I am a newbie of SPARQL and Fuseki. I set up Fuseki Server with
fuseki-server --update --mem /address_act
to create a dataset.
And I have a graph containing many triples and then I want to add these triples into the dataset via SPARQLUPDATE. Here are the code to get triples into graph and try to save it to the dataset via Fuseki Server:
import requests
import rdflib
import re
from rdflib import ConjunctiveGraph, Graph, Literal, URIRef
from rdflib.plugins.stores import sparqlstore
query_endpoint = 'http://localhost:3030/address_act/query'
update_endpoint = 'http://localhost:3030/address_act/update'
store = sparqlstore.SPARQLUpdateStore()
store.open((update_endpoint, update_endpoint))
default_graph = URIRef('http://example.org/default-graph')
ng = Graph(store, identifier=default_graph)
g = Graph()
g1 = Graph()
for i in range(1,2):
url = 'http://gnafld.net/address/?per_page=3&page=' + str(i)
g.parse(url)
page = g.query("""SELECT ?subject
WHERE {
?subject a <http://gnafld.net/def/gnaf#Address>.
}""")
for row in page:
ad_info = requests.get(row.subject).content
g1.parse(data=ad_info, format='turtle')
#print('The number of triples in Graph: {}'.format(len(g1)))
ng.update(
u'INSERT DATA { %s }' % g1.serialize(format='turtle')
)
Besides, I have another way to do this using SPARQLWrapper:
import requests
import rdflib
import re
from rdflib import ConjunctiveGraph, Graph, Literal, URIRef
from rdflib.plugins.stores import sparqlstore
from SPARQLWrapper import SPARQLWrapper
query_endpoint = 'http://localhost:3030/address_act/query'
update_endpoint = 'http://localhost:3030/address_act/update'
store = sparqlstore.SPARQLUpdateStore()
store.open((update_endpoint, update_endpoint))
default_graph = URIRef('http://example.org/default-graph')
g = Graph()
g1 = Graph(identifier=default_graph)
for i in range(1,2):
url = 'http://gnafld.net/address/?per_page=3&page=' + str(i)
g.parse(url)
page = g.query("""SELECT ?subject
WHERE {
?subject a <http://gnafld.net/def/gnaf#Address>.
}""")
for row in page:
ad_info = requests.get(row.subject).content
g1.parse(data=ad_info, format='turtle')
#print('The number of triples in Graph: {}'.format(len(g1)))
for s,p,o in g1:
queryStringUpload = 'INSERT DATA {GRAPH <http://example.org/default-graph> {%s %s %s}}' %(s,p,o)
sparql = SPARQLWrapper('http://localhost:3030/address_act/update')
sparql.setQuery(queryStringUpload)
sparql.method = 'POST'
sparql.query()
When I run the two above, the last sentence ng.update(u'INSERT DATA { %s }' % g1.serialize(format='turtle')) and sparql.query() in the program causes the error. I am sure these triples have existed in the graph, but when do update, both give the error like:
QueryBadFormed: QueryBadFormed: a bad request has been sent to the endpoint, probably the sparql query is bad formed.
Response:
b'Error 400: Lexical error at line 11, column 59. Encountered: "\\\'" (39), after : "b"\n\n\nFuseki - version 3.7.0 (Build date: 2018-04-05T11:04:59+0000)\n'
and error like:
QueryBadFormed: QueryBadFormed: a bad request has been sent to the endpoint, probably the sparql query is bad formed.
Response:
b'Error 400: Line 1, column 56: Unresolved prefixed name: http:\n\n\nFuseki - version 3.7.0 (Build date: 2018-04-05T11:04:59+0000)\n'
It seems that the SPARQL update operation does not work. Is there any grammar mistake so that the triples cannot be inserted? Any idea how to solve this? Grateful for any effort.
Related
I am working on a python script which will use QS APIs to retrieve the user parameter selections but keep getting the below error:
parameters = response['Dashboard']['Parameters'] KeyError: 'Parameters'
If I try a different code to retrieve the datasets in my QS account, it works but the Parameters code doesn't. I think I am missing some configuration.
#Code to retrieve the parameters from a QS dashboard (which fails):
import boto3
quicksight = boto3.client('quicksight')
response = quicksight.describe_dashboard(
AwsAccountId='99999999999',
DashboardId='zzz-zzzz-zzzz'
)
parameters = response['Dashboard']['Parameters']
for parameter in parameters:
print(parameter['Name'], ':', parameter['Value'])
#Code to display the datasets in the QS account (which works):
import boto3
import json
account_id = '99999999999'
session = boto3.Session(profile_name='default')
qs_client = session.client('quicksight')
response = qs_client.list_data_sets(AwsAccountId = account_id,MaxResults = 100)
results = response['DataSetSummaries']
while "NextToken" in response.keys():
response = qs_client.list_data_sets(AwsAccountId = account_id,MaxResults = 100,NextToken=response["NextToken"])
results.extend(response["DataSetSummaries"])
for i in results:
x = i['DataSetId']
try:
response = qs_client.describe_data_set(AwsAccountId=account_id,DataSetId=x)
print("succeeded loading: {} for data set {} ".format(x, response['DataSet']['Name']))
except:
print("failed loading: {} ".format(x))
I'm struggling to correctly set Vertex AI pipeline which does the following:
read data from API and store to GCS and as as input for batch prediction.
get an existing model (Video classification on Vertex AI)
create Batch prediction job with input from point 1.
As it will be seen, I don't have much experience with Vertex Pipelines/Kubeflow thus I'm asking for help/advice, hope it's just some beginner mistake.
this is the gist of the code I'm using as pipeline
from google_cloud_pipeline_components import aiplatform as gcc_aip
from kfp.v2 import dsl
from kfp.v2.dsl import component
from kfp.v2.dsl import (
Output,
Artifact,
Model,
)
PROJECT_ID = 'my-gcp-project'
BUCKET_NAME = "mybucket"
PIPELINE_ROOT = "{}/pipeline_root".format(BUCKET_NAME)
#component
def get_input_data() -> str:
# getting data from API, save to Cloud Storage
# return GS URI
gcs_batch_input_path = 'gs://somebucket/file'
return gcs_batch_input_path
#component(
base_image="python:3.9",
packages_to_install=['google-cloud-aiplatform==1.8.0']
)
def load_ml_model(project_id: str, model: Output[Artifact]):
"""Load existing Vertex model"""
import google.cloud.aiplatform as aip
model_id = '1234'
model = aip.Model(model_name=model_id, project=project_id, location='us-central1')
#dsl.pipeline(
name="batch-pipeline", pipeline_root=PIPELINE_ROOT,
)
def pipeline(gcp_project: str):
input_data = get_input_data()
ml_model = load_ml_model(gcp_project)
gcc_aip.ModelBatchPredictOp(
project=PROJECT_ID,
job_display_name=f'test-prediction',
model=ml_model.output,
gcs_source_uris=[input_data.output], # this doesn't work
# gcs_source_uris=['gs://mybucket/output/'], # hardcoded gs uri works
gcs_destination_output_uri_prefix=f'gs://{PIPELINE_ROOT}/prediction_output/'
)
if __name__ == '__main__':
from kfp.v2 import compiler
import google.cloud.aiplatform as aip
pipeline_export_filepath = 'test-pipeline.json'
compiler.Compiler().compile(pipeline_func=pipeline,
package_path=pipeline_export_filepath)
# pipeline_params = {
# 'gcp_project': PROJECT_ID,
# }
# job = aip.PipelineJob(
# display_name='test-pipeline',
# template_path=pipeline_export_filepath,
# pipeline_root=f'gs://{PIPELINE_ROOT}',
# project=PROJECT_ID,
# parameter_values=pipeline_params,
# )
# job.run()
When running the pipeline it throws this exception when running Batch prediction:
details = "List of found errors: 1.Field: batch_prediction_job.model; Message: Invalid Model resource name.
so I'm not sure what could be wrong. I tried to load model in the notebook (outside of component) and it correctly returns.
Second issue I'm having is referencing GCS URI as output from component to batch job input.
input_data = get_input_data2()
gcc_aip.ModelBatchPredictOp(
project=PROJECT_ID,
job_display_name=f'test-prediction',
model=ml_model.output,
gcs_source_uris=[input_data.output], # this doesn't work
# gcs_source_uris=['gs://mybucket/output/'], # hardcoded gs uri works
gcs_destination_output_uri_prefix=f'gs://{PIPELINE_ROOT}/prediction_output/'
)
During compilation, I get following exception TypeError: Object of type PipelineParam is not JSON serializable, though I think this could be issue of ModelBatchPredictOp component.
Again any help/advice appreciated, I'm dealing with this from yesterday, so maybe I missed something obvious.
libraries I'm using:
google-cloud-aiplatform==1.8.0
google-cloud-pipeline-components==0.2.0
kfp==1.8.10
kfp-pipeline-spec==0.1.13
kfp-server-api==1.7.1
UPDATE
After comments, some research and tuning, for referencing model this works:
#component
def load_ml_model(project_id: str, model: Output[Artifact]):
region = 'us-central1'
model_id = '1234'
model_uid = f'projects/{project_id}/locations/{region}/models/{model_id}'
model.uri = model_uid
model.metadata['resourceName'] = model_uid
and then I can use it as intended:
batch_predict_op = gcc_aip.ModelBatchPredictOp(
project=gcp_project,
job_display_name=f'batch-prediction-test',
model=ml_model.outputs['model'],
gcs_source_uris=[input_batch_gcs_path],
gcs_destination_output_uri_prefix=f'gs://{BUCKET_NAME}/prediction_output/test'
)
UPDATE 2
regarding GCS path, a workaround is to define path outside of the component and pass it as an input parameter, for example (abbreviated):
#dsl.pipeline(
name="my-pipeline",
pipeline_root=PIPELINE_ROOT,
)
def pipeline(
gcp_project: str,
region: str,
bucket: str
):
ts = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
gcs_prediction_input_path = f'gs://{BUCKET_NAME}/prediction_input/video_batch_prediction_input_{ts}.jsonl'
batch_input_data_op = get_input_data(gcs_prediction_input_path) # this loads input data to GCS path
batch_predict_op = gcc_aip.ModelBatchPredictOp(
project=gcp_project,
model=training_job_run_op.outputs["model"],
job_display_name='batch-prediction',
# gcs_source_uris=[batch_input_data_op.output],
gcs_source_uris=[gcs_prediction_input_path],
gcs_destination_output_uri_prefix=f'gs://{BUCKET_NAME}/prediction_output/',
).after(batch_input_data_op) # we need to add 'after' so it runs after input data is prepared since get_input_data doesn't returns anything
still not sure, why it doesn't work/compile when I return GCS path from get_input_data component
I'm glad you solved most of your main issues and found a workaround for model declaration.
For your input.output observation on gcs_source_uris, the reason behind it is because the way the function/class returns the value. If you dig inside the class/methods of google_cloud_pipeline_components you will find that it implements a structure that will allow you to use .outputs from the returned value of the function called.
If you go to the implementation of one of the components of the pipeline you will find that it returns an output array from convert_method_to_component function. So, in order to have that implemented in your custom class/function your function should return a value which can be called as an attribute. Below is a basic implementation of it.
class CustomClass():
def __init__(self):
self.return_val = {'path':'custompath','desc':'a desc'}
#property
def output(self):
return self.return_val
hello = CustomClass()
print(hello.output['path'])
If you want to dig more about it you can go to the following pages:
convert_method_to_component, which is the implementation of convert_method_to_component
Properties, basics of property in python.
How to invoke sagemaker endpoint with input data type numpy.ndarray.
I have deployed a sagemaker model and trying to hit it using lambda function.
But I am unable to figure out how to do it. I am getting server error.
One row of the Input data.
The total data set has shape=(91,5,12).
The below is only one row of Input data.
array([[[0.30440741, 0.30209799, 0.33520652, 0.41558442, 0.69096432,
0.69611016, 0.25153326, 0.98333333, 0.82352941, 0.77187154,
0.7664042 , 0.74468085],
[0.30894981, 0.33151662, 0.22907725, 0.46753247, 0.69437367,
0.70410559, 0.29259044, 0.9 , 0.80882353, 0.79401993,
0.89501312, 0.86997636],
[0.33511896, 0.34338939, 0.24065546, 0.48051948, 0.70384005,
0.71058715, 0.31031288, 0.86666667, 0.89705882, 0.82724252,
0.92650919, 0.89125296],
[0.34617355, 0.36150251, 0.23726854, 0.54545455, 0.71368726,
0.71703244, 0.30228356, 0.85 , 0.86764706, 0.86157254,
0.97112861, 0.94089835],
[0.36269508, 0.35923332, 0.40285461, 0.62337662, 0.73325475,
0.7274392 , 0.26241391, 0.85 , 0.82352941, 0.89922481,
0.9343832 , 0.90780142]]])
I am using the following code but unable to invoke the endpoint
import boto3
def lambda_handler(event, context):
# The SageMaker runtime is what allows us to invoke the endpoint that we've created.
runtime = boto3.Session().client('sagemaker-runtime')
endpoint = 'sagemaker-tensorflow-2019-04-22-07-16-51-717'
print('givendata ', event['body'])
# data = numpy.array([numpy.array(xi) for xi in event['body']])
data = event['body']
print('numpy array ', data)
# Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given
response = runtime.invoke_endpoint(EndpointName = endpoint,# The name of the endpoint we created
ContentType = 'application/json', # The data format that is expected
Body = data) # The actual review
# The response is an HTTP response whose body contains the result of our inference
result = response['Body'].read().decode('utf-8')
print('response', result)
# Round the result so that our web app only gets '1' or '0' as a response.
result = round(float(result))
return {
'statusCode' : 200,
'headers' : { 'Content-Type' : 'text/plain', 'Access-Control-Allow-Origin' : '*' },
'body' : str(result)
}
I am unable to figure out what should be written in place of ContentType.
Because I am not aware of MIME type in case of numpy.ndarray.
Illustration of what I had and how I solved
from sagemaker.tensorflow import TensorFlowPredictor
predictor = TensorFlowPredictor('sagemaker-tensorflow-serving-date')
data = np.array(raw_data)
response = predictor.predict(data=data)
predictions = response['predictions']
print(predictions)
What I did to find the answer:
Looked up predictions.py and content_types.py implementation in sagemaker python library to see what content types it used and what arguments it had.
First I thought that application/x-npy content_type was used and thus tried using serialisation code from predictor.py and passing the application/x-npy as content_type to invoke_endpoint.
After receiving 415 (unsupported media type), the issue was still the content_type. The following print statements helped me to reveal what content_type predictor actually uses (application/json) and thus I took the appropriate serialisation code from predictor.py
from sagemaker.tensorflow import TensorFlowPredictor
predictor = TensorFlowPredictor('sagemaker-tensorflow-serving-date')
data = np.array(raw_data)
response = predictor.predict(data=data)
print(predictor.content_type)
print(predictor.accept)
predictions = response['predictions']
print(predictions)
TL;DR
Solution for lambda:
import json
import boto3
ENDPOINT_NAME = 'sagemaker-tensorflow-serving-date'
config = botocore.config.Config(read_timeout=80)
runtime= boto3.client('runtime.sagemaker', config=config)
data = np.array(raw_data)
payload = json.dumps(data.tolist())
response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME,
ContentType='application/json',
Body=payload)
result = json.loads(response['Body'].read().decode())
res = result['predictions']
Note: numpy is not included in lambda thus you would either include the numpy yourself or instead of data.tolist() operate with python list and json.dump that list (of lists). From your code it seems to me you have python list instead of numpy array, so simple json dump should work.
If you are training and hosting custom algorithm on SageMaker using TensorFlow, you can serialize/de-serialize the request and response format as JSON as in TensorFlow Serving Predict API.
import numpy
from sagemaker.predictor import json_serializer, json_deserializer
# define predictor
predictor = estimator.deploy(1, instance_type)
# format request
data = {'instances': numpy.asarray(np_array).astype(float).tolist()}
# set predictor request/response formats
predictor.accept = 'application/json'
predictor.content_type = 'application/json'
predictor.serializer = json_serializer
predictor.deserializer = json_deserializer
# run inference using SageMaker predict class
# https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/predictor.py
predictor.predict(data)
You can refer the example notebook here to train and host custom TensorFlow container.
What is a good way to hook a custom SPARQL function into rdflib?
I have been looking around in rdflib for an entry point for custom function. I found no dedicated entry point but found that rdflib.plugins.sparql.CUSTOM_EVALS might be a place to add the custom function.
So far I have made an attempt with the code below. It seems "dirty" to me. I am calling a "hidden" function (_eval) and I am not sure I got all the argument updating correct. Beyond the custom_eval.py example code (which form the basis for my code) I found little other code or documentation about CUSTOM_EVALS.
import rdflib
from rdflib.plugins.sparql.evaluate import evalPart
from rdflib.plugins.sparql.sparql import SPARQLError
from rdflib.plugins.sparql.evalutils import _eval
from rdflib.namespace import Namespace
from rdflib.term import Literal
NAMESPACE = Namespace('//custom/')
LENGTH = rdflib.term.URIRef(NAMESPACE + 'length')
def customEval(ctx, part):
"""Evaluate custom function."""
if part.name == 'Extend':
cs = []
for c in evalPart(ctx, part.p):
if hasattr(part.expr, 'iri'):
# A function
argument = _eval(part.expr.expr[0], c.forget(ctx, _except=part.expr._vars))
if part.expr.iri == LENGTH:
e = Literal(len(argument))
else:
raise SPARQLError('Unhandled function {}'.format(part.expr.iri))
else:
e = _eval(part.expr, c.forget(ctx, _except=part._vars))
if isinstance(e, SPARQLError):
raise e
cs.append(c.merge({part.var: e}))
return cs
raise NotImplementedError()
QUERY = """
PREFIX custom: <%s>
SELECT ?s ?length WHERE {
BIND("Hello, World" AS ?s)
BIND(custom:length(?s) AS ?length)
}
""" % (NAMESPACE,)
rdflib.plugins.sparql.CUSTOM_EVALS['exampleEval'] = customEval
for row in rdflib.Graph().query(QUERY):
print(row)
So first off, I want to thank you for showing how you implemented a new SPARQL function.
Secondly, by using your code I was able to create a SPARQL function that evaluates two strings by using the Levenshtein distance. It has been really insightful and I wish to share it for it holds additional documentation that could help other developers creating their own custom SPARQL functions.
# Import needed to introduce new SPARQL function
import rdflib
from rdflib.plugins.sparql.evaluate import evalPart
from rdflib.plugins.sparql.sparql import SPARQLError
from rdflib.plugins.sparql.evalutils import _eval
from rdflib.namespace import Namespace
from rdflib.term import Literal
# Import for custom function calculation
from Levenshtein import distance as levenshtein_distance # python-Levenshtein==0.12.2
def SPARQL_levenshtein(ctx:object, part:object) -> object:
"""
The first two variables retrieved from a SPARQL-query are compared using the Levenshtein distance.
The distance value is then stored in Literal object and added to the query results.
Example:
Query:
PREFIX custom: //custom/ # Note: this part refereces to the custom function
SELECT ?label1 ?label2 ?levenshtein WHERE {
BIND("Hello" AS ?label1)
BIND("World" AS ?label2)
BIND(custom:levenshtein(?label1, ?label2) AS ?levenshtein)
}
Retrieve:
?label1 ?label2
Calculation:
levenshtein_distance(?label1, ?label2) = distance
Output:
Save distance in Literal object.
:param ctx: <class 'rdflib.plugins.sparql.sparql.QueryContext'>
:param part: <class 'rdflib.plugins.sparql.parserutils.CompValue'>
:return: <class 'rdflib.plugins.sparql.processor.SPARQLResult'>
"""
# This part holds basic implementation for adding new functions
if part.name == 'Extend':
cs = []
# Information is retrieved and stored and passed through a generator
for c in evalPart(ctx, part.p):
# Checks if the function holds an internationalized resource identifier
# This will check if any custom functions are added.
if hasattr(part.expr, 'iri'):
# From here the real calculations begin.
# First we get the variable arguments, for example ?label1 and ?label2
argument1 = str(_eval(part.expr.expr[0], c.forget(ctx, _except=part.expr._vars)))
argument2 = str(_eval(part.expr.expr[1], c.forget(ctx, _except=part.expr._vars)))
# Here it checks if it can find our levenshtein IRI (example: //custom/levenshtein)
# Please note that IRI and URI are almost the same.
# Earlier this has been defined with the following:
# namespace = Namespace('//custom/')
# levenshtein = rdflib.term.URIRef(namespace + 'levenshtein')
if part.expr.iri == levenshtein:
# After finding the correct path for the custom SPARQL function the evaluation can begin.
# Here the levenshtein distance is calculated using ?label1 and ?label2 and stored as an Literal object.
# This object is than stored as an output value of the SPARQL-query (example: ?levenshtein)
evaluation = Literal(levenshtein_distance(argument1, argument2))
# Standard error handling and return statements
else:
raise SPARQLError('Unhandled function {}'.format(part.expr.iri))
else:
evaluation = _eval(part.expr, c.forget(ctx, _except=part._vars))
if isinstance(evaluation, SPARQLError):
raise evaluation
cs.append(c.merge({part.var: evaluation}))
return cs
raise NotImplementedError()
namespace = Namespace('//custom/')
levenshtein = rdflib.term.URIRef(namespace + 'levenshtein')
query = """
PREFIX custom: <%s>
SELECT ?label1 ?label2 ?levenshtein WHERE {
BIND("Hello" AS ?label1)
BIND("World" AS ?label2)
BIND(custom:levenshtein(?label1, ?label2) AS ?levenshtein)
}
""" % (namespace,)
# Save custom function in custom evaluation dictionary.
rdflib.plugins.sparql.CUSTOM_EVALS['SPARQL_levenshtein'] = SPARQL_levenshtein
for row in rdflib.Graph().query(query):
print(row)
To answer your question: "What is a good way to hook a custom SPARQL function into rdflib?
Currently I'm developing a class that handles RDF data and I believe it might be best to implement the following code in to __init__function.
For example:
class ClassName():
"""DOCSTRING"""
def __init__(self):
"""DOCSTRING"""
# Save custom function in custom evaluation dictionary.
rdflib.plugins.sparql.CUSTOM_EVALS['SPARQL_levenshtein'] = SPARQL_levenshtein
Please note, this SPARQL function will only work for the endpoint on which it is implemented. Even though the SPARQL syntax in the query is correct, it is not possible applying the function in SPARQL-queries used for databases like DBPedia. The DBPedia endpoint does not support this custom function (yet).
I'm trying to replicate the following successful cURL operation with Grinder.
curl -X PUT -d "title=Here%27s+the+title&content=Here%27s+the+content&signature=myusername%3A3ad1117dab0ade17bdbd47cc8efd5b08" http://www.mysite.com/api
Here's my script:
from net.grinder.script import Test
from net.grinder.script.Grinder import grinder
from net.grinder.plugin.http import HTTPRequest
from HTTPClient import NVPair
import hashlib
test1 = Test(1, "Request resource")
request1 = HTTPRequest(url="http://www.mysite.com/api")
test1.record(request1)
log = grinder.logger.info
test1.record(log)
m = hashlib.md5()
class TestRunner:
def __call__(self):
params = [NVPair("title","Here's the title"),NVPair("content", "Here's the content")]
params.sort(key=lambda param: param.getName())
ps = ""
for param in params:
ps = ps + param.getValue() + ":"
ps = ps + "myapikey"
m.update(ps)
params.append(NVPair("signature", ("myusername:" + m.hexdigest())))
request1.setFormData(tuple(params))
result = request1.PUT()
The test runs okay, but it seems that my script doesn't actually send any of the params data to the API, and I can't work out why. There are no errors generated, but I get a 401 Unauthorized response from the API, indicating that a successful PUT request reached it, but obviously without a signature the request was rejected.
This isn't exactly an answer, more of a workaround that I came up with, that I've decided to post since this question hasn't yet received any responses, and it may help anyone else trying to achieve the same thing.
The workaround is basically to use the httplib and urllib modules to build and make the PUT request instead of the HTTPClient module.
import hashlib
import httplib, urllib
....
params = [("title", "Here's the title"),("content", "Here's the content")]
params.sort(key=lambda param: param[0])
ps = ""
for param in params:
ps = ps + param[1] + ":"
ps = ps + "myapikey"
m = hashlib.md5()
m.update(ps)
params.append(("signature", "myusername:" + m.hexdigest()))
params = urllib.urlencode(params)
print params
headers = {"Content-type": "application/x-www-form-urlencoded"}
conn = httplib.HTTPConnection("www.mysite.com:80")
conn.request("PUT", "/api", params, headers)
response = conn.getresponse()
print response.status, response.reason
print response.read()
conn.close()
(Based on the example at the bottom of this documentation page.)
You have to refer to the multi-form posting example in Grinder script gallery, but changing the Post to Put. It works for me.
files = ( NVPair("self", "form.py"), )
parameters = ( NVPair("run number", str(grinder.runNumber)), )
# This is the Jython way of creating an NVPair[] Java array
# with one element.
headers = zeros(1, NVPair)
# Create a multi-part form encoded byte array.
data = Codecs.mpFormDataEncode(parameters, files, headers)
grinder.logger.output("Content type set to %s" % headers[0].value)
# Call the version of POST that takes a byte array.
result = request1.PUT("/upload", data, headers)