A Cloud-Native Platform for Scalable Deep Learning of Genotype × Environment × Management Interactions

Platform Architecture

End-to-end pipeline from raw multi-modal inputs through modality-specific encoders, tensor-train fusion, distributed GCP training, to serverless inference and explainability outputs. Click any layer to expand details.

Connecting G×E×M Data to Deep Models

Three neural components — tensor-train networks, graph attention networks, and transformer-based sequence models — work in concert to extract, align, and fuse signals from genomic markers, climate sequences, and management treatments.

① Tensor-Train Networks for G×E×M Interaction Modeling

The G×E×M tensor X ∈ ℝ^{I×J×K×T} (genotypes × environments × management levels × traits) is inherently high-dimensional and sparse. We parameterize the interaction term using a tensor-train (TT) decomposition rather than a full Tucker decomposition, reducing parameter count from O(IJKd) to O((I+J+K)·r·d) while preserving expressivity. TT-cores are learned jointly with the encoder networks via end-to-end backpropagation.

# Tensor-Train core contraction for G×E×M fusion
import torch, torch.nn as nn

class TensorTrainFusion(nn.Module):
    def __init__(self, d=256, r=32):
        super().__init__()
        # TT-cores for Genotype, Environment, Management
        self.G_core = nn.Parameter(torch.randn(d, r))    # ℝ^{d × r}
        self.E_core = nn.Parameter(torch.randn(r, r))    # ℝ^{r × r}
        self.M_core = nn.Parameter(torch.randn(r, d))    # ℝ^{r × d_fuse}
        self.norm   = nn.LayerNorm(d)

    def forward(self, z_G, z_E, z_M):
        # z_G: (I,d)  z_E: (J,d)  z_M: (K,d)
        h = torch.einsum('id,dr->ir', z_G, self.G_core)  # (I, r)
        h = torch.einsum('ir,rr->ir', h,   self.E_core)  # (I, r)
        h = torch.einsum('ir,rd->id', h,   self.M_core)  # (I, d)
        return self.norm(h + z_G)                         # residual connection

② Graph Attention Networks over Genotype–Environment Graphs

A bipartite graph G = (V_G ∪ V_E, E) is constructed from trial data: edge (i,j) ∈ E exists if genotype i was evaluated in environment j. Edge features encode the management level, missing-data indicator, aridity index, and trial year. GAT layers propagate environment-specific stress signals to genotype nodes, enabling non-local G×E interactions that linear tensor methods cannot capture.

# Heterogeneous GAT over bipartite genotype–environment graph
from torch_geometric.nn import GATConv, HeteroConv

gat_conv = HeteroConv({
    ('genotype', 'tested_in', 'environment'):
        GATConv(in_channels=256, out_channels=256,
                heads=2, concat=False, edge_dim=8),
    ('environment', 'hosts', 'genotype'):
        GATConv(in_channels=256, out_channels=256,
                heads=2, concat=False, edge_dim=8),
}, aggr='mean')

# edge_attr: [management_level, missing_flag,
#             env_aridity_index, trial_year_normalized]
x_dict = gat_conv(x_dict, edge_index_dict, edge_attr_dict)

③ Transformer Sequence Models for Climate Encoding

Each environment j is characterized by a daily climate sequence from CHIRPS/ERA5 — precipitation, min/max temperature, solar radiation, and vapor pressure deficit over the growing season (~180 days). A 6-layer Transformer encoder with a prepended [CLS] token encodes this sequence into a fixed-length environment embedding z_E ∈ ℝ^{256}.

# Climate Transformer Encoder (per environment, 180-day sequence)
class ClimateTransformer(nn.Module):
    def __init__(self, n_vars=5, d_model=256,
                 nhead=8, num_layers=6):
        super().__init__()
        self.embed   = nn.Linear(n_vars, d_model)
        enc_layer    = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=1024, dropout=0.1,
            batch_first=True)
        self.encoder = nn.TransformerEncoder(enc_layer,
                                             num_layers=num_layers)
        self.pool    = nn.Linear(d_model, d_model)
        self.cls     = nn.Parameter(torch.zeros(1, 1, d_model))

    def forward(self, x):  # x: (B, T=180, n_vars)
        h   = self.embed(x)                        # (B, T, d)
        cls = self.cls.expand(h.size(0), -1, -1)  # (B, 1, d)
        h   = torch.cat([cls, h], dim=1)          # (B, T+1, d)
        h   = self.encoder(h)                      # (B, T+1, d)
        return self.pool(h[:, 0, :])             # CLS token → (B, d)

Datasets

Google Cloud Platform Architecture

A production-grade cloud engineering stack built on GCP primitives: containerized microservices, Kubernetes-orchestrated distributed training, Dataflow-managed ETL, and serverless inference via Cloud Functions.

Dockerfile — Trainer Container

FROM pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime

WORKDIR /app
COPY requirements.txt .
RUN  pip install --no-cache-dir -r requirements.txt
# requirements: torch-geometric captum shap google-cloud-storage
#               tensorboard scipy pandas scikit-learn

COPY src/ .
ENV  PYTHONPATH=/app

ENTRYPOINT ["python", "-m", "trainer.train"]

Cloud Function — Inference Entry Point

import functions_framework, torch, json
from google.cloud import storage

MODEL = None

def load_model():
    client = storage.Client()
    blob   = client.bucket("gxexm-models").blob("latest/model.pt")
    blob.download_to_filename("/tmp/model.pt")
    return torch.jit.load("/tmp/model.pt").eval()

@functions_framework.http
def predict(request):
    global MODEL
    if MODEL is None:
        MODEL = load_model()           # warm once per instance
    payload = request.get_json()
    with torch.no_grad():
        mu, sigma, attn = MODEL(
            torch.tensor(payload["snp"]),
            torch.tensor(payload["climate"]),
            torch.tensor(payload["management"]))
    return json.dumps({
        "mu":        mu.tolist(),
        "sigma":     sigma.tolist(),
        "attention": attn.tolist()
    })

Evaluation & Benchmarks

Two datasets, five-fold cross-validation, eight competing methods spanning classical statistics, tensor decompositions, machine learning, and deep learning baselines.

7.1 Experimental Setup

7.2 Competing Methods

7.3 Prediction Results — CIMMYT Wheat (5-Fold CV)

The improvement over the CP baseline (RMSE 0.28 → 0.21, a 25% reduction) provides evidence for the capacity of nonlinear deep learning to capture interaction patterns that the best linear tensor methods cannot. The rank correlation improvement for top-20% genotypes (0.73 → 0.83) is of direct practical value — correctly identifying elite candidates reduces the cost of unnecessary field trials on inferior varieties. The platform recovered 94% of the Tucker-factor variance (vs 87% for standard CP factor analysis) and identified nonlinear environmental stress patterns driven by geographic climate variation that were not recoverable from order-sorted CP factors.

7.4 Computational Efficiency

At large scale, GPU-accelerated distributed training makes the deep learning approach competitive with CPU-based tensor methods in wall-clock time despite added model complexity. At the medium scale typical of most national breeding programs (I≈500, J≈50), a complete run costs approximately $4 — accessible to programs without institutional HPC resources.

Explainability Layer

Three complementary explainability techniques provide breeders with interpretable, modality-specific attributions — from genomic loci to climate events to management factor rankings — returned alongside every inference call.

Integrated Gradients — Captum Implementation

from captum.attr import IntegratedGradients

ig = IntegratedGradients(model.forward_snp)

# Baseline: all-zero SNP vector (no genetic information)
baseline_snp = torch.zeros_like(snp_input)

attributions, delta = ig.attribute(
    inputs=snp_input,
    baselines=baseline_snp,
    additional_forward_args=(climate_input, mgmt_input),
    n_steps=50,
    return_convergence_delta=True
)
# attributions: (I, n_snp) — per-marker contribution to yield
# delta: convergence check — require |delta| < 0.05

per_marker_importance = attributions.abs().mean(dim=0)  # (n_snp,)

GAT Attention Weight Export

# Extract attention weights from heterogeneous GAT during forward pass
class InstrumentedGAT(nn.Module):
    def forward(self, x_dict, edge_index_dict, edge_attr_dict):
        x_out, attn = self.gat_conv(
            x_dict, edge_index_dict, edge_attr_dict,
            return_attention_weights=True)
        self.last_attn = {}
        for key, (edge_idx, weights) in attn.items():
            self.last_attn[key] = {
                "edges":   edge_idx.cpu().numpy(),
                "weights": weights.mean(dim=-1).cpu().numpy()
                # mean over heads → scalar per edge
            }
        return x_out