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Jun 20, 2024
What is serverless GPU computing?
Jun 3, 2024
Increase GPU Quota on AWS: A Comprehensive Guide
May 22, 2024
From Naive RAGs to Advanced: Improving your Retrieval
Get started with Tensorfuse today.
Deploy in minutes, scale in seconds.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
Get started with Tensorfuse today.
Deploy in minutes, scale in seconds.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
Get started with Tensorfuse today.
Deploy in minutes, scale in seconds.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
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