Abstract
These support matrices provide a look into the supported platforms, features, and hardware capabilities of the TensorRT 5.1.2 Release Candidate (RC) APIs, parsers, and layers.
For previously released TensorRT documentation, see TensorRT Archives.
1. Features For Platforms And Software
Linux x86-64 | Windows x64 | Linux AArch64 | QNX AArch64 | Android AArch64 | |
---|---|---|---|---|---|
Supported CUDA versions | 9.0, 10.0, 10.1 | 9.0, 10.0, 10.1 | 10.1 | 10.1 | 10.0 |
Supported cuDNN versions | 7.5.0 | 7.5.0 | 7.5.0 | 7.5.0 | 7.5.0 |
TensorRT Python API | Yes | No | Yes | No | No |
NvUffParser | Yes | Yes | Yes | Yes | Yes |
NvOnnxParser | Yes | Yes | Yes | Yes | Yes |
2. Layers And Features
Layer | Dimensions of input tensor | Dimensions of output tensor | Does the operation apply to only the innermost 3 dimensions? | Supports broadcast1 | Supports broadcast across batch2 |
---|---|---|---|---|---|
Activation | 0-7 dimensions | 0-7 dimensions | No | No | No |
Concatenation | 1-7 dimensions | 1-7 dimensions | No | No | No |
Constant | 0-7 dimensions | 0-7 dimensions | No | No | Always |
Convolution | 3 or more dimensions | 3 or more dimensions | Yes | No | No |
Deconvolution | 3 or more dimensions | 3 or more dimensions | Yes | No | No |
ElementWise | 0-7 dimensions | 0-7 dimensions | No | Yes | Yes |
FullyConnected | 3 or more dimensions | 3 or more dimensions | Yes | No | No |
Gather |
|
0-7 dimensions | No | No | Yes |
Identity | 0-7 dimensions | 0-7 dimensions | No | No | No |
IPluginV2 | User defined | User defined | User defined | User defined | User defined |
LRN | 3 or more dimensions | 3 or more dimensions | Yes | No | No |
MatrixMultiply | 2 or more dimensions | 2 or more dimensions | No | Yes | Yes |
Padding | 3 or more dimensions | 3 or more dimensions | Yes | No | No |
Plugin | User defined | User defined | User defined | User defined | User defined |
Pooling | 3 or more dimensions | 3 or more dimensions | Yes | Yes | Yes |
RaggedSoftMax |
|
2 or more dimensions | No | No | Yes |
Reduce | 1-7 dimensions | 0-7 dimensions | No | No | No |
RNN | 3 dimensions | 3 dimensions | No | No | No |
RNNv2 |
|
Data/Hidden/Cell: 2 or more dimensions | No | No | No |
Scale | 3 or more dimensions | 3 or more dimensions | Yes | No | No |
Shuffle | 0-7 dimensions | 0-7 dimensions | No | No | No |
Slice | 1-7 dimensions | 1-7 dimensions | No | No | Yes |
SoftMax | 1-7 dimensions | 1-7 dimensions | No | No | Yes |
TopK | 1-7 dimensions |
|
Yes | No | Yes |
Unary | 0-7 dimensions | 0-7 dimensions | No | No | No |
3. Layers And Precision
For more information about each of the TensorRT layers, see TensorRT Layers. To view a list of the specific attributes that are supported by each layer, refer to the TensorRT API documentation.
Layer | FP32 | FP16 | INT8 | DLA3 |
---|---|---|---|---|
Activation | Yes | Yes | Yes | Yes |
Concatenation | Yes | Yes | Yes | Yes |
Constant | Yes | Yes | Yes | No |
Convolution | Yes | Yes | Yes | Yes |
Deconvolution | Yes | Yes | Yes | Yes |
ElementWise | Yes | Yes | No | Yes |
FullyConnected | Yes | Yes | Yes | Yes |
Gather | Yes | Yes | No | No |
Identity | Yes | Yes | Yes | No |
IPluginV2 | Yes | Yes | No | No |
LRN | Yes | Yes | Yes | Yes |
MatrixMultiply | Yes | Yes | No | No |
Padding | Yes | Yes | Yes | No |
Plugin | Yes | Yes | No | No |
Pooling | Yes | Yes | Yes | Yes |
RaggedSoftMax | Yes | No | No | No |
Reduce | Yes | Yes | No | No |
RNN | Yes | Yes | No | No |
RNNv2 | Yes | Yes | No | No |
Scale | Yes | Yes | Yes | Yes |
Shuffle | Yes | Yes | Yes | No |
Slice | Yes | Yes | No4 | No |
SoftMax | Yes | Yes | No | No |
TopK | Yes | Yes | No | No |
Unary | Yes | Yes | No | No |
4. Hardware And Precision
CUDA Compute Capability | Example Device | FP32 | FP16 | INT8 | FP16 Tensor Cores | INT8 Tensor Cores | DLA |
---|---|---|---|---|---|---|---|
7.5 | Tesla T4 | Yes | Yes | Yes | Yes | Yes | No |
7.2 | Jetson AGX Xavier | Yes | Yes | Yes | Yes | Yes | Yes |
7.0 | Tesla V100 | Yes | Yes | Yes | Yes | No | No |
6.2 | Jetson TX2 | Yes | Yes | No | No | No | No |
6.1 | Tesla P4 | Yes | No | Yes | No | No | No |
6.0 | Tesla P100 | Yes | Yes | No | No | No | No |
5.3 | Jetson TX1 | Yes | Yes | No | No | No | No |
5.2 | Tesla M4 | Yes | No | No | No | No | No |
5.0 | Quadro K2200 | Yes | No | No | No | No | No |
3.7 | Tesla K80 | Yes | No | No | No | No | No |
3.5 | Tesla K40 | Yes | No | No | No | No | No |
3.0 | Tesla K10 | Yes | No | No | No | No | No |
5. Software Versions Per Platform
Compiler version | Python version | |
---|---|---|
Ubuntu 14.04 | gcc 4.8.4 | 2.7, 3.4 |
Ubuntu 16.04 | gcc 5.4.0 | 2.7, 3.5 |
Ubuntu 18.04 | gcc 7.3.0 | 2.7, 3.6 |
CentOS 7.5 | gcc 4.8.5 | 2.7, 3.6 |
Windows 10 |
|
|
Linux AArch64 | gcc 5.3.1 | 2.7, 3.6 |
QNX AArch64 | gcc 5.4.0 | |
Android AArch64 | Clang 5.0.300080 |
6. Supported Ops
Caffe
These are the operations that are supported in a Caffe framework:- BatchNormalization
- BNLL
- Clip5
- Concatenation
- Convolution
- Crop
- Deconvolution
- Dropout
- ElementWise
- ELU
- InnerProduct
- LeakyReLU
- LRN
- Permute
- Pooling
- Power
- Reduction
- ReLU, TanH, and Sigmoid
- Reshape
- SoftMax
- Scale
TensorFlow
These are the operations that are supported in a TensorFlow framework:- Add, Sub, Mul, Div, Minimum and Maximum
- ArgMax
- ArgMin
- AvgPool
- BiasAdd
- Clip
- ConcatV2
- Const
- Conv2D
- ConvTranspose2D
- DepthwiseConv2dNative
- Elu
- ExpandDims
- FusedBatchNorm
- Identity
- LeakyReLU
- MaxPool
- Mean
- Negative, Abs, Sqrt, Recip, Rsqrt, Pow, Exp and Log
- Pad is supported if followed by one of these TensorFlow layers: Conv2D, DepthwiseConv2dNative, MaxPool, and AvgPool.
- Placeholder
- ReLU, TanH, and Sigmoid
- Relu6
- Reshape
- Sin, Cos, Tan, Asin, Acos, Atan, Sinh, Cosh, Asinh, Acosh, Atanh, Ceil and Floor
- Selu
- Slice
- SoftMaxNote: If the input to a TensorFlow SoftMax op is not NHWC, TensorFlow will automatically insert a transpose layer with a non-constant permutation, causing the UFF converter to fail. It is therefore advisable to manually transpose SoftMax inputs to NHWC using a constant permutation.
- Softplus
- Softsign
- Transpose
ONNX
Since the ONNX parser is an open source project, the most up-to-date information regarding the supported operations can be found in GitHub: ONNX TensorRT.- Abs
- Add
- ArgMax
- ArgMin
- AveragePool
- BatchNormalization
- Cast
- Ceil
- Clip
- Concat
- Constant
- Conv
- ConvTranspose
- DepthToSpace
- Div
- Dropout
- Elu
- Exp
- Flatten
- Floor
- Gather
- Gemm
- GlobalAveragePool
- GlobalMaxPool
- HardSigmoid
- Identity
- ImageScaler
- InstanceNormalization
- LRN
- LeakyRelU
- Log
- LogSoftmax
- MatMul
- Max
- MaxPool
- Mean
- Min
- Mul
- Neg
- Pad
- ParametricSoftplus
- Pow
- Reciprocal
- ReduceL1
- ReduceL2
- ReduceLogSum
- ReduceLogSumExp
- ReduceMax
- ReduceMean
- ReduceMin
- ReduceProd
- ReduceSum
- ReduceSumSquare
- Relu
- Reshape
- ScaledTanh
- Selu
- Shape
- Sigmoid
- Sin, Cos, Tan, Asin, Acos, Atan, Sinh, Cosh, Asinh, Acosh, and Atanh
- Size
- Slice
- Softmax
- Softplus
- Softsign
- SpaceToDepth
- Split
- Squeeze
- Sub
- Sum
- Tanh
- ThresholdedRelu
- TopK
- Transpose
- Unsqueeze
- Upsample
Notices
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