Bone Age Assessment (BAA) is a task performed by physicians to estimate the skeletal development of a pediatric patient. Tipically physicians perform this exam by doing a manual analysis of the X-ray image of the non-dominant hand of a child, either by taking the image as a whole or paying attention to certain anatomical Regions Of Interest (ROIs). Over the years, several datasets have been proposed in order to generate automated methods to perform this task. Most notably, in 2017 the Radiological Society of North America (RSNA)1 created the Pediatric Bone Age Challenge, which encouraged the development of machine learning approaches for this task. In this paper, we present GPNet a convolutional neural network capable of performing BAA precisely and effectively by analyzing the whole hand in a single forward pass. We train GPNet using the training data available from the dataset created in the RSNA challenge and evaluate our method using the validation set. We use the testing set to compare our performance with the current state-of-the-art and find that GPNet significantly outperforms previous methods. During our architecture search we perform several experiments to demonstrate the effect of different layers, proving that some blocks do not contribute to the performance of the network, but instead they affect it. As a result, we are able to develop a method that reduces the number of trainable parameters by nearly 82.15 M in comparison to the state-of-the-art, while improving the performance.