Agentic AI, Model Training, and CUDA

Run Python code in a Google Colab notebook and manage notebook files across runtime sessions.

Manipulate strings, lists, tuples, sets, and dictionaries using their common operations.

Define a Python class with initialized state and methods.

Create NumPy arrays and apply indexing, reshaping, vectorized operations, and broadcasting.

Load, transform, and save tabular data with pandas.

Serialize and deserialize structured data using JSON.

Send an HTTP request from Python and interpret a JSON response.

Validate function inputs and handle expected failures with Python exceptions.

Identify the dimensions and meaning of scalars, vectors, matrices, and higher-rank tensors.

Compute dot products and matrix products and verify that their dimensions are compatible.

Calculate a neuron's weighted sum and apply a nonlinear activation function.

Compute the derivative of a simple single-variable function and interpret its slope.

Apply the chain rule to calculate gradients in a small computational graph.

Summarize a dataset using means, variances, and simple empirical probabilities.

Implement gradient descent to minimize a one-parameter loss function.

Frame a concrete prediction problem by identifying inputs, targets, and a measurable objective.

Partition labeled data into training, validation, and test sets without data leakage.

Transform raw features into numerical inputs suitable for a machine learning model.

Fit a baseline regression or classification model with scikit-learn.

Calculate and interpret appropriate classification or regression evaluation metrics.

Diagnose underfitting and overfitting from training and validation results.

Compare an improved model against a simple baseline using held-out data.

Create PyTorch tensors and perform shape-safe tensor operations.

Define a multilayer neural network by subclassing PyTorch's nn.Module.

Select an output layer and loss function that match a regression or classification task.

Use automatic differentiation to calculate parameter gradients.

Package examples into a Dataset and iterate over mini-batches with a DataLoader.

Implement PyTorch training and evaluation loops with distinct model modes.

Train and evaluate a small neural classifier on held-out data.

Explain why GPUs accelerate parallel tensor workloads using cores, streaming multiprocessors, and SIMT execution.

Select a CUDA-enabled Colab runtime and verify GPU availability from Python and PyTorch.

Move models and tensors between CPU and GPU without device-mismatch errors.

Measure GPU execution time and memory use while accounting for asynchronous execution.

Tune batch size and data loading to improve throughput within a fixed memory budget.

Apply automatic mixed-precision training with gradient scaling.

Diagnose and correct common out-of-memory and numerical-stability failures.

Compare data, tensor/model, and pipeline parallelism and select an appropriate multi-GPU strategy for a stated workload.

Encode and decode text with a subword tokenizer and inspect the resulting token sequence.

Map token IDs to learned embeddings and add positional information.

Compute scaled dot-product attention for a small set of query, key, and value vectors.

Apply a causal attention mask to prevent access to future tokens.

Explain how multiple attention heads capture different token relationships.

Implement a transformer block with attention, a feed-forward network, residual connections, and layer normalization.

Stack transformer blocks to construct a tiny autoregressive language model in PyTorch.

Calculate a transformer's parameter count and estimate the memory required for its parameters.

Convert a text corpus into training and validation batches with next-token labels.

Train a tiny autoregressive transformer on a hosted GPU and track its loss.

Save and resume model, optimizer, and training state from a checkpoint.

Calculate validation loss and perplexity for a language model.

Generate text with greedy decoding, temperature, top-k sampling, and top-p sampling.

Diagnose unstable or overfit language-model training from loss curves and generated samples.

Apply parameter-efficient fine-tuning to a compact pretrained model on a small dataset.

Choose between training from scratch, full fine-tuning, parameter-efficient tuning, and prompting for a stated use case.

Distinguish CUDA hardware, drivers, runtimes, kernels, and framework-level GPU operations.

Read a simple CUDA C kernel and identify its pointers, indices, and launch parameters.

Map array elements to CUDA threads using grid, block, and thread indices.

Write and launch an elementwise CUDA kernel from Python in Colab.

Add boundary checks and validate CUDA kernel output against a CPU reference.

Explain how registers, shared memory, caches, and global memory affect kernel performance.

Use CUDA events to benchmark a kernel without asynchronous timing errors.

Compare a custom CUDA kernel with an optimized PyTorch operation and decide whether customization is justified.

Call the Anthropic API from Colab while keeping credentials out of notebook source code.

Construct system and user messages that clearly specify an automation task and its constraints.

Request and validate structured model output against a defined JSON shape.

Measure token usage and estimate the API cost of a model interaction.

Define a tool with a focused purpose, typed inputs, and an unambiguous result.

Execute a model-requested tool and return its result in the expected conversation format.

Distinguish a deterministic LLM workflow from an agent that selects actions dynamically.

Create test cases that reveal ambiguous instructions or malformed model outputs.

Implement a bounded model-tool interaction loop with an explicit termination condition.

Represent an agent's task progress and tool results as explicit state.

Route requests among tools while restricting unavailable or inappropriate actions.

Add timeouts, retries, and idempotency protections to external tool calls.

Apply input validation, least-privilege access, and human confirmation to consequential actions.

Identify prompt-injection risks in untrusted tool data and isolate that data from control instructions.

Record traces that make each model decision, tool call, and failure inspectable.

Evaluate an agent with a repeatable suite of success, safety, and efficiency tests.

Specify a bounded automation use case with permitted actions, failure conditions, and measurable success criteria.

Implement mock task-management, calendar, and messaging tools with validated inputs and structured outputs.

Package the trained compact model behind an inference function that the automation agent can invoke as a tool.

Benchmark a relevant CUDA kernel and document whether it should replace the framework implementation.

Connect the Anthropic model, agent state, and tools into an end-to-end automation workflow.

Require user confirmation before the agent performs a consequential simulated action.

Run an evaluation set and categorize the agent's planning, tool-use, model, and safety failures.

Produce an architecture diagram and technical report explaining the system's data flow, model-training path, GPU usage, CUDA experiment, and safety boundaries.