The correct answer is Managed GPU cluster with medium memory and 20 nodes.

This option is best because GPU acceleration is well suited to training convolutional neural networks used in image recognition and a managed cluster provides scaling and operational simplicity. Using a medium memory configuration across multiple nodes lets the team train on reasonable batch sizes and distribute work to reduce wall clock time without taking on the extra complexity of custom distributed infra.

TPU cluster with high memory and 20 nodes is not the best fit because TPUs are typically optimized for very large scale workloads and specific model types and they can add cost and operational constraints that are unnecessary for a small robotics firm. TPUs may also require additional engineering to adapt some training pipelines.

Single compute instance with low memory and 3 CPU cores is insufficient because CPUs with low memory will be too slow and may run out of memory for typical image datasets and modern deep learning models. Training on that instance would be impractically long.

Single compute instance with high memory and 3 CPU cores is also not suitable because high memory does not replace the parallel compute capabilities of GPUs. CPU only training will be much slower and will limit iteration speed during model development.

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