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Update Sagemaker Neo Notebooks examples to PyTorch 1.6 #2018

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Feb 23, 2021
Merged

Update Sagemaker Neo Notebooks examples to PyTorch 1.6 #2018

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42 changes: 21 additions & 21 deletions sagemaker_neo_compilation_jobs/pytorch_torchvision/code/resnet18.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

import numpy as np
import torch
import neopytorch
import torchvision.transforms as transforms
from PIL import Image # Training container doesn't have this package

Expand Down Expand Up @@ -55,24 +56,23 @@ def transform_fn(model, payload, request_content_type,
def model_fn(model_dir):

logger.info('model_fn')
with torch.neo.config(model_dir=model_dir, neo_runtime=True):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The compiled model is saved as "compiled.pt"
model = torch.jit.load(os.path.join(model_dir, 'compiled.pt'))
model = model.to(device)

# It is recommended to run warm-up inference during model load
sample_input_path = os.path.join(model_dir, 'sample_input.pkl')
with open(sample_input_path, 'rb') as input_file:
model_input = pickle.load(input_file)
if torch.is_tensor(model_input):
model_input = model_input.to(device)
model(model_input)
elif isinstance(model_input, tuple):
model_input = (inp.to(device)
for inp in model_input if torch.is_tensor(inp))
model(*model_input)
else:
print("Only supports a torch tensor or a tuple of torch tensors")

return model
neopytorch.config(model_dir=model_dir, neo_runtime=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The compiled model is saved as "compiled.pt"
model = torch.jit.load(os.path.join(model_dir, 'compiled.pt'), map_location=device)

# It is recommended to run warm-up inference during model load
sample_input_path = os.path.join(model_dir, 'sample_input.pkl')
with open(sample_input_path, 'rb') as input_file:
model_input = pickle.load(input_file)
if torch.is_tensor(model_input):
model_input = model_input.to(device)
model(model_input)
elif isinstance(model_input, tuple):
model_input = (inp.to(device)
for inp in model_input if torch.is_tensor(inp))
model(*model_input)
else:
print("Only supports a torch tensor or a tuple of torch tensors")

return model
2 changes: 1 addition & 1 deletion sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@
"data_shape = '{\"input0\":[1,3,224,224]}'\n",
"target_device = 'ml_c5'\n",
"framework = 'PYTORCH'\n",
"framework_version = '1.4.0'\n",
"framework_version = '1.6'\n",
"compiled_model_path = 's3://{}/{}/output'.format(bucket, compilation_job_name)"
]
},
Expand Down
38 changes: 19 additions & 19 deletions sagemaker_neo_compilation_jobs/pytorch_vgg19_bn/code/vgg19_bn_compiled.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

import numpy as np
import torch
import neopytorch
import torchvision.transforms as transforms
from PIL import Image # Training container doesn't have this package

Expand Down Expand Up @@ -55,24 +56,23 @@ def transform_fn(model, payload, request_content_type,
def model_fn(model_dir):

logger.info('model_fn')
with torch.neo.config(model_dir=model_dir, neo_runtime=True):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The compiled model is saved as "compiled.pt"
model = torch.jit.load(os.path.join(model_dir, 'compiled.pt'))
model = model.to(device)
neopytorch.config(model_dir=model_dir, neo_runtime=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The compiled model is saved as "compiled.pt"
model = torch.jit.load(os.path.join(model_dir, 'compiled.pt'), map_location=device)

# It is recommended to run warm-up inference during model load
sample_input_path = os.path.join(model_dir, 'sample_input.pkl')
with open(sample_input_path, 'rb') as input_file:
model_input = pickle.load(input_file)
if torch.is_tensor(model_input):
model_input = model_input.to(device)
model(model_input)
elif isinstance(model_input, tuple):
model_input = (inp.to(device)
for inp in model_input if torch.is_tensor(inp))
model(*model_input)
else:
print("Only supports a torch tensor or a tuple of torch tensors")
# It is recommended to run warm-up inference during model load
sample_input_path = os.path.join(model_dir, 'sample_input.pkl')
with open(sample_input_path, 'rb') as input_file:
model_input = pickle.load(input_file)
if torch.is_tensor(model_input):
model_input = model_input.to(device)
model(model_input)
elif isinstance(model_input, tuple):
model_input = (inp.to(device)
for inp in model_input if torch.is_tensor(inp))
model(*model_input)
else:
print("Only supports a torch tensor or a tuple of torch tensors")

return model
return model
12 changes: 6 additions & 6 deletions sagemaker_neo_compilation_jobs/pytorch_vgg19_bn/code/vgg19_bn_uncompiled.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

import numpy as np
import torch
import neopytorch
import torchvision.transforms as transforms
from PIL import Image # Training container doesn't have this package

Expand Down Expand Up @@ -55,10 +56,9 @@ def transform_fn(model, payload, request_content_type,
def model_fn(model_dir):

logger.info('model_fn')
with torch.neo.config(model_dir=model_dir, neo_runtime=True):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The compiled model is saved as "compiled.pt"
model = torch.jit.load(os.path.join(model_dir, 'model.pth'))
model = model.to(device)
neopytorch.config(model_dir=model_dir, neo_runtime=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# The compiled model is saved as "compiled.pt"
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Fix the comment - it says compiled.pt when the name used is model.pth

model = torch.jit.load(os.path.join(model_dir, 'model.pth'), map_location=device)

return model
return model
4 changes: 2 additions & 2 deletions sagemaker_neo_compilation_jobs/pytorch_vgg19_bn/pytorch-vgg19-bn.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
"metadata": {},
"outputs": [],
"source": [
"!~/anaconda3/envs/pytorch_p36/bin/pip install torch==1.4.0 torchvision==0.5.0"
"!~/anaconda3/envs/pytorch_p36/bin/pip install torch==1.6.0 torchvision==0.7.0"
]
},
{
Expand Down Expand Up @@ -127,7 +127,7 @@
"data_shape = '{\"input0\":[1,3,224,224]}'\n",
"target_device = 'ml_c5'\n",
"framework = 'pytorch'\n",
"framework_version = '1.4.0'\n",
"framework_version = '1.6'\n",
"compiled_model_path = 's3://{}/{}/output'.format(bucket, compilation_job_name)\n",
"\n",
"inference_image_uri = image_uris.retrieve(f'neo-{framework}', region, framework_version, instance_type=target_device)"
Expand Down