To check the GPU devices that TensorFlow can access, run the tf.config.list_physical_devices(‘GPU’) in the Python Interactive Shell. You will see all the GPU devices that TensorFlow can use in the output. Here, we have only one GPU GPU:0 that TensorFlow can use for AI/ML acceleration...
RuntimeError: torch_xla/csrc/tensor.cpp:486 : Check failed: data_ != nullptr *** Begin stack trace *** tensorflow::CurrentStackTrace() torch_xla::XLATensor::data() const torch_xla::XLATensor::GetIrValue() const torch_xla::XLATensor::native_batch_norm_backward(torch_xla::XLATensor cons...
has 1.5 billion parameters, and its parameters consume ~3gb of memory in 16-bit precision. however, one can hardly train it on a single gpu with 30gb of memory. that’s 10x the model’s memory, and you might wonder how that could be even possible. while the focus of this article is...
Also, if you plan to keep using the SMP translation of the model for further training in future, you can switch it off to save the SMP translation of the model for later use. Translating the model back to the Hugging Face Transformers model checkpoint format is needed when you wrap up ...
However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments...