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tiling # define the input A = te.placeholder((m, n), name="A") # define the output and computation using Lamda expression B = te.compute((m, n), lambda i, j: A[i, j], name="B") # create a schedule (including a series of transformation) s = te.

# Step 1: Create a Conda environmentconda create --name myenv python=3.8 # Step 2: Activate the environment conda activate myenv # Step 3: Install packages with Conda conda install numpy scipy pandas # Step 4: Install remaining packages with Pip (if not available in Conda) pip install transformers requests After we activate the virtual environment, all the packages installed by Conda and pip will be in the virtual environment.

Concept of TensorIR, RelayIR, IRModule, Prim_fun, TVM Script Two levels represent the entire neural network program; the first level is a computational graph, and the second level is the tensor program.

Memory Activation values: 50 times the parameter memory Optimizer state: 2 times the parameter memory Gradients: 1 times the parameter memory KV Cache: 0.5 times the memory Number of Parameters Number of parameters: approximately (number)：$12h^2l$ Memory Usage Independent of input data

Introduction to Computer Science MIT 6.0001 Harvard CS50 Berkeley CS61A Data Structures and Algorithms Stanford CS106 MIT 6.006, 6.046 Berkeley CS61A, CS61B Princeton Operating Systems CMU 15-213 Berkeley CS162, CS262 MIT 6.

Unlike my peers majoring in computer science, I pursued a self-learning path based on my interests. While encountering some difficulties, the journey was also highly rewarding. My first exposure to computer science was during my freshman year, where I learned C language - my first programming language.