Which question is holding you up today?
From the first load to checkpoints, organize the points to verify before a compute campaign. Find guides to size, measure and keep results you can use.
My model hasn't run yet. Where do I start?
A memory envelope and the inputs for the first trial.
Sizing GPU memory: computation, reservations and validation
Compute weights in GiB, build an explicit reserve and validate the peak phase by phase. Worked examples, quantization pitfalls and per-card selection.
GPU memory: explaining the gap between estimate and peak
Distinguish weights, cache and activations, measure allocated and reserved by phase, then choose a margin with a notebook and a PyTorch protocol.
KV cache: calculating memory by context, GQA and batch
Calculate the KV cache using KV heads, context and concurrent sequences. Distinguish GQA, precision and static cache before a real trial.
The score is improving, but can I trust it?
An isolated evaluation set, reviewed errors and a qualified difference.
Building an ML evaluation set without data leakage
Separate groups, duplicates, and data uses to build a held-out ML evaluation set that is controllable and suited to your decision.
Analyze ML errors to choose the next experiment
Analyze classification and extraction errors: confusion matrix, references, taxonomy, regressions and verification of the next fix.
Variability across seeds: is an ML gap convincing?
Interpret ML repetitions with paired seeds, dispersion, and a minimum effect threshold. Worked example and limitations of a small sample.
I'm preparing a training run. What should I check before the run?
A full update, an explicit batch and usable traces.
GPU training: validate the loop before the campaign
Prepare the data, the batch and the resumption of a training run. One protocol per phase to control memory, gradients, validation and output files.
Microbatch and accumulation: computing the effective batch
Compute the effective batch, normalize the loss and verify gradient accumulation across one or more cards, along with its equivalence limits.
Tracking your ML experiments: from run to decision
Connect data, code, parameters, predictions and decision with an ML experiment manifest. An example of run and artifact tracking without a mandated tool.
Adapt or compress the model: how do I choose a variant?
A baseline, a controlled change and comparable quality.
Fine-tuning: choosing an adaptation and verifying its contribution
Prepare a fine-tuning with a reference, separate data, and a reload check. LoRA, quantized base, budget, and regression analysis.
Comparing quantizations: memory, quality, and useful cost
Compare reference, calibration, weight format, and KV cache with the same data. Measure memory and quality before deciding on the GPU budget.
Planning ML ablations: control, effects and budget
Build an ML ablation plan with a control, factors, interactions and a trial budget. A worked example to prioritize variants and reach a conclusion.
The budget is limited. Which trials should I run first?
An order of experiments and the plan's cost weighed against useful results.
Budgeting an ML campaign: runs, decisions, and the full package
Break down an ML campaign, prioritize variants, and tie the GPU package to useful results, with an example schedule and budget in USD.
Planning ML ablations: control, effects and budget
Build an ML ablation plan with a control, factors, interactions and a trial budget. A worked example to prioritize variants and reach a conclusion.
ML benchmark: comparing cost at equivalent quality
Fix the quality, measure the same corpus and compare the full cost of 3-, 7- or 30-day GPU plans. Protocol and raw table available for download.