crajah

import os
import io
import boto3
import json
import csv

# Get the Endpoint name form the Environment.
ENDPOINT_NAME = os.environ['ENDPOINT_NAME']

# Make a SageMaker Runtime reference.

crajah

from sagemaker.huggingface import HuggingFaceModel
from sagemaker.serverless import ServerlessInferenceConfig
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'dalle-mini/dalle-mini',
	'HF_TASK':'image-classification'
}

crajah

from sagemaker.huggingface import HuggingFaceModel
from sagemaker.serverless import ServerlessInferenceConfig
import sagemaker

role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
	'HF_MODEL_ID':'dalle-mini/dalle-mini',
	'HF_TASK':'image-classification'
}

crajah

  object database extends Graph(databaseName = "graphTest") {
    val airports: DocumentCollection[Airport] = vertex[Airport]
    val flights: DocumentCollection[Flight] = edge[Flight]
    val airportSearch: View[Airport] = view(
      name = "airportSearch",
      collection = airports,
      analyzers = List(Analyzer.Identity),
      includeAllFields = true,
      fields = Airport.name -> List(Analyzer.TextEnglish)
    )

crajah

package parallelai.loop.api.dao

import java.net.URL

import com.outr.arango.CollectionType._
import com.outr.arango._
import com.outr.arango.query._
import com.outr.arango.transaction.Transaction
import io.youi.net.URL
import parallelai.loop.api.model.dto.Long