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<tabber> | |||
| | |-|HTTPS= | ||
<pre> | |||
#Be sure to have git-lfs installed (https://git-lfs.com) | |||
git lfs install | |||
git clone https://huggingface.co/SZTAKI-HLT/hubert-base-cc | |||
| | #To clone the repo without large files – just their pointers | ||
| | #prepend git clone with the following env var: | ||
GIT_LFS_SKIP_SMUDGE=1 | |||
</pre> | |||
|-|SSH= | |||
<pre> | |||
#Be sure to have git-lfs installed (https://git-lfs.com) | |||
git lfs install | |||
git clone [email protected]:SZTAKI-HLT/hubert-base-cc | |||
#To clone the repo without large files – just their pointers | |||
#prepend git clone with the following env var: | |||
GIT_LFS_SKIP_SMUDGE=1 | |||
</pre> | |||
</tabber> | |||
==Hugging Face Transformers Library== | |||
<pre> | |||
from transformers import AutoModel | |||
model = AutoModel.from_pretrained("SZTAKI-HLT/hubert-base-cc") | |||
</pre> | |||
==Deployment== | |||
===Inference API=== | |||
<tabber> | |||
</tabber> | |||
===Amazon SageMaker=== | |||
<tabber> | |||
|-|Automatic Speech Recognition= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'automatic-speech-recognition' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'automatic-speech-recognition' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | }} | ||
|-|Conversational= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'conversational' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'conversational' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Feature Extraction= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'feature-extraction' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'feature-extraction' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Fill-Mask= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'fill-mask' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'fill-mask' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Image Classification= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'image-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'image-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Question Answering= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'question-answering' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'question-answering' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Summarization= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'summarization' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'summarization' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Table Question Answering= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'table-question-answering' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'table-question-answering' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Text Classification= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'text-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'text-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Text Generation= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'text-generation' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'text-generation' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Text2Text Generation= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'text2text-generation' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'text2text-generation' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Token Classification= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'token-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'token-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Translation= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'translation' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'translation' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
|-|Zero-Shot Classification= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import sagemaker | |||
role = sagemaker.get_execution_role() | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'zero-shot-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
from sagemaker.huggingface import HuggingFaceModel | |||
import boto3 | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
# Hub Model configuration. https://huggingface.co/models | |||
hub = { | |||
'HF_MODEL_ID':'SZTAKI-HLT/hubert-base-cc', | |||
'HF_TASK':'zero-shot-classification' | |||
} | |||
# create Hugging Face Model Class | |||
huggingface_model = HuggingFaceModel( | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
env=hub, | |||
role=role, | |||
) | |||
# deploy model to SageMaker Inference | |||
predictor = huggingface_model.deploy( | |||
initial_instance_count=1, # number of instances | |||
instance_type='ml.m5.xlarge' # ec2 instance type | |||
) | |||
predictor.predict({ | |||
'inputs': No input example has been defined for this model task. | |||
}) | |||
</pre> | |||
}} | |||
</tabber> | |||
===Spaces=== | |||
<pre> | |||
undefined | |||
</pre> | |||
==Training== | |||
===Amazon SageMaker=== | |||
<tabber> | |||
|-|Causal Language Modeling= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_clm.py', | |||
source_dir='./examples/pytorch/language-modeling', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_clm.py', | |||
source_dir='./examples/pytorch/language-modeling', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
|-|Masked Language Modeling= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_mlm.py', | |||
source_dir='./examples/pytorch/language-modeling', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_mlm.py', | |||
source_dir='./examples/pytorch/language-modeling', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
|-|Question Answering= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_qa.py', | |||
source_dir='./examples/pytorch/question-answering', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_qa.py', | |||
source_dir='./examples/pytorch/question-answering', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
|-|Summarization= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_summarization.py', | |||
source_dir='./examples/pytorch/seq2seq', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_summarization.py', | |||
source_dir='./examples/pytorch/seq2seq', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
|-|Text Classification= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_glue.py', | |||
source_dir='./examples/pytorch/text-classification', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_glue.py', | |||
source_dir='./examples/pytorch/text-classification', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
|-|Token Classification= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/token-classification | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_ner.py', | |||
source_dir='./examples/pytorch/token-classification', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/token-classification | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_ner.py', | |||
source_dir='./examples/pytorch/token-classification', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
|-|Translation= | |||
{{#tag:tabber| | |||
AWS= | |||
<pre> | |||
import sagemaker | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
role = sagemaker.get_execution_role() | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_translation.py', | |||
source_dir='./examples/pytorch/seq2seq', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
{{!}}-{{!}} | |||
Local Machine= | |||
<pre> | |||
import sagemaker | |||
import boto3 | |||
from sagemaker.huggingface import HuggingFace | |||
# gets role for executing training job | |||
iam_client = boto3.client('iam') | |||
role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] | |||
hyperparameters = { | |||
'model_name_or_path':'SZTAKI-HLT/hubert-base-cc', | |||
'output_dir':'/opt/ml/model' | |||
# add your remaining hyperparameters | |||
# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq | |||
} | |||
# git configuration to download our fine-tuning script | |||
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} | |||
# creates Hugging Face estimator | |||
huggingface_estimator = HuggingFace( | |||
entry_point='run_translation.py', | |||
source_dir='./examples/pytorch/seq2seq', | |||
instance_type='ml.p3.2xlarge', | |||
instance_count=1, | |||
role=role, | |||
git_config=git_config, | |||
transformers_version='4.17.0', | |||
pytorch_version='1.10.2', | |||
py_version='py38', | |||
hyperparameters = hyperparameters | |||
) | |||
# starting the train job | |||
huggingface_estimator.fit() | |||
</pre> | |||
}} | |||
</tabber> |