# Neuron Check Model¶

## Overview¶

Neuron Check Model tool provides user with basic information about the compiled and uncompiled model’s operations without the use of TensorBoard-Neuron. For additional visibility into the models, please see Getting Started: TensorBoard-Neuron (Deprecated).

Neuron Check Model tool scans the user’s uncompiled model and provides a table of the operations within the uncompiled model. By default, the table shows each operation type and number of instances of that type within model, and whether the type is supported in Neuron. If –show_names option is specified, the table shows each operation by name and whether the type of that operation is supported in Neuron.

If the model is already compiled, the tool also provides the table of operations as for uncompiled model. The table include the Neuron subgraph type and number of instances of that type, along with operations that have not been compiled to Neuron. Additionally, the tool displays a message showing the minimum number of NeuronCores required to run the model, followed by another table which shows the list of Neuron subgraphs by name and the number of pipelined NeuronCores used by each subgraph. More information about NeuronCore pipeline can be found in NeuronCore Pipeline. If –expand_subgraph option is specified, the operations within each subgraph are printed below the subgraph information.

Neuron Check Model tool is currently available for TensorFlow and MXNet. To check PT model, please use torch.neuron.analyze_model function as shown in PyTorch-Neuron Getting Started tutorial PyTorch Resnet-50 Tutorial

## TensorFlow-Neuron Check Model¶

The following example shows how to run TensorFlow-Neuron Check Model tool with TensorFlow ResNet50 tutorial.

1. Start with the TensorFlow ResNet50 tutorial at Tensorflow-Resnet50 Tutorial and do the first three steps of the tutorial. Please stay in the Python environment that you setup during the tutorial.

Install needed tensorflow_hub package and download the tool:

```
pip install tensorflow_hub
wget https://raw.githubusercontent.com/aws/aws-neuron-sdk/master/src/neuron-gatherinfo/tf_neuron_check_model.py
python tf_neuron_check_model.py -h
```

```
usage: tf_neuron_check_model.py [-h] [--show_names] [--expand_subgraph]
model_path
positional arguments:
model_path a TensorFlow SavedModel directory (currently supporting
TensorFlow v1 SaveModel only).
optional arguments:
-h, --help show this help message and exit
--show_names list operation by name instead of summarizing by type
(caution: this option will generate many lines of output
for a large model).
--expand_subgraph show subgraph operations.
```

After step 3 of the TensorFlow ResNet50 tutorial, you can check the uncompiled model to see Neuron supported operations (currently supporting TensorFlow v1 SaveModel only):

```
$ python tf_neuron_check_model.py ws_resnet50/resnet50/
* The following table shows the supported and unsupported operations within this uncompiled model.
* Each line shows an operation type, the number of instances of that type within model,
* and whether the type is supported in Neuron.
* Some operation types are excluded from table because they are no-operations or training-related operations:
['Placeholder', 'PlaceholderWithDefault', 'NoOp', 'Const', 'Identity', 'IdentityN', 'VarHandleOp',
'VarIsInitializedOp', 'AssignVariableOp', 'ReadVariableOp', 'StringJoin', 'ShardedFilename', 'SaveV2',
'MergeV2Checkpoints', 'RestoreV2']
Op Type Num Instances Neuron Supported ?
------- ------------- ------------------
Pad 2 Yes
RandomUniform 54 Yes
Sub 54 Yes
Mul 54 Yes
Add 54 Yes
Conv2D 53 Yes
BiasAdd 54 Yes
FusedBatchNormV3 53 Yes
Relu 49 Yes
MaxPool 1 Yes
AddV2 16 Yes
Fill 56 Yes
Mean 1 Yes
MatMul 1 Yes
Softmax 1 Yes
Pack 1 Yes
* Total inference operations: 504
* Total Neuron supported inference operations: 504
* Percent of total inference operations supported by Neuron: 100.0
```

You can also check the compiled model to see the number of pipeline NeuronCores for each subgraph:

```
$ python tf_neuron_check_model.py ws_resnet50/resnet50_neuron/
* Found 1 Neuron subgraph(s) (NeuronOp(s)) in this compiled model.
* Use this tool on the original uncompiled model to see Neuron supported operations.
* The following table shows all operations, including Neuron subgraphs.
* Each line shows an operation type, the number of instances of that type within model,
* and whether the type is supported in Neuron.
* Some operation types are excluded from table because they are no-operations or training-related operations:
['Placeholder', 'PlaceholderWithDefault', 'NoOp', 'Const', 'Identity', 'IdentityN', 'VarHandleOp',
'VarIsInitializedOp', 'AssignVariableOp', 'ReadVariableOp', 'StringJoin', 'ShardedFilename', 'SaveV2',
'MergeV2Checkpoints', 'RestoreV2']
Op Type Num Instances Neuron Supported ?
------- ------------- ------------------
NeuronOp 1 Yes
* Please run this model on Inf1 instance with at least 1 NeuronCore(s).
* The following list show each Neuron subgraph with number of pipelined NeuronCores used by subgraph
* (and subgraph operations if --expand_subgraph is used):
Subgraph Name Num Pipelined NeuronCores
------------- -------------------------
conv5_block3_3_bn/FusedBatchNormV3/ReadVariableOp/neuron_op_d6f098c01c780733 1
```

When showing subgraph information, you can use –expand_subgraph to show operation types in each subgraph:

```
$ python tf_neuron_check_model.py ws_resnet50/resnet50_neuron/ --expand_subgraph
(output truncated to show subgraph information only)
Subgraph Name Num Pipelined NeuronCores
------------- -------------------------
conv5_block3_3_bn/FusedBatchNormV3/ReadVariableOp/neuron_op_d6f098c01c780733 1
Op Type Num Instances
------- -------------
MatMul 1
Relu 49
Add 16
FusedBatchNorm 53
BiasAdd 54
Conv2D 53
Pad 2
Mean 1
MaxPool 1
Softmax 1
```

Use –show_names to see full operation names (caution: this option will generate many lines of output for a large model):

```
$ python tf_neuron_check_model.py ws_resnet50/resnet50_neuron/ --show_names
* Found 1 Neuron subgraph(s) (NeuronOp(s)) in this compiled model.
* Use this tool on the original uncompiled model to see Neuron supported operations.
* The following table shows all operations, including Neuron subgraphs.
* Each line shows an operation name and whether the type of that operation is supported in Neuron.
* Some operation types are excluded from table because they are no-operations or training-related operations:
['Placeholder', 'PlaceholderWithDefault', 'NoOp', 'Const', 'Identity', 'IdentityN', 'VarHandleOp',
'VarIsInitializedOp', 'AssignVariableOp', 'ReadVariableOp', 'StringJoin', 'ShardedFilename', 'SaveV2',
'MergeV2Checkpoints', 'RestoreV2']
Op Name Op Type Neuron Supported ?
------- ------- ------------------
conv5_block3_3_bn/FusedBatchNormV3/ReadVariableOp/neuron_op_d6f098c01c780733 NeuronOp Yes
* Please run this model on Inf1 instance with at least 1 NeuronCore(s).
* The following list show each Neuron subgraph with number of pipelined NeuronCores used by subgraph
* (and subgraph operations if --expand_subgraph is used):
Subgraph Name Num Pipelined NeuronCores
------------- -------------------------
conv5_block3_3_bn/FusedBatchNormV3/ReadVariableOp/neuron_op_d6f098c01c780733 1
```

## MXNet-Neuron Check Model¶

The following example shows how to run MXNet-Neuron Check Model tool with MXNet ResNet50 tutorial.

1. Start with the MXNet ResNet50 tutorial at mxnet-resnet50 and do the first three steps of the tutorial. Please stay in the Python environment that you setup during the tutorial.

Download the tool:

```
wget https://raw.githubusercontent.com/aws/aws-neuron-sdk/master/src/neuron-gatherinfo/mx_neuron_check_model.py
python mx_neuron_check_model.py -h
```

```
usage: mx_neuron_check_model.py [-h] [--show_names] [--expand_subgraph]
model_path
positional arguments:
model_path path prefix to MXNet model (the part before -symbol.json)
optional arguments:
-h, --help show this help message and exit
--show_names list operation by name instead of summarizing by type
(caution: this option will generate many lines of output
for a large model).
--expand_subgraph show subgraph operations.
```

After step 3 of MXNet ResNet50 tutorial, you can check the uncompiled model to see Neuron supported operations:

```
$ python mx_neuron_check_model.py resnet-50
* The following table shows the supported and unsupported operations within this uncompiled model.
* Each line shows an operation type, the number of instances of that type within model,
* and whether the type is supported in Neuron.
* Some operation types are excluded from table because they are no-operations or training-related operations:
['null']
Op Type Num Instances Neuron Supported ?
------- ------------- ------------------
BatchNorm 51 Yes
Convolution 53 Yes
Activation 50 Yes
Pooling 2 Yes
elemwise_add 16 Yes
Flatten 1 Yes
FullyConnected 1 Yes
SoftmaxOutput 1 No
* Total inference operations: 175
* Total Neuron supported inference operations: 174
* Percent of total inference operations supported by Neuron: 99.4
```

You can also check the compiled model to see the number of pipeline NeuronCores for each subgraph:

```
$ python mx_neuron_check_model.py resnet-50_compiled
* Found 1 Neuron subgraph(s) (_neuron_subgraph_op(s)) in this compiled model.
* Use this tool on the original uncompiled model to see Neuron supported operations.
* The following table shows all operations, including Neuron subgraphs.
* Each line shows an operation type, the number of instances of that type within model,
* and whether the type is supported in Neuron.
* Some operation types are excluded from table because they are no-operations or training-related operations:
['null']
Op Type Num Instances Neuron Supported ?
------- ------------- ------------------
_neuron_subgraph_op 1 Yes
SoftmaxOutput 1 No
* Please run this model on Inf1 instance with at least 1 NeuronCore(s).
* The following list show each Neuron subgraph with number of pipelined NeuronCores used by subgraph
* (and subgraph operations if --expand_subgraph is used):
Subgraph Name Num Pipelined NeuronCores
------------- -------------------------
_neuron_subgraph_op0 1
```

When showing subgraph information, you can use –expand_subgraph to show operation types in each subgraph:

```
$ python mx_neuron_check_model.py resnet-50_compiled --expand_subgraph
(output truncated to show subgraph information only)
Subgraph Name Num Pipelined NeuronCores
------------- -------------------------
_neuron_subgraph_op0 1
Op Type Num Instances
------- -------------
BatchNorm 51
Convolution 53
Activation 50
Pooling 2
elemwise_add 16
Flatten 1
FullyConnected 1
```

Use –show_names to see full operation names (caution: this option will generate many lines of output for a large model):

```
$ python mx_neuron_check_model.py resnet-50_compiled --show_names
* Found 1 Neuron subgraph(s) (_neuron_subgraph_op(s)) in this compiled model.
* Use this tool on the original uncompiled model to see Neuron supported operations.
* The following table shows all operations, including Neuron subgraphs.
* Each line shows an operation name and whether the type of that operation is supported in Neuron.
* Some operation types are excluded from table because they are no-operations or training-related operations:
['null']
Op Name Op Type Neuron Supported ?
------- ------- ------------------
_neuron_subgraph_op0 _neuron_subgraph_op Yes
softmax SoftmaxOutput No
* Please run this model on Inf1 instance with at least 1 NeuronCore(s).
* The following list show each Neuron subgraph with number of pipelined NeuronCores used by subgraph
* (and subgraph operations if --expand_subgraph is used):
Subgraph Name Num Pipelined NeuronCores
------------- -------------------------
_neuron_subgraph_op0 1
```