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How to Combine TensorFlow and PyTorch and Not Run Out of CUDA Memory | by GLAMI Engineering | Medium
![RuntimeError: CUDA out of memory. Tried to allocate 9.54 GiB (GPU 0; 14.73 GiB total capacity; 5.34 GiB already allocated; 8.45 GiB free; 5.35 GiB reserved in total by PyTorch) - Course Project - Jovian Community RuntimeError: CUDA out of memory. Tried to allocate 9.54 GiB (GPU 0; 14.73 GiB total capacity; 5.34 GiB already allocated; 8.45 GiB free; 5.35 GiB reserved in total by PyTorch) - Course Project - Jovian Community](https://jovian.ai/forum/uploads/default/original/2X/2/2a72fff20db2d8abbf7d252bdb4a6ed54b2f2b3e.png)
RuntimeError: CUDA out of memory. Tried to allocate 9.54 GiB (GPU 0; 14.73 GiB total capacity; 5.34 GiB already allocated; 8.45 GiB free; 5.35 GiB reserved in total by PyTorch) - Course Project - Jovian Community
PyTorch-Direct: Introducing Deep Learning Framework with GPU-Centric Data Access for Faster Large GNN Training | NVIDIA On-Demand
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Training language model with nn.DataParallel has unbalanced GPU memory usage - fastai users - Deep Learning Course Forums
![RuntimeError: CUDA out of memory. Tried to allocate 384.00 MiB (GPU 0; 11.17 GiB total capacity; 10.62 GiB already allocated; 145.81 MiB free; 10.66 GiB reserved in total by PyTorch) - Beginners - Hugging Face Forums RuntimeError: CUDA out of memory. Tried to allocate 384.00 MiB (GPU 0; 11.17 GiB total capacity; 10.62 GiB already allocated; 145.81 MiB free; 10.66 GiB reserved in total by PyTorch) - Beginners - Hugging Face Forums](https://aws1.discourse-cdn.com/standard14/uploads/hellohellohello/original/1X/c164a248b2ba7d82986a125ea7190c868081b81c.png)
RuntimeError: CUDA out of memory. Tried to allocate 384.00 MiB (GPU 0; 11.17 GiB total capacity; 10.62 GiB already allocated; 145.81 MiB free; 10.66 GiB reserved in total by PyTorch) - Beginners - Hugging Face Forums
![Leandro von Werra on Twitter: "Training large (transformer) models can be challenging and sooner or later a big red "CUDA out of memory" slaps you in the face. But there are a Leandro von Werra on Twitter: "Training large (transformer) models can be challenging and sooner or later a big red "CUDA out of memory" slaps you in the face. But there are a](https://pbs.twimg.com/media/FLVRV-EWYAEvjoK.jpg:large)