributedlearning.ai/dsummary",
collaboration_id=3,
# organization_ids=[3], # specify where the central container should run!
input_=input_)
#
# # 4. poll if central container is finished
# task_id = task.get("id")
# print(f"task id={task_id}")
#
# task = client.request(f"task/{task_id}")
# while not task.get("complete"):
# task = client.request(f"task/{task_id}")
# print("Waiting for results...")
# time.sleep(1)
#
# # 5. obtain the finished results
results = client.get_results(task_id=task.get("id"))
from vantage6.client import Client
from pathlib import Path
import time
print("Attempt login to Vantage6 API")
client = Client("http://localhost", 5000, "/api")
client.authenticate("johan", "1234")
client.setup_encryption(None)
input_ = {"master": "true", "method": "master", "args": [], "kwargs": {}}
print("Requesting to execute summary algorithm")
task = client.post_task(name="testing",
image="docker.io/username/imagename",
collaboration_id=1,
input_=input_,
organization_ids=[1])
print("Wait and fetch results")
res = client.result.get(id_=task.get("results")[0]['id'])
attempts = 1
while ((res["result"] == None) and attempts < 7):
print("waiting...")
time.sleep(5)
res = client.result.get(id_=task.get("results")[0]['id'])
attempts += 1
print(res)
# subscribe to the websocket channel.
taskNamespace = socket.define(TasksNamespace, "/tasks")
taskNamespace.emit("join_room", f"collaboration_{collaboration_id}")
# input for the dsummary Docker image (algorithm)
input_ = {
"method": "summary",
"args": [],
"kwargs": {
"decimal": ",",
"seperator": ";",
"columns": {
"patient_id": "Int64",
"age": "Int64",
"weight": "float64",
"stage": "category",
"cat": "category",
"hot_encoded": "Int64"
}
}
}
# post the task to the server
task = client.post_task(name="summary",
image="harbor.vantage6.ai/algorithms/summary",
collaboration_id=collaboration_id,
input_=input_)
socket.wait(seconds=100)