prediction-try-java-python | Sample application illustrating use of the Google | GCP library
kandi X-RAY | prediction-try-java-python Summary
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- Handle POST
- Get user credentials
- Parses the given json file
- Forward a request to the given path
- Get server credentials
- Invoked to retrieve the OAuth code
- Handle an HTTP GET request
prediction-try-java-python Key Features
prediction-try-java-python Examples and Code Snippets
Trending Discussions on GCP
Trending Discussions on GCP
QUESTION
I have a pyspark job available on GCP Dataproc to be triggered on airflow as shown below:
config = help.loadJSON("batch/config_file")
MY_PYSPARK_JOB = {
"reference": {"project_id": "my_project_id"},
"placement": {"cluster_name": "my_cluster_name"},
"pyspark_job": {
"main_python_file_uri": "gs://file/loc/my_spark_file.py"]
"properties": config["spark_properties"]
"args":
},
}
I need to supply command line arguments to this pyspark job as show below [this is how I am running my pyspark job from command line]:
spark-submit gs://file/loc/my_spark_file.py --arg1 val1 --arg2 val2
I am providing the arguments to my pyspark job using "configparser". Therefore, arg1 is the key and val1 is the value from my spark-submit commant above.
How do I define the "args" param in the "MY_PYSPARK_JOB" defined above [equivalent to my command line arguments]?
ANSWER
Answered 2022-Mar-28 at 08:18You have to pass a Sequence[str]
. If you check DataprocSubmitJobOperator you will see that the params job
implements a class google.cloud.dataproc_v1.types.Job.
class DataprocSubmitJobOperator(BaseOperator):
...
:param job: Required. The job resource. If a dict is provided, it must be of the same form as the protobuf message.
:class:`~google.cloud.dataproc_v1.types.Job`
So, on the section about job type pySpark
which is google.cloud.dataproc_v1.types.PySparkJob:
args Sequence[str] Optional. The arguments to pass to the driver. Do not include arguments, such as
--conf
, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
QUESTION
What is the equivalent of header=0
in pandas
, which recognises the first line as a heading in gspread
?
pandas import statement (correct)
import pandas as pd
# gcp / google sheets URL
df_URL = "https://docs.google.com/spreadsheets/d/1wKtvNfWSjPNC1fNmTfUHm7sXiaPyOZMchjzQBt1y_f8/edit?usp=sharing"
raw_dataset = pd.read_csv(df_URL, na_values='?',sep=';'
, skipinitialspace=True, header=0, index_col=None)
Using the gspread function, so far I import the data, change the first line to the heading then delete the first line after but this recognises everything in the DataFrame as a string. I would like to recognise the first line as a heading right away in the import statement.
gspread import statement that needs header=True equivalent
import pandas as pd
from google.colab import auth
auth.authenticate_user()
import gspread
from oauth2client.client import GoogleCredentials
# gcp / google sheets url
df_URL = "https://docs.google.com/spreadsheets/d/1wKtvNfWSjPNC1fNmTfUHm7sXiaPyOZMchjzQBt1y_f8/edit?usp=sharing"
# importing the data from Google Drive setup
gc = gspread.authorize(GoogleCredentials.get_application_default())
# read data and put it in dataframe
g_sheets = gc.open_by_url(df_URL)
df = pd.DataFrame(g_sheets.get_worksheet(0).get_all_values())
# change first row to header
df = df.rename(columns=df.iloc[0])
# drop first row
df.drop(index=df.index[0], axis=0, inplace=True)
ANSWER
Answered 2022-Mar-16 at 08:12Looking at the API documentation, you probably want to use:
df = pd.DataFrame(g_sheets.get_worksheet(0).get_all_records(head=1))
The .get_all_records
method returns a dictionary of with the column headers as the keys and a list of column values as the dictionary values. The argument head=
determines which row to use as keys; rows start from 1 and follow the numeration of the spreadsheet.
Since the values returned by .get_all_records()
are lists of strings, the data frame constructor, pd.DataFrame
, will return a data frame that is all strings. To convert it to floats, we need to replace the empty strings, and the the dash-only strings ('-'
) with NA-type values, then convert to float
.
Luckily pandas DataFrame has a convenient method for replacing values .replace
. We can pass it mapping from the string we want as NAs to None, which gets converted to NaN.
import pandas as pd
data = g_sheets.get_worksheet(0).get_all_records(head=1)
na_strings_map= {
'-': None,
'': None
}
df = pd.DataFrame(data).replace(na_strings_map).astype(float)
QUESTION
I'm trying to grab the latest secret version. Is there a way to do that without specifying the version number? Such as using the keyword "latest". I'm trying to avoid having to iterate through all the secret versions with a for loop as GCP documentation shows:
try (SecretManagerServiceClient client = SecretManagerServiceClient.create()) {
// Build the parent name.
SecretName projectName = SecretName.of(projectId, secretId);
// Get all versions.
ListSecretVersionsPagedResponse pagedResponse = client.listSecretVersions(projectName);
// List all versions and their state.
pagedResponse
.iterateAll()
.forEach(
version -> {
System.out.printf("Secret version %s, %s\n", version.getName(), version.getState());
});
}
ANSWER
Answered 2021-Sep-12 at 18:54import com.google.cloud.secretmanager.v1.AccessSecretVersionResponse;
import com.google.cloud.secretmanager.v1.SecretManagerServiceClient;
import com.google.cloud.secretmanager.v1.SecretVersionName;
import java.io.IOException;
public class AccessSecretVersion {
public static void accessSecretVersion() throws IOException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "your-project-id";
String secretId = "your-secret-id";
String versionId = "latest"; //<-- specify version
accessSecretVersion(projectId, secretId, versionId);
}
// Access the payload for the given secret version if one exists. The version
// can be a version number as a string (e.g. "5") or an alias (e.g. "latest").
public static void accessSecretVersion(String projectId, String secretId, String versionId)
throws IOException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (SecretManagerServiceClient client = SecretManagerServiceClient.create()) {
SecretVersionName secretVersionName = SecretVersionName.of(projectId, secretId, versionId);
// Access the secret version.
AccessSecretVersionResponse response = client.accessSecretVersion(secretVersionName);
// Print the secret payload.
//
// WARNING: Do not print the secret in a production environment - this
// snippet is showing how to access the secret material.
String payload = response.getPayload().getData().toStringUtf8();
System.out.printf("Plaintext: %s\n", payload);
}
}
}
QUESTION
I'm working on a Terraform project that will set up all the GCP resources needed for a large project spanning multiple GitHub repos. My goal is to be able to recreate the cloud infrastructure from scratch completely with Terraform.
The issue I'm running into is in order to setup build triggers with Terraform within GCP, the GitHub repo that is setting off the trigger first needs to be connected. Currently, I've only been able to do that manually via the Google Cloud Build dashboard. I'm not sure if this is possible via Terraform or with a script but I'm looking for any solution I can automate this with. Once the projects are connected updating everything with Terraform is working fine.
TLDR; How can I programmatically connect a GitHub project with a GCP project instead of using the dashboard?
ANSWER
Answered 2022-Feb-12 at 16:16Currently there is no way to programmatically connect a GitHub repo to a Google Cloud Project. This must be done manually via Google Cloud.
My workaround is to manually connect an "admin" project, build containers and save them to that project's artifact registry, and then deploy the containers from the registry in the programmatically generated project.
QUESTION
I'm unable to create a Cloud Function in my GCP project using GUI, but have admin roles for GCF, SA and IAM.
Here is the error message:
Missing necessary permission iam.serviceAccounts.actAs for cloud-client-api-gae on the service account serviceaccountname@DOMAIN.iam.gserviceaccount.com. Grant the role 'roles/iam.serviceAccountUser' to cloud-client-api-gae on the service account serviceaccountname@DOMAIN.iam.gserviceaccount.com.
cloud-client-api-gae
is not an SA nor User on my IAM list. It must be a creature living underneath Graphical User Interfrace.
I have Enabled API for GCF, AppEngine and I have Service Account Admin
role.
I had literally 0 search results when googling for cloud-client-api-gae
.
ANSWER
Answered 2022-Jan-18 at 13:53I contacted GCP support and it seems I was missing a single permission for my user: Service Account User
- that's it.
PS: Person from support didn't know what this thing called "cloud-client-api-gae" is.
QUESTION
I have a TypeScript project that has been deployed several times without any problems to Google App Engine, Standard environment, running Node 10. However, when I try to update the App Engine project to either Node 12 or 14 (by editing the engines.node
value in package.json
and the runtime
value in app.yaml
), the deploy fails, printing the following to the console:
> ####@1.0.1 prepare /workspace
> npm run gcp-build
> ####@1.0.1 gcp-build /workspace
> tsc -p .
sh: 1: tsc: not found
npm ERR! code ELIFECYCLE
npm ERR! syscall spawn
npm ERR! file sh
npm ERR! errno ENOENT
npm ERR! ####@1.0.1 gcp-build: `tsc -p .`
npm ERR! spawn ENOENT
npm ERR!
npm ERR! Failed at the ####@1.0.1 gcp-build script.
npm ERR! This is probably not a problem with npm. There is likely additional logging output above.
According to the following documentation, App Engine should be installing the modules listed in devDependencies
in package.json
prior to running the gcp-build
script (which it has been doing, as expected, when deploying with the Node version set to 10).
https://cloud.google.com/appengine/docs/standard/nodejs/running-custom-build-step
I can't find any documentation regarding how this App Engine deployment behavior has changed when targeting Node 12 or 14 rather than Node 10. Is there some configuration I am missing? Or does Google no longer install devDependencies
prior to running the gcp-build
script?
Here is the devDependencies
section of my package.json
(TypeScript is there):
"devDependencies": {
"@google-cloud/nodejs-repo-tools": "^3.3.0",
"@types/cors": "^2.8.12",
"@types/express": "^4.17.13",
"@types/pg": "^8.6.1",
"@types/request": "^2.48.7",
"tsc-watch": "^4.4.0",
"tslint": "^6.1.3",
"typescript": "^4.3.5"
},
ANSWER
Answered 2022-Jan-16 at 14:32I encountered the exact same problem and just put typescript in dependencies, not devDependencies.
It worked after that, but cannot assure that it is due to this change (since I have no proof of that).
QUESTION
I am following this article ,for submit a job to an existing Dataproc cluster via a Dataproc API
For the following line of code :
// Configure the settings for the job controller client.
JobControllerSettings jobControllerSettings =
JobControllerSettings.newBuilder().setEndpoint(myEndpoint).build();
I am getting the following errors :
SEVERE: Servlet.service() for servlet [dispatcherServlet] in context with path [] threw exception [Handler dispatch failed; nested exception is java.lang.NoSuchMethodError: 'com.google.api.gax.core.GoogleCredentialsProvider$Builder com.google.api.gax.core.GoogleCredentialsProvider$Builder.setUseJwtAccessWithScope(boolean)'] with root cause
java.lang.NoSuchMethodError: 'com.google.api.gax.core.GoogleCredentialsProvider$Builder com.google.api.gax.core.GoogleCredentialsProvider$Builder.setUseJwtAccessWithScope(boolean)'
In my pom I used the following dependencies :
com.google.cloud
libraries-bom
24.1.2
pom
import
org.springframework.cloud
spring-cloud-gcp-dependencies
1.2.8.RELEASE
pom
import
And added the dataproc
com.google.cloud
google-cloud-dataproc
Any help what I am missing here?
ANSWER
Answered 2022-Jan-14 at 19:22The method com.google.api.gax.core.GoogleCredentialsProvider$Builder com.google.api.gax.core.GoogleCredentialsProvider$Builder.setUseJwtAccessWithScope(boolean)
was introduced in com.google.api:gax
in version 2.3.0.
Can you
run
mvn dependency:tree
and confirm that your version ofcom.google.api:gax
is above version 2.3.0?upgrade all Google libraries to the latest version?
Here is a similar issue found on the internet.
QUESTION
I'm currently building PoC Apache Beam pipeline in GCP Dataflow. In this case, I want to create streaming pipeline with main input from PubSub and side input from BigQuery and store processed data back to BigQuery.
Side pipeline code
side_pipeline = (
p
| "periodic" >> PeriodicImpulse(fire_interval=3600, apply_windowing=True)
| "map to read request" >>
beam.Map(lambda x:beam.io.gcp.bigquery.ReadFromBigQueryRequest(table=side_table))
| beam.io.ReadAllFromBigQuery()
)
Function with side input code
def enrich_payload(payload, equipments):
id = payload["id"]
for equipment in equipments:
if id == equipment["id"]:
payload["type"] = equipment["type"]
payload["brand"] = equipment["brand"]
payload["year"] = equipment["year"]
break
return payload
Main pipeline code
main_pipeline = (
p
| "read" >> beam.io.ReadFromPubSub(topic="projects/my-project/topics/topiq")
| "bytes to dict" >> beam.Map(lambda x: json.loads(x.decode("utf-8")))
| "transform" >> beam.Map(transform_function)
| "timestamping" >> beam.Map(lambda src: window.TimestampedValue(
src,
dt.datetime.fromisoformat(src["timestamp"]).timestamp()
))
| "windowing" >> beam.WindowInto(window.FixedWindows(30))
)
final_pipeline = (
main_pipeline
| "enrich data" >> beam.Map(enrich_payload, equipments=beam.pvalue.AsIter(side_pipeline))
| "store" >> beam.io.WriteToBigQuery(bq_table)
)
result = p.run()
result.wait_until_finish()
After deploy it to Dataflow, everything looks fine and no error. But then I noticed that enrich data
step has two nodes instead of one.
And also, the side input stuck as you can see it has Elements Added
with 21 counts in Input Collections and -
value in Elements Added
in Output Collections.
You can find the full pipeline code here and mock pubsub publisher here
I already follow all instruction in these documentations:
- https://beam.apache.org/documentation/patterns/side-inputs/
- https://beam.apache.org/releases/pydoc/2.35.0/apache_beam.io.gcp.bigquery.html
Yet still found this error. Please help me. Thanks!
ANSWER
Answered 2022-Jan-12 at 13:12Here you have a working example:
mytopic = ""
sql = "SELECT station_id, CURRENT_TIMESTAMP() timestamp FROM `bigquery-public-data.austin_bikeshare.bikeshare_stations` LIMIT 10"
def to_bqrequest(e, sql):
from apache_beam.io import ReadFromBigQueryRequest
yield ReadFromBigQueryRequest(query=sql)
def merge(e, side):
for i in side:
yield f"Main {e.decode('utf-8')} Side {i}"
pubsub = p | "Read PubSub topic" >> ReadFromPubSub(topic=mytopic)
side_pcol = (p | PeriodicImpulse(fire_interval=300, apply_windowing=False)
| "ApplyGlobalWindow" >> WindowInto(window.GlobalWindows(),
trigger=trigger.Repeatedly(trigger.AfterProcessingTime(5)),
accumulation_mode=trigger.AccumulationMode.DISCARDING)
| "To BQ Request" >> ParDo(to_bqrequest, sql=sql)
| ReadAllFromBigQuery()
)
final = (pubsub | "Merge" >> ParDo(merge, side=beam.pvalue.AsList(side_pcol))
| Map(logging.info)
)
p.run()
Note this uses a GlobalWindow
(so that both inputs have the same window). I used a processing time trigger so that the pane contains multiple rows. 5
was chosen arbitrarily, using 1
would work too.
Please note matching the data between side and main inputs is non deterministic, and you may see fluctuating values from older fired panes.
In theory, using FixedWindows
should fix this, but I cannot get the FixedWindows
to work.
QUESTION
echo Give yearmonth "yyyyMM"
setlocal enabledelayedexpansion
SET /p yearmonth=
SET ClientName[0]=abc
SET ClientName[1]=def
SET i = 0
:myLoop
if defined ClientName[%i%] (
call bq query --use_legacy_sql=false "CREATE EXTERNAL TABLE `test.!ClientName[%%i]!.%yearmonth%` OPTIONS (format = 'CSV',skip_leading_rows = 1 uris = ['gs://test/!ClientName[%%i]!/AWS/%yearmonth%/Metrics/data/*.csv'])"
set /a "i+=1"
GOTO :myLoop
)
Hi, I am trying to create a batch so that i can run Multiple BIG QUERY at once. Above i tried to write a batch script putting command in a loop .
I am trying to create a table by using yearmonth as user input and then create array to create a table with different client name .
But I am unable to print if i =0 ClientName[i] = abc in a call query i am using !ClientName[%%i]! to print but its not working.
Call query inside loop is not running in GCP console, when i executed the bat file .
Can you please help me resolve this
ANSWER
Answered 2022-Jan-09 at 11:04It is bad practice to
set
variables as standalone alphabetical characters likei
. One reason is exactly as you have experienced, you have confusedfor
metavariable%%i
with aset
variable%i%
.You are expanding in the loop, but have not
enabledelayedexpansion
so there are 2 ways, which we will get to in a second.set
ting variables should not have spaces before or after=
excluding the likes ofset /a
So, Method 1, without delayedexpansion
(note how the variables are used with double %%
in the loop with the call
command).
@echo off
echo Give yearmonth "yyyyMM"
SET /p yearmonth=
SET ClientName[0]=abc
SET ClientName[1]=def
SET num=0
:myLoop
if defined ClientName[%num%] (
call bq query --use_legacy_sql=false "CREATE EXTERNAL TABLE `test.%%ClientName[%num%]%%.%yearmonth%` OPTIONS (format = 'CSV',skip_leading_rows = 1 uris = ['gs://test/%%ClientName[%num%]%%/AWS/%yearmonth%/Metrics/data/*.csv'])"
set /a num+=1
GOTO :myLoop
)
Method 2: (better method using delayedexpansion
)
@echo off
setlocal enabledelayedexpansion
echo Give yearmonth "yyyyMM"
SET /p yearmonth=
SET ClientName[0]=abc
SET ClientName[1]=def
SET num=0
:myLoop
if defined ClientName[%num%] (
call bq query --use_legacy_sql=false "CREATE EXTERNAL TABLE `test.!ClientName[%num%]!.%yearmonth%` OPTIONS (format = 'CSV',skip_leading_rows = 1 uris = ['gs://test/!ClientName[%num%]!/AWS/%yearmonth%/Metrics/data/*.csv'])"
set /a num+=1
GOTO :myLoop
)
QUESTION
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
ANSWER
Answered 2021-Dec-21 at 14:35I'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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install prediction-try-java-python
You can use prediction-try-java-python like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the prediction-try-java-python component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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