Triton Inference Server Release 24.06

The Triton Inference Server container image, release 24.06, is available on NGC and is open source on GitHub.

Contents of the Triton Inference Server container

The Triton Inference Server Docker image contains the inference server executable and related shared libraries in /opt/tritonserver.

For a complete list of what the container includes, refer to Deep Learning Frameworks Support Matrix.

The container also includes the following:

Driver Requirements

Release 24.06 is based on NVIDIA CUDA 12.5.0.23, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

GPU Requirements

Release 24.06 supports CUDA compute capability 6.0 and later. This corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, NVIDIA Ampere architecture, NVIDIA Hopper™, and NVIDIA Ada Lovelace architecture families. For a list of GPUs to which this compute capability corresponds, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

This Inference Server release includes the following key features and enhancements.

  • The TensorRT Backend now supports the BF16 datatype.
  • A new tutorial on auto-scaling and load balancing TensorRT-LLM model deployments with Triton Inference Server has been released and is located here: https://github.com/triton-inference-server/tutorials/tree/main/Deployment/Kubernetes/TensorRT-LLM_Autoscaling_and_Load_Balancing.
  • A compare subcommand has been added to GenAi-Perf to allow comparison across multiple runs.
  • Multi-LoRA and multi-model support in GenAI-Perf.
  • Custom visualizations in GenAI-Perf.
  • A fixed request count can now be requested from Perf Analyzer.
  • Ensemble top-level response caching support in Perf Analyzer.
  • Added –enable-peer-access to control trying to enable GPU peer access on triton startup. Default is TRUE.
  • Python models in default mode may send its response using the InferenceResponseSender similarly to models in decoupled mode.
  • Addressed an issue where Triton would cease processing gRPC requests after receiving multiple cancellation requests.

NVIDIA Triton Inference Server Container Versions

The following table shows what versions of Ubuntu, CUDA, Triton Inference Server, and NVIDIA TensorRT™ are supported in each of the NVIDIA containers for Triton Inference Server. For older container versions, refer to the Frameworks Support Matrix.

Container Version Triton Inference Server Ubuntu CUDA Toolkit TensorRT
24.06 2.47.0 22.04 NVIDIA CUDA 12.5.0.23 TensorRT 10.1.0.27
24.05 2.46.0 NVIDIA CUDA 12.4.1 TensorRT 10.0.1.6
24.04 2.45.0 TensorRT 8.6.3
24.03 2.44 NVIDIA CUDA 12.4.0.41
24.02 2.43 NVIDIA CUDA 12.3.2
24.01 2.42 TensorRT 8.6.1.6
23.12 2.41
23.11 2.40 NVIDIA CUDA 12.3.0
23.10 2.39.0 NVIDIA CUDA 12.2.2
23.09 2.38.0 NVIDIA CUDA 12.2.1
23.08 2.37.0
23.07 2.36.0 NVIDIA CUDA 12.1.1
23.06 2.35.0
23.05 2.34.0 TensorRT 8.6.1.2
23.04 2.33.0 20.04 NVIDIA CUDA 12.1.0 TensorRT 8.6.1
23.03 2.32.0 TensorRT 8.5.3
23.02 2.31.0 NVIDIA CUDA 12.0.1
23.01 2.30.0 TensorRT 8.5.2.2
22.12 2.29.0 NVIDIA CUDA 11.8.0 TensorRT 8.5.1
22.11 2.28.0
22.10 2.27.0 TensorRT 8.5 EA
22.09 2.26.0
22.08 2.25.0 NVIDIA CUDA 11.7.1 TensorRT 8.4.2.4
22.07 2.24.0 NVIDIA CUDA 11.7 Update 1 Preview TensorRT 8.4.1
22.06 2.23.0 TensorRT 8.2.5
22.05 2.22.0 NVIDIA CUDA 11.7.0
22.04 2.21.0 NVIDIA CUDA 11.6.2 TensorRT 8.2.4.2 and

for x86 Linux and SBSA

TensorRT 8.4.0 for JetPack/Jetson

22.03 2.20.0 NVIDIA CUDA 11.6.1 TensorRT 8.2.3 and

for x86 Linux and SBSA

TensorRT 8.4.0 for JetPack/Jetson

22.02 2.19.0 NVIDIA CUDA 11.6.0 TensorRT 8.2.3
22.01 2.18.0 TensorRT 8.2.2
21.12 2.17.0 NVIDIA CUDA 11.5.0 TensorRT 8.2.1.8
21.11 2.16.0

TensorRT 8.2.1.8 for x64 Linux

TensorRT 8.0.2.2 for ARM SBSA Linux
21.10 2.15.0 NVIDIA CUDA 11.4.2 with cuBLAS 11.6.5.2
21.09 2.14.0 NVIDIA CUDA 11.4.2 TensorRT 8.0.3
21.08 2.13.0 NVIDIA CUDA 11.4.1 TensorRT 8.0.1.6
21.07 2.12.0 NVIDIA CUDA 11.4.0
21.06.1 2.11.0 NVIDIA CUDA 11.3.1 TensorRT 7.2.3.4
21.06
21.05 2.10.0 NVIDIA CUDA 11.3.0
21.04 2.9.0
21.03 2.8.0 NVIDIA CUDA 11.2.1 TensorRT 7.2.2.3
21.02 2.7.0 NVIDIA CUDA 11.2.0 TensorRT 7.2.2.3+cuda11.1.0.024
20.12 2.6.0 NVIDIA CUDA 11.1.1 TensorRT 7.2.2
20.11 2.5.0

18.04

 NVIDIA CUDA 11.1.0 TensorRT 7.2.1
20.10 2.4.0
20.09 2.3.0 NVIDIA CUDA 11.0.3 TensorRT 7.1.3
20.08

2.2.0
20.07 1.15.0

2.1.0

 NVIDIA CUDA 11.0.194
20.06 1.14.0

2.0.0

 NVIDIA CUDA 11.0.167 TensorRT 7.1.2
20.03.1 1.13.0 NVIDIA CUDA 10.2.89 TensorRT 7.0.0
20.03 1.12.0

20.02

20.01

 1.11.0
1.10.0

19.12

19.11

 1.9.0 TensorRT 6.0.1
1.8.0
19.10 1.7.0 NVIDIA CUDA 10.1.243
19.09 1.6.0
19.08 1.5.0 TensorRT 5.1.5

Known Issues

  • When using Python models indecoupled mode, users need to ensure that the ResponseSender goes out of scope or is properly cleaned up before unloading the model to guarantee that the unloading process executes correctly.
  • Restart support was temporarily removed for Python models.
  • TensorRT v10 does not support implicit batching. As a result, Triton no longer supports TensorRT models with implicit batch dimensions.
  • Since TensorRT v10 no longer supports implicit batch, Tritonserver will not be able to load existing TF-TRT models that use implicit batch. Therefore, we need to build TF-TRT models withdynamic batch support.
  • Multiple model configuration files are not supported by loading models with file override. Users still need to provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
  • Perf Analyzer no longer supports the --trace-file option.
  • TensorRT-LLM backend provides limited support of Triton extensions and features.
  • The TensorRT-LLM backend may core dump on server shutdown. This impacts server teardown only and will not impact inferencing.
  • The Java CAPI is known to have intermittent segfaults.
  • Some systems which implement malloc() may not release memory back to the operating system right away causing a false memory leak. This can be mitigated by using a different malloc implementation. Tcmalloc and jemalloc are installed in the Triton container and can be used by specifying the library in LD_PRELOAD. NVIDIA recommends experimenting with both tcmalloc and jemalloc to determine which one works better for your use case.
  • Auto-complete may cause an increase in server start time. To avoid a start time increase, users can provide the full model configuration and launch the server with --disable-auto-complete-config.
  • Auto-complete does not support PyTorch models due to lack of metadata in the model. It can only verify that the number of inputs and the input names matches what is specified in the model configuration. There is no model metadata about the number of outputs and datatypes. Related PyTorch bug:https://github.com/pytorch/pytorch/issues/38273.
  • Triton Client PIP wheels for ARM SBSA are not available from PyPI and pip will install an incorrect Jetson version of Triton Client library for Arm SBSA. The correct client wheel file can be pulled directly from the Arm SBSA SDK image and manually installed.
  • Traced models in PyTorch seem to create overflows when int8 tensor values are transformed to int32 on the GPU. Refer to pytorch/pytorch#66930 for more information.
  • Triton cannot retrieve GPU metrics with MIG-enabled GPU devices.
  • Triton metrics might not work if the host machine is running a separate DCGM agent on bare-metal or in a container.
  • When cloud storage (AWS, GCS, AZURE) is used as a model repository and a model has multiple versions, Triton creates an extra local copy of the cloud model’s folder in the temporary directory, which is deleted upon server’s shutdown.
  • Python backend support for Windows is limited and does not currently support the following features:
    • GPU tensors
    • CPU and GPU-related metrics
    • Custom execution environments
    • The model load/unload APIs
  • Starting in 24.06, if you use Triton's iGPU container you might encounter this error message when loading TensorRT models built with the 24.06 TensorRT iGPU container: 
    "Serialization (Serialization assertion stdVersionRead == serializationVersion failed.Version tag does not match. Note: Current Version: 236, Serialized Engine Version: 237)."
    If this happens you can rebuild your iGPU models with the 24.04 TensorRT iGPU container and then run them in the Triton 24.06 iGPU container.