I have a colab pro+ subscription and like many, I'm finding that the GPU allowance is rather small compared to the price. While I wait for GPU access I was wondering if the TPU VM would be a substitute. It's running now and seems slower. I have not adjusted my code. Is there any point in this? To be honest, I'm not quite clear on the difference between a TPU and a GPU. I ran lscpu in the console and …

I have this code: def transform(feature,target): return feature,target def frame_tensor(feature,target,batch_size=2024,shuffle=True): df = tf.data.Dataset.from_tensor_slices((feature,target)) df = df.map(transform) if shuffle: df = df.shuffle(1024) df = df.batch(batch_size).cache().prefetch(tf.data.experimental.AUTOTUNE) gc.collect() return df Now, let's say I write: dt = frame_tensor() I want to build my embedding model and train my dataset. In the embedding model, I will need the input dimension. When training the dataset, I will need both the train and validation dataset. My questions are: (1) How should I get the input dimension …

Firstly, thank you so much for looking at this post. I could really use some help. I have followed this guide as closely as possible: https://github.com/kingoflolz/mesh-transformer-jax I'm trying to fine-tune GPT-J with a small dataset of ~500 lines: You are important to me. <|endoftext|> I love spending time with you. <|endoftext|> You make me smile. <|endoftext|> feel so lucky to be your friend. <|endoftext|> You can always talk to me, even if it’s about something that makes you nervous or …

What are the downsides of using TPUs instead of GPUs when performing neural network training or inference? From what I read on https://www.predictiveanalyticsworld.com/machinelearningtimes/should-you-choose-a-gpu-or-a-tpu-to-train-your-machine-learning-models/10460/ there are only upsides: