#Be sure to have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/facebook/convnext-large-224 #To clone the repo without large files – just their pointers #prepend git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
Facebook/convnext-large-224 model: Difference between revisions
(Created page with "{{Model infobox | hugging-face-uri = facebook/convnext-large-224 | creator = | type = Computer Vision | task = Image Classification | library = PyTorch, TensorFlow, Transformers | dataset = imagenet-1k | language = | paper = | license = arxiv:2201.03545, apache-2.0 | related-to = convnext, vision, AutoTrain Compatible | all-tags = Image Classification, PyTorch, TensorFlow, Transformers, imagenet-1k, convnext, vision, AutoTrain Compatible, arxiv:2201.03545, License: ap...") |
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| dataset = imagenet-1k | | dataset = imagenet-1k | ||
| language = | | language = | ||
| paper | | paper = arxiv:2201.03545 | ||
| license = apache-2.0 | |||
| related-to = convnext, vision, AutoTrain Compatible | | related-to = convnext, vision, AutoTrain Compatible | ||
| all-tags = Image Classification, PyTorch, TensorFlow, Transformers, imagenet-1k, convnext, vision, AutoTrain Compatible, arxiv:2201.03545, License: apache-2.0 | | all-tags = Image Classification, PyTorch, TensorFlow, Transformers, imagenet-1k, convnext, vision, AutoTrain Compatible, arxiv:2201.03545, License: apache-2.0 | ||
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==Model Card== | ==Model Card== | ||
==Comments== | |||
<comments /> |
Latest revision as of 03:28, 23 May 2023
Hugging Face
Name
convnext-large-224
User / Organization
Type
Task
Library
Dataset
Paper
License
Related to
Facebook/convnext-large-224 model is a Computer Vision model used for Image Classification.
Model Description
Clone Model Repository
#Be sure to have git-lfs installed (https://git-lfs.com) git lfs install git clone [email protected]:facebook/convnext-large-224 #To clone the repo without large files – just their pointers #prepend git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
Hugging Face Transformers Library
from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("facebook/convnext-large-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnext-large-224")
Deployment
Inference API
import requests API_URL = "https://api-inference.huggingface.co/models/facebook/convnext-large-224" headers = {"Authorization": f"Bearer {API_TOKEN}"} def query(filename): with open(filename, "rb") as f: data = f.read() response = requests.post(API_URL, headers=headers, data=data) return response.json() output = query("cats.jpg")
async function query(filename) { const data = fs.readFileSync(filename); const response = await fetch( "https://api-inference.huggingface.co/models/facebook/convnext-large-224", { headers: { Authorization: "Bearer {API_TOKEN}" }, method: "POST", body: data, } ); const result = await response.json(); return result; } query("cats.jpg").then((response) => { console.log(JSON.stringify(response)); });
curl https://api-inference.huggingface.co/models/facebook/convnext-large-224 \ -X POST \ --data-binary '@cats.jpg' \ -H "Authorization: Bearer {API_TOKEN}"
Amazon SageMaker
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'facebook/convnext-large-224', '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': "cats.jpg" })
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':'facebook/convnext-large-224', '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': "cats.jpg" })
Spaces
import gradio as gr gr.Interface.load("models/facebook/convnext-large-224").launch()
Training
Amazon SageMaker
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'facebook/convnext-large-224', '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()
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':'facebook/convnext-large-224', '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()
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