Full API reference.

BEFORE YOU BEGIN

Find the API. Understand its role.

This is the complete upstream API map. Entries identify public names and important arguments; source docstrings remain the authoritative field-by-field reference.

Tensors

Tensor properties: .shape, .ndim, .size, .dtype, .device, .requires_grad, .is_leaf, .grad, .T.

Tensor methods: .backward(), .zero_grad(), .retain_grad(), .detach(), .numpy(), .tolist(), .item(), .astype(), .to(), .cpu(), .cuda(), .clone(), .reshape(), .transpose(), .flatten(), .sum(), .mean(), .max(), .min(), .argmax(), .argmin(), .exp(), .log(), .sqrt(), .abs(), .clip(), .matmul(), .dot().

Operations

Arithmetic: add, subtract, multiply, divide, negative, power, exp, log, sqrt, abs, clip.

Linear algebra: matmul, dot.

Reductions: sum, mean, max, min, variance.

Shape: reshape, transpose, flatten, concatenate, stack.

Activations: relu, sigmoid, tanh, softmax, log_softmax.

Convolution: conv2d(x, weight, bias, *, stride, padding), max_pool2d(x, pool_size, *, stride, padding), avg_pool2d(x, pool_size, *, stride, padding). Images are NHWC (batch, height, width, channels) and kernels are (kernel_h, kernel_w, in_channels, out_channels) — TensorFlow's layout. padding is "valid", "same" or an int.

Gradient mode

eo.no_grad(), eo.enable_grad(), eo.set_grad_enabled(mode) — all usable as context managers or decorators.

Layers and models

Attention helpers: eo.scaled_dot_product_attention(q, k, v, *, mask, dropout), eo.causal_mask(size), eo.padding_mask(lengths, size).

Module methods: parameters(), named_parameters(), named_modules(), register_buffer(), named_buffers(), buffers(), num_parameters(), zero_grad(), train(), eval(), to(device), state_dict(), load_state_dict(), get_config(), summary().

Losses

eo.MSELoss, eo.MAELoss, eo.BCELoss, eo.BCEWithLogitsLoss, eo.CrossEntropyLoss — all accept reduction in {"mean", "sum", "none"}. Functional forms live in everyo.nn: mse_loss, mae_loss, binary_cross_entropy, cross_entropy.

CrossEntropyLoss takes raw logits and applies log-softmax internally, so the final layer should be a plain Linear.

Optimizers

Both provide zero_grad(), step() and get_config().

Data

eo.Dataset, eo.ArrayDataset, eo.TransformDataset, eo.Subset, eo.load_csv_dataset, eo.DataLoader.

Preprocessing: eo.StandardScaler, eo.MinMaxScaler, eo.standardize, eo.one_hot_encode; everyo.data additionally exports normalize, min_max_scale and shuffle_arrays.

Splitting: eo.train_test_split, eo.random_split, eo.stratified_split.

Training

eo.Trainer(model, optimizer, loss_fn, *, metrics, device, gradient_clip) with fit(), evaluate(), predict() and predict_classes().

Callbacks: eo.EarlyStopping, eo.ModelCheckpoint, eo.ProgressLogger, eo.CSVLogger, eo.LearningRateScheduler, and eo.Callback to write your own.

Metrics: eo.accuracy, eo.confusion_matrix; everyo.training also exports binary_accuracy, mean_absolute_error, mean_squared_error and r2_score.

eo.History supports history["loss"], .best(), .last(), .to_dict(), .to_json() and .to_csv().

Serialization

eo.save(model, path, *, metadata, overwrite), eo.load(path, *, model, strict) and eo.inspect_archive(path).

Visualization

eo.plot_loss, eo.plot_accuracy, eo.plot_history, eo.plot_confusion_matrix, eo.plot_predictions, eo.plot_benchmark; everyo.visualization also exports plot_metric, plot_decision_boundary and plot_benchmark_bars. All accept save_path= and work headlessly.

Devices and CUDA

eo.device(spec, *, strict), eo.Device, and the eo.cuda namespace: is_available(), device_count(), runtime_info(), unavailable_reason(), plus the kernels add, multiply, matmul, relu, sum_all (each taking strict=).

Datasets

everyo.datasets: load_digits, render_digit, make_regression, make_blobs, make_moons, make_spirals, make_xor. All are generated locally from a seed; nothing is downloaded.

Configuration and logging

eo.load_config(path), eo.Config, eo.ModelConfig, eo.TrainingConfig, eo.DeviceConfig; eo.configure_logging(level) and eo.get_logger(name).

Mixed precision

eo.autocast(enabled=True, *, dtype="float16") — context manager and decorator; eo.is_autocast_enabled(), eo.autocast_dtype(). eo.GradScaler(init_scale, *, growth_factor, backoff_factor, growth_interval, enabled) with scale, backward, unscale_, step, update, get_scale, state_dict and load_state_dict. Only matmul and conv2d are autocast; see scaling.md.

ONNX export

eo.export_onnx(model, path, *, input_shape, input_name, output_name, opset, model_name), eo.run_onnx(path, inputs, *, input_name), eo.onnx_available(). everyo.serialization.onnx_export also exports SUPPORTED_LAYERS and DEFAULT_OPSET. Needs the optional everyo[onnx] extra.

Distributed training

everyo.distributed: spawn(fn, world_size, *, args, kwargs, start_method, timeout), ProcessGroup (rank, world_size, is_main, barrier, all_reduce_mean, broadcast, close), average_gradients(parameters, group), all_reduce_mean(arrays, group), shard_indices(count, rank, world_size, *, drop_last), available_workers(), DistributedError. Single machine only.

Exceptions

EveryOError is the base class. Subclasses: EveryOShapeError, EveryODeviceError, EveryODTypeError, EveryOGradientError, EveryOSerializationError, EveryOConfigurationError, EveryOBackendError, EveryOCudaError, and everyo.distributed.DistributedError.

Quantization

everyo.quantization.quantize(array, axis=None) creates a symmetric int8 representation. quantize_dynamic(model) returns an inference-only copy where each Linear layer is replaced by a per-output-channel QuantizedLinear. Activations stay floating point; no calibration dataset is required.

Profiling

Use with everyo.profiler.profile() as result: around inference or training. Every module call is timed only while the context is active. summary() aggregates calls, while export_chrome_trace() produces a trace for Perfetto or Chrome DevTools. record_function() adds user-defined regions.

Command line

everyo info        # versions and available backends
everyo doctor      # diagnose the installation
everyo benchmark   # measure matmul across backends
everyo test        # run the bundled test suite
everyo demo        # train the bundled digit classifier