In the machine learning domain, machine learning frameworks are predominantly written and maintained in NVIDIA® CUDA™ language. There have been attempts to port these frameworks to OpenCL®, notably the ports of Caffe framework by Gu et al; Tschopp; and Engel; and of Torch framework by Perkins. The authors of these frameworks found merging their work into the mainstream framework challenging, and maintain their forks as separate branches or repositories. CUDA-on-CL addresses this problem by leaving the reference implementation entirely in NVIDIA CUDA, both host-side and device-side, and providing a compiler and a runtime component, so that any CUDA C++11 application can in theory be compiled and run on any OpenCL 1.2 device. We use Tensorflow framework as a case-study, and demonstrate the ability to run unary, binary and reduction Tensorflow and Eigen kernels, with no modification to the original CUDA source-code. Performance studies are undertaken, using the Tensorflow kernels. For buffer sizes of 1MB or more, performance is comparable between CUDA and CUDA-on-CL, across unary operations, binary operations and single-axis reductions. Full reduction is around 14 times slower on CUDA-on-CL than on CUDA. We think this may be because of the absence of the low-level hardware shfl operation. The asymptotic time for zero buffer sizes is double that of CUDA, possibly because of the overhead of additional kernel boilerplate needed to workaround limitations in the OpenCL 1.2 standard.