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Version: 1.43

Supported Versions

This article describes the supported versions and compatibility with supported ML frameworks. For some frameworks, links to example repositories on Gitlab are included. These example repositories can be used to create example deployments on Deeploy.

Deeploy1.29.x - 1.31.x1.32.x - 1.37.x1.38.x - 1.41.x1.42.x +
KServe0.9.00.11.00.12.00.13.1
Python3.7 - 3.93.8 - 3.113.8 - 3.113.9 - 3.11

Model Frameworks​

The versions described in the following sections are the recommended versions on which the model images have been built. If you deviate from the version, check for potential compatibility issues.

PyTorch​

Pytorch default imagepytorch/torchserve-kfs:0.6.0pytorch/torchserve-kfs:0.8.0pytorch/torchserve-kfs:0.9.0
torch1.7.12.0.02.1.0
torchserve0.7.00.8.00.9.0
torch-model-archiver0.7.00.8.00.9.0

Tensorflow​

Tensorflow default imagetensorflow/serving:2.6.2tensorflow/serving:2.6.2
tensorflow2.6.22.6.2

XGBoost​

XGBoost default imagekserve/xgbserver:v0.9.0kserve/xgbserver:v0.11.0kserve/xgbserver:v0.12.0
xgboost1.5.01.7.52.0.2

Scikit-learn​

Scikit-learn default imagekserve/sklearnserver:v0.9.0kserve/sklearnserver:v0.11.0
scikit-learn1.0.11.3.0
joblib1.1.01.3.1

LightGBM​

LightGBM default imagekserve/lgbserver:v0.9.0kserve/lgbserver:v0.11.0
lightgbm3.3.23.3.2

Huggingface​

Triton server default image-kserve/huggingfaceserver:v0.12.0
huggingface-hub->=0.14.0,<1.0.0

NVIDIA Triton​

Triton server default image-nvcr.io/nvidia/tritonserver:23.05-py3
triton-2.34.0
pytorch-2.0.0
onnx-1.15.0
tensorrt-8.6.1.2
tensorflow-2.12.0

Custom model Docker images​

Deeploy versionPrivate Cloud < v1.32.0Private Cloud >= v1.32.0, < v1.38.0Private Cloud >= v1.38.0, < v1.42.0Cloud & Private Cloud >= v1.42.0
deeploy-cli-> 1.35.0>= 1.38.0>= 1.42.0
kserve0.9.00.11.00.12.00.13.1

Standard Explainers​

Tree SHAP​

Deploy a tree SHAP explainer without training it yourself. Available only for tree-based classification models when using the XGBoost, Scikit-learn, or LightGBM model frameworks.

Saliency​

Deploy a saliency based explainer without training it yourself. Available only for text generation and text-to-text generation models when using the Hugging Face model framework.

Attention​

Deploy a attention based explainer without training it yourself. Available only for text generation and text-to-text generation models when using the Hugging Face model framework.

Trained Explainer Frameworks​

The versions described in the following sections are the recommended versions for the specific frameworks to use. If you deviate from the version, check for potential compatibility issues.

SHAP​

Deeploy versionPrivate Cloud < v1.32.0Cloud & Private Cloud >= v1.32.0
shap0.36.00.42.1
dill0.3.30.3.7

Anchors​

Deeploy versionPrivate Cloud < v1.32.0Cloud & Private Cloud >= v1.32.0
alibi0.6.40.9.4
dill0.3.30.3.7

MACE​

Deeploy versionPrivate Cloud < v1.32.0Cloud & Private Cloud >= v1.32.0
omnixai1.1.41.3.1
dill0.3.30.3.7

PDP​

  • Expected pre-trained explainer artefact: dill (explainer.dill)
Deeploy versionPrivate Cloud < v1.32.0Cloud & Private Cloud >= v1.32.0
omnixai1.1.41.3.1
dill0.3.30.3.7

Custom explainer Docker images​

Deeploy versionPrivate Cloud < v1.32.0Private Cloud >= v1.32.0, < v1.38.0Private Cloud >= v1.38.0, <1.42.0Cloud & Private Cloud >= v1.42.0
deeploy-cli-> 1.35.0>= 1.38.0>= 1.42.0
kserve0.9.00.11.00.12.00.13.1

Transformer Frameworks​

Custom transformer Docker images​

Deeploy versionPrivate Cloud < v1.32.0Private Cloud >= v1.32.0, < v1.38.0Private Cloud >= v1.38.0, <1.42.0Cloud & Private Cloud >= v1.42.0
deeploy-cli-> 1.35.0>= 1.38.0>= 1.42.0
kserve0.9.00.11.00.12.00.13.1