For example, there is a Jenkins stage with defined variable - jenkinsVariable = 5
How can I access that variable in the powershell block? Is that possible like this or I need to call powershell.ps1 with parameters in order to transfer variable from jenkins to powershell area?
stage (testStage)
{
def jenkinsVariable = 5;
def result = powershell (returnStdout: true, script: 'Write-Output jenkinsVariable')
}
I have an LSH table builder utility class which goes as follows (referred from here):
class BuildLSHTable:
def __init__(self, hash_size=8, dim=2048, num_tables=10, lsh_file="lsh_table.pkl"):
self.hash_size = hash_size
self.dim = dim
self.num_tables = num_tables
self.lsh = LSH(self.hash_size, self.dim, self.num_tables)
self.embedding_model = embedding_model
self.lsh_file = lsh_file
def train(self, training_files):
for id, training_file in enumerate(training_files):
image, label = training_file
if len(image.shape) < 4:
image = image[None, ...]
features = self.embedding_model.predict(image)
self.lsh.add(id, features, label)
with open(self.lsh_file, "wb") as handle:
pickle.dump(self.lsh,
handle, protocol=pickle.HIGHEST_PROTOCOL)
I then execute the following in order to build my LSH table:
training_files = zip(images, labels)
lsh_builder = BuildLSHTable()
lsh_builder.train(training_files)
Now, when I am trying to do this via Apache Beam (code below), it's throwing:
TypeError: can't pickle tensorflow.python._pywrap_tf_session.TF_Operation objects
Code used for Beam:
def generate_lsh_table(args):
options = beam.options.pipeline_options.PipelineOptions(**args)
args = namedtuple("options", args.keys())(*args.values())
with beam.Pipeline(args.runner, options=options) as pipeline:
(
pipeline
| 'Build LSH Table' >> beam.Map(
args.lsh_builder.train, args.training_files)
)
This is how I am invoking the beam runner:
args = {
"runner": "DirectRunner",
"lsh_builder": lsh_builder,
"training_files": training_files
}
generate_lsh_table(args)
Apache Beam pipelines should be converted to a standard (for example, proto) format before being executed. As a part of this certain pipeline objects such as DoFns get serialized (picked). If your DoFns have instance variables that cannot be serialized this process cannot continue.
One way to solve this is to load/define such instance objects or modules during execution instead of creating and storing such objects during pipeline submission. This might require adjusting your pipeline.
I have the below test_dss.py file which is used for pytest:
import dataikuapi
import pytest
def setup_list():
client = dataikuapi.DSSClient("{DSS_URL}", "{APY_KEY}")
client._session.verify = False
project = client.get_project("{DSS_PROJECT}")
# Check that there is at least one scenario TEST_XXXXX & that all test scenarios pass
scenarios = project.list_scenarios()
scenarios_filter = [obj for obj in scenarios if obj["name"].startswith("TEST")]
return scenarios_filter
def test_check_scenario_exist():
assert len(setup_list()) > 0, "You need at least one test scenario (name starts with 'TEST_')"
#pytest.mark.parametrize("scenario", setup_list())
def test_scenario_run(scenario, params):
client = dataikuapi.DSSClient(params['host'], params['api'])
client._session.verify = False
project = client.get_project(params['project'])
scenario_id = scenario["id"]
print("Executing scenario ", scenario["name"])
scenario_result = project.get_scenario(scenario_id).run_and_wait()
assert scenario_result.get_details()["scenarioRun"]["result"]["outcome"] == "SUCCESS", "test " + scenario[
"name"] + " failed"
My issue is with setup_list function, which able to get only hard coded values for {DSS_URL}, {APY_KEY}, {PROJECT}. I'm not able to use PARAMS or other method like in test_scenario_run
any idea how I can pass the PARAMS also to this function?
The parameters in the mark.parametrize marker are read at load time, where the information about the config parameters is not yet available. Therefore you have to parametrize the test at runtime, where you have access to the configuration.
This can be done in pytest_generate_tests (which can live in your test module):
#pytest.hookimpl
def pytest_generate_tests(metafunc):
if "scenario" in metafunc.fixturenames:
host = metafunc.config.getoption('--host')
api = metafuc.config.getoption('--api')
project = metafuc.config.getoption('--project')
metafunc.parametrize("scenario", setup_list(host, api, project))
This implies that your setup_list function takes these parameters:
def setup_list(host, api, project):
client = dataikuapi.DSSClient(host, api)
client._session.verify = False
project = client.get_project(project)
...
And your test just looks like this (without the parametrize marker, as the parametrization is now done in pytest_generate_tests):
def test_scenario_run(scenario, params):
scenario_id = scenario["id"]
...
The parametrization is now done at run-time, so it behaves the same as if you had placed a parametrize marker in the test.
And the other test that tests setup_list now has also to use the params fixture to get the needed arguments:
def test_check_scenario_exist(params):
assert len(setup_list(params["host"], params["api"], params["project"])) > 0,
"You need at least ..."
When I implement this part of this python code in Azure Databricks:
class clustomTransformations(Transformer):
<code>
custom_transformer = customTransformations()
....
pipeline = Pipeline(stages=[custom_transformer, assembler, scaler, rf])
pipeline_model = pipeline.fit(sample_data)
pipeline_model.save(<your path>)
When I attempt to save the pipeline, I get this:
AttributeError: 'customTransformations' object has no attribute '_to_java'
Any work arounds?
It seems like there is no easy workaround but to try and implement the _to_java method, as is suggested here for StopWordsRemover:
Serialize a custom transformer using python to be used within a Pyspark ML pipeline
def _to_java(self):
"""
Convert this instance to a dill dump, then to a list of strings with the unicode integer values of each character.
Use this list as a set of dumby stopwords and store in a StopWordsRemover instance
:return: Java object equivalent to this instance.
"""
dmp = dill.dumps(self)
pylist = [str(ord(d)) for d in dmp] # convert byes to string integer list
pylist.append(PysparkObjId._getPyObjId()) # add our id so PysparkPipelineWrapper can id us.
sc = SparkContext._active_spark_context
java_class = sc._gateway.jvm.java.lang.String
java_array = sc._gateway.new_array(java_class, len(pylist))
for i in xrange(len(pylist)):
java_array[i] = pylist[i]
_java_obj = JavaParams._new_java_obj(PysparkObjId._getCarrierClass(javaName=True), self.uid)
_java_obj.setStopWords(java_array)
return _java_obj
I need to understand how to deploy models on Google Cloud ML. My first task is to deploy a very simple text classifier on the service. I do it in the following steps (could perhaps be shortened to fewer steps, if so, feel free to let me know):
Define the model using Keras and export to YAML
Load up YAML and export as a Tensorflow SavedModel
Upload model to Google Cloud Storage
Deploy model from storage to Google Cloud ML
Set the upload model version as default on the models website.
Run model with a sample input
I've finally made step 1-5 work, but now I get this strange error seen below when running the model. Can anyone help? Details on the steps is below. Hopefully, it can also help others that are stuck on one of the previous steps. My model works fine locally.
I've seen Deploying Keras Models via Google Cloud ML and Export a basic Tensorflow model to Google Cloud ML, but they seem to be stuck on other steps of the process.
Error
Prediction failed: Exception during model execution: AbortionError(code=StatusCode.INVALID_ARGUMENT, details="In[0] is not a matrix
[[Node: MatMul = MatMul[T=DT_FLOAT, _output_shapes=[[-1,64]], transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](Mean, softmax_W/read)]]")
Step 1
# import necessary classes from Keras..
model_input = Input(shape=(maxlen,), dtype='int32')
embed = Embedding(input_dim=nb_tokens,
output_dim=256,
mask_zero=False,
input_length=maxlen,
name='embedding')
x = embed(model_input)
x = GlobalAveragePooling1D()(x)
outputs = [Dense(nb_classes, activation='softmax', name='softmax')(x)]
model = Model(input=[model_input], output=outputs, name="fasttext")
# export to YAML..
Step 2
from __future__ import print_function
import sys
import os
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
import keras
from keras import backend as K
from keras.models import model_from_config, model_from_yaml
from optparse import OptionParser
EXPORT_VERSION = 1 # for us to keep track of different model versions (integer)
def export_model(model_def, model_weights, export_path):
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
K.set_learning_phase(0) # all new operations will be in test mode from now on
yaml_file = open(model_def, 'r')
yaml_string = yaml_file.read()
yaml_file.close()
model = model_from_yaml(yaml_string)
# force initialization
model.compile(loss='categorical_crossentropy',
optimizer='adam')
Wsave = model.get_weights()
model.set_weights(Wsave)
# weights are not loaded as I'm just testing, not really deploying
# model.load_weights(model_weights)
print(model.input)
print(model.output)
pred_node_names = output_node_names = 'Softmax:0'
num_output = 1
export_path_base = export_path
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes('initial'))
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
x = model.input
y = model.output
values, indices = tf.nn.top_k(y, 5)
table = tf.contrib.lookup.index_to_string_table_from_tensor(tf.constant([str(i) for i in xrange(5)]))
prediction_classes = table.lookup(tf.to_int64(indices))
classification_inputs = tf.saved_model.utils.build_tensor_info(model.input)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(inputs={tf.saved_model.signature_constants.CLASSIFY_INPUTS: classification_inputs},
outputs={tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES: classification_outputs_classes, tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES: classification_outputs_scores},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={'predict_images': prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: classification_signature,},
legacy_init_op=legacy_init_op)
builder.save()
print('Done exporting!')
raise SystemExit
if __name__ == '__main__':
usage = "usage: %prog [options] arg"
parser = OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) < 3:
raise ValueError("Too few arguments!")
model_def = args[0]
model_weights = args[1]
export_path = args[2]
export_model(model_def, model_weights, export_path)
Step 3
gsutil cp -r fasttext_cloud/ gs://quiet-notch-xyz.appspot.com
Step 4
from __future__ import print_function
from oauth2client.client import GoogleCredentials
from googleapiclient import discovery
from googleapiclient import errors
import time
projectID = 'projects/{}'.format('quiet-notch-xyz')
modelName = 'fasttext'
modelID = '{}/models/{}'.format(projectID, modelName)
versionName = 'Initial'
versionDescription = 'Initial release.'
trainedModelLocation = 'gs://quiet-notch-xyz.appspot.com/fasttext/'
credentials = GoogleCredentials.get_application_default()
ml = discovery.build('ml', 'v1', credentials=credentials)
# Create a dictionary with the fields from the request body.
requestDict = {'name': modelName, 'description': 'Online predictions.'}
# Create a request to call projects.models.create.
request = ml.projects().models().create(parent=projectID, body=requestDict)
# Make the call.
try:
response = request.execute()
except errors.HttpError as err:
# Something went wrong, print out some information.
print('There was an error creating the model.' +
' Check the details:')
print(err._get_reason())
# Clear the response for next time.
response = None
raise
time.sleep(10)
requestDict = {'name': versionName,
'description': versionDescription,
'deploymentUri': trainedModelLocation}
# Create a request to call projects.models.versions.create
request = ml.projects().models().versions().create(parent=modelID,
body=requestDict)
# Make the call.
try:
print("Creating model setup..", end=' ')
response = request.execute()
# Get the operation name.
operationID = response['name']
print('Done.')
except errors.HttpError as err:
# Something went wrong, print out some information.
print('There was an error creating the version.' +
' Check the details:')
print(err._get_reason())
raise
done = False
request = ml.projects().operations().get(name=operationID)
print("Adding model from storage..", end=' ')
while (not done):
response = None
# Wait for 10000 milliseconds.
time.sleep(10)
# Make the next call.
try:
response = request.execute()
# Check for finish.
done = True # response.get('done', False)
except errors.HttpError as err:
# Something went wrong, print out some information.
print('There was an error getting the operation.' +
'Check the details:')
print(err._get_reason())
done = True
raise
print("Done.")
Step 5
Use website.
Step 6
def predict_json(instances, project='quiet-notch-xyz', model='fasttext', version=None):
"""Send json data to a deployed model for prediction.
Args:
project (str): project where the Cloud ML Engine Model is deployed.
model (str): model name.
instances ([Mapping[str: Any]]): Keys should be the names of Tensors
your deployed model expects as inputs. Values should be datatypes
convertible to Tensors, or (potentially nested) lists of datatypes
convertible to tensors.
version: str, version of the model to target.
Returns:
Mapping[str: any]: dictionary of prediction results defined by the
model.
"""
# Create the ML Engine service object.
# To authenticate set the environment variable
# GOOGLE_APPLICATION_CREDENTIALS=<path_to_service_account_file>
service = googleapiclient.discovery.build('ml', 'v1')
name = 'projects/{}/models/{}'.format(project, model)
if version is not None:
name += '/versions/{}'.format(version)
response = service.projects().predict(
name=name,
body={'instances': instances}
).execute()
if 'error' in response:
raise RuntimeError(response['error'])
return response['predictions']
Then run function with test input: predict_json({'inputs':[[18, 87, 13, 589, 0]]})
There is now a sample demonstrating the use of Keras on CloudML engine, including prediction. You can find the sample here:
https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census/keras
I would suggest comparing your code to that code.
Some additional suggestions that will still be relevant:
CloudML Engine currently only supports using a single signature (the default signature). Looking at your code, I think prediction_signature is more likely to lead to success, but you haven't made that the default signature. I suggest the following:
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,},
legacy_init_op=legacy_init_op)
If you are deploying to the service, then you would invoke prediction like so:
predict_json({'images':[[18, 87, 13, 589, 0]]})
If you are testing locally using gcloud ml-engine local predict --json-instances the input data is slightly different (matches that of the batch prediction service). Each newline-separated line looks like this (showing a file with two lines):
{'images':[[18, 87, 13, 589, 0]]}
{'images':[[21, 85, 13, 100, 1]]}
I don't actually know enough about the shape of model.x to ensure the data being sent is correct for your model.
By way of explanation, it may be insightful to consider the difference between the Classification and Prediction methods in SavedModel. One difference is that, when using tensorflow_serving, which is based on gRPC, which is strongly typed, Classification provides a strongly-typed signature that most classifiers can use. Then you can reuse the same client on any classifier.
That's not overly useful when using JSON since JSON isn't strongly typed.
One other difference is that, when using tensorflow_serving, Prediction accepts column-based inputs (a map from feature name to every value for that feature in the whole batch) whereas Classification accepts row based inputs (each input instance/example is a row).
CloudML abstracts that away a bit and always requires row-based inputs (a list of instances). We even though we only officially support Prediction, but Classification should work as well.