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Last Epoch __FULL__


As always we'd love to hear your feedback on our newly launched Last Epoch branch. Let us know your thoughts in our Discord, and select the Last Epoch role in #roles to stay up to date with our content development. Last Epoch is heating up and we can't wait to blast the multiplayer patch with you all. See you in game!




last epoch



Hi,I am using ray tune with the PyTorch Lightning callback. I followed the instructions in the tutorial but encountered a problem. it seems that although checkpoints are being saved the best model is chosen based on the last epoch. that is the analysis.best_checkpoint is the checkpoint of the model which had the best score at last epoch so I guess the ray algorithm just considers the last epoch of each model in order to find the best model which is not appropriate for my model. am I doing anything wrong?


I am using ray tune with the PyTorch Lightning callback. I followed the instructions in the tutorial but encountered a problem. it seems that although checkpoints are being saved the best model is chosen based on the last epoch. that is the analysis.best_checkpoint is the checkpoint of the model which had the best score at last epoch so I guess the ray algorithm just considers the last epoch of each model in order to find the best model which is not appropriate for my model. am I doing anything wrong?


the numbers in the table show metrics at last epoch (the number of iterations are different because of early stopping), as you can see at last line the analysis.best_checkpoint is for 00002. however when I look at the logs I see that 00000 at epoch 20 had the highest val_ci compared to others so I expect analysis.best_checkpoint be for 00000.also I can see that for each trial two checkpoints are saved. one for the best best epoch one the last epoch (although i set checkpoint_at_end to False). and if I set chekpoint_at_end to True the following error occurs:ValueError: 'checkpoint_at_end' cannot be used with a checkpointable function. You can specify and register checkpoints within your trainable function.Here is my trainable function:


So as you said Ray Tune will use the performance of my model to explore the space. but which performance. at checkpoint or last epoch? what should I do in order to make it be the checkpoint?You mean that I should make another metric inside LightningModule which holds the performance of the model at last checkpoint?


@rliaw I have the same question. It seems like ray.tune is using the last epoch during training to measure the performance of a trial. Instead, is there a way to tell it to use the best epoch of each trial?I understand I can use analysis.get_best_checkpoint(metric, mode="max") to obtain that after tuning is done, but what about the logs or result tables that are printed during tuning - is it possible to do anything there?


Obviously the model didn't improve after epoch 2, but patience=2 led the algorithm terminate after 4 epochs. When I run model.evaluate now, does it take the model trained after 4 epochs or does it take the model trained after 2 epochs?Is there any need to save and load the best model with ModelCheckpoint then in order to evaluate?


Assuming the goal of a training is to minimize the loss. With this,the metric to be monitored would be 'loss', and mode would be 'min'. Amodel.fit() training loop will check at end of every epoch whether theloss is no longer decreasing, considering the min_delta and patienceif applicable. Once it's found no longer decreasing,model.stop_training is marked True and the training terminates.


To make an example, you can use restore_best_weights even without early_stopping. The training would end only at the end of your set number of epochs, but the model returned at the end would have been the one with the best performances. This could be useful in the situation of bouncing loss due to a wrong learning rate and\or optimizer


According to Steam DB (opens in new tab), the action-RPG has over 26,000 people playing right now, and yesterday this reached an all-time high of 40,591 concurrent players. To put this into perspective, prior to the update's release last Thursday, March 9, the game's player count regularly didn't even reach 1,000. Given that Last Epoch centres around exploring dungeons and hunting for loot, being able to jump into the game with others is a mighty enticing prospect, but we're guessing that even Eleventh Hour Games weren't anticipating a player surge on such a massive scale.


Those who have already tried Last Epoch have been impressed with what they've played. Currently, its recent reviews on Steam are "mostly positive" and it's got a "very positive" rating overall. "Like the lovechild of Diablo and Path of Exile that has the best features of both, Last Epoch is everything I'm looking for in an ARPG," says one reviewer on Steam. Another writes, "As someone who played thousands of hours of ARPGs and tried pretty much each one that's released for the last 20 years, I think this is already one of the best ARPGs on the market."


The unprecedented growth in DNN model complexity, size and the amount of training data have led to a commensurate increase in demand for computing and a search for minimal encoding. Recent research advocates Hybrid Block Floating-Point (HBFP) as a technique that minimizes silicon provisioning in accelerators by converting the majority of arithmetic operations in training to 8-bit fixed-point. In this paper, we perform a full-scale exploration of the HBFP design space including minimal mantissa encoding, varying block sizes, and mixed mantissa bit-width across layers and epochs. We propose Accuracy Boosters, an epoch-driven mixed-mantissa HBFP that uses 6-bit mantissa only in the last epoch and converts $99.7\%$ of all arithmetic operations in training to 4-bit mantissas. Accuracy Boosters enable reducing silicon provisioning for an HBFP training accelerator by $16.98\times$ as compared to FP32, while preserving or outperforming FP32 accuracy. 041b061a72


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