LSTM cells based on the sentence's length. ## PyTorch ## • Fundamental Concepts of PyTorch • Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools data • Train • Visualise • Iterate over weights examples ## Tensor ## • Tensor? • PyTorch Tensors are just like numpy arrays, but they can run on GPU. ## • Examples: import numpy # create a tensor 8741, 0.9729, 0.3821]]) ## Loading Data, Devices and CUDA • Numpy arrays to PyTorch tensors • torch.from_numpy(x_train) • Returns a cpu tensor! • PyTorch tensor to numpy • t.numpy() •

is Pytorch? • Training & Testing Neural Networks in Pytorch • Dataset & Dataloader • Tensors • torch.nn: Models, Loss Functions • torch.optim: Optimization • Save/load models ## Prerequisites shuffle=False)  ## Tensors • High-dimensional matrices (arrays)   Note: dim in PyTorch == axis in NumPy ## Tensors – Creating

黄海广 副教授 2022年03月 ## 本章目录 01 Tensors张量 02 Autograd自动求导 03 神经网络 04 训练一个分类器 ### 1. Tensors张量 ## 01 Tensors张量 02 Autograd自动求导 03 神经网络 04 训练一个分类器 ### 1. Tensors张量的概念 Tensor实际上就是一个多维数组（multidimensional 张量（大于等于3阶张量）  ### 1. Tensors张量的概念 ## • 创建张量的几种方法 - 用现有数据创建张量，使用 torch.tensor() • 如torch.tensor([[1., -1.], [1., -1.]]) - 创建与另一个张量具有相同大小的张量，请使用 torch.*_like • 如torch.rand_like() - 创建与其他张量具有相似类型但大小不同的张量，请使用tensor.new_*创建操作。 ### 1. Tensors张量的概念 ## • 查看张量的属性 • 查看Tensor类型 - tensor1 = torch.randn(2,3) #形状为(2,3)一组从标准正态分布中随机抽取的数据 • tensor1

(continues on next page) deserialize \ --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors Which downloads the model tensors from your S3 bucket and deserializes them. You can the shard's rank. Sharded models serialized with this script will be named as model-rank-%03d.tensors For more information on the available arguments for serializing, run `python -m examples.tensorize_vllm_model model_loader_extra_config=TensorizerConfig( tensorizer_uri = path_to_tensors, num_readers=3, ) ) ``` A serialized model can be used during

--dtype float16 \ deserialize \ --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors ``` Which downloads the model tensors from your S3 bucket and deserializes them. You the shard's rank. Sharded models serialized with this script will be named as model-rank-%03d.tensors For more information on the available arguments for serializing, run `python -m examples.tensorize_vllm_model model_loader_extra_config=TensorizerConfig( tensorizer_uri = path_to_tensors, num_readers=3, ) ) ``` A serialized model can be used

--dtype float16 \ deserialize \ --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors ``` Which downloads the model tensors from your S3 bucket and deserializes them. You the shard's rank. Sharded models serialized with this script will be named as model-rank-%03d.tensors For more information on the available arguments for serializing, run `python -m examples.tensorize_vllm_model model_loader_extra_config=TensorizerConfig( tensorizer_uri = path_to_tensors, num_readers=3, ) ) ``` A serialized model can be used

--dtype float16 \ deserialize \ --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors ``` Which downloads the model tensors from your S3 bucket and deserializes them. You shard's rank. Sharded models serialized with this script will be named as model-rank-%03d.tensors For more information on the available arguments for serializing, run `python -m examples.tensorize_vllm_model model_loader_extra_config=TensorizerConfig( tensorizer_uri = path_to_tensors, num_readers=3, ) ) ``` A serialized model can be used during

--dtype float16 \ deserialize \ --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors ``` Which downloads the model tensors from your S3 bucket and deserializes them. You the shard's rank. Sharded models serialized with this script will be named as model-rank-%03d.tensors For more information on the available arguments for serializing, run `python -m examples.tensorize_vllm_model model_loader_extra_config=TensorizerConfig( tensorizer_uri = path_to_tensors, num_readers=3, ) ) ``` A serialized model can be used

--dtype float16 \ deserialize \ --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/vllm/model.tensors ``` Which downloads the model tensors from your S3 bucket and deserializes them. You by providing the `--tensorizer-uri` CLI argument that is functionally the same as the `--path-to-tensors` argument in this script, along with `--vllm-tensorized`, to signify that the model to be deserialized ", load_format="tensorizer", tensorizer_uri=path_to_opt_tensors, num_readers=3, vllm_tensorized=True) """ ``` ```python