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PyTorch destaca por su ejecución define‑by‑run, flexibilidad para investigación y soporte oficial de la comunidad Linux Foundation. En Itrion lo utilizamos para prototipar rápido, entrenar modelos a gran escala y desplegar inferencia optimizada con Torch Script y ONNX.
Hemos puesto en producción 210 modelos PyTorch que sirven 11 mil M de predicciones anuales, manteniendo un SLA 99,95 % en entornos regulados.
Pilares del ecosistema PyTorch
| Componente | Propósito | Ventaja clave |
|---|---|---|
| PyTorch Core | Tensors & Autograd | Define‑by‑run dinámico |
| torch.nn | Bloques de red | Modular y extensible |
| torch.distributed | Entrenamiento multinodo | Collective API NCCL |
| Torch Script | Graficado & compilación | Inference C++ sin Python |
| ONNX Runtime | Portabilidad | CPU/GPU/Edge acelerado |
Pipeline PyTorch Lightning en Itrion
Lightning automatiza horas de "boilerplate" y nos permite centrarnos en la investigación.
PyTorch vs TensorFlow – foco en producción
| Aspecto | PyTorch | TensorFlow |
|---|---|---|
| Ejecución | Dinámica (eager) | Gráfico + Eager |
| Expresión de modelos | nn.Module nativo Python | Keras / TF low level |
| Compilación | Torch Compile (ℹ️ TorchInductor) | XLA |
| Portabilidad | ONNX, TorchScript | SavedModel, TF Lite |
| Distribuido | Gloo / NCCL / RPC | ps, mirror, TPU |
Fortalezas de Itrion con PyTorch
Compilación avanzada Torch‑Inductor
Optimizamos kernels con Triton IR y scheduling multi‑stream, logrando un 33 % más de throughput en GPU A100.
Triton Inference Server
Batch dinámico, multi‑model & backend Python/C++; latencia P95 12 ms en vision API.
Entrenamiento multinodo + FSDP
Implementamos Fully Sharded Data Parallel y gradient checkpointing para modelos >10 B parámetros.
MLOps GitOps + Kubeflow
Pipelines Argo + LightningCLI, validación de datos y A/B testing automatizado.
210+
Modelos IA en producción
11 B
Predicciones al año
6 semanas
Media MVP delivery
Por qué elegir a Itrion
- • Especialistas en investigación aplicada: participamos en el programa PyTorch Edge Pilot.
- • Cost‑efficiency: ahorramos hasta un 38 % del gasto GPU mediante perfilado nvprof.
- • Compliance: pipelines auditados ISO 27017, ENS y HIPAA.
- • Soporte 24/7: respuesta S1 <15 min y retraining en caliente.
PyTorch stands out for its define‑by‑run execution, flexibility for research, and official support from the Linux Foundation community. At Itrion, we use it to prototype quickly, train large-scale models, and deploy optimized inference with Torch Script and ONNX.
We have put into production 210 PyTorch models serving 11 billion annual predictions, maintaining 99.95% SLA in regulated environments.
PyTorch ecosystem pillars
| Component | Purpose | Key advantage |
|---|---|---|
| PyTorch Core | Tensors & Autograd | Dynamic define‑by‑run |
| torch.nn | Network blocks | Modular and extensible |
| torch.distributed | Multi-node training | Collective API NCCL |
| Torch Script | Graphing & compilation | C++ inference without Python |
| ONNX Runtime | Portability | CPU/GPU/Edge accelerated |
PyTorch Lightning pipeline at Itrion
Lightning automates hours of boilerplate and lets us focus on research.
PyTorch vs TensorFlow – production focus
| Aspect | PyTorch | TensorFlow |
|---|---|---|
| Execution | Dynamic (eager) | Graph + Eager |
| Model expression | Native Python nn.Module | Keras / TF low level |
| Compilation | Torch Compile (ℹ️ TorchInductor) | XLA |
| Portability | ONNX, TorchScript | SavedModel, TF Lite |
| Distributed | Gloo / NCCL / RPC | ps, mirror, TPU |
Itrion strengths with PyTorch
Advanced Torch‑Inductor compilation
We optimize kernels with Triton IR and multi-stream scheduling, achieving 33% more throughput on GPU A100.
Triton Inference Server
Dynamic batching, multi-model & Python/C++ backend; P95 latency 12 ms on vision API.
Multi-node training + FSDP
We implement Fully Sharded Data Parallel and gradient checkpointing for models >10B parameters.
MLOps GitOps + Kubeflow
Argo pipelines + LightningCLI, data validation, and automated A/B testing.
210+
Models in production
11B
Predictions per year
6 weeks
Average MVP delivery
Why choose Itrion
- • Applied research specialists: we participate in the PyTorch Edge Pilot program.
- • Cost‑efficiency: saving up to 38% GPU spend with nvprof profiling.
- • Compliance: pipelines audited ISO 27017, ENS and HIPAA.
- • 24/7 support: S1 response <15 min and hot retraining.
PyTorch destaca por su ejecución define‑by‑run, flexibilidad para investigación y soporte oficial de la comunidad Linux Foundation. En Itrion lo utilizamos para prototipar rápido, entrenar modelos a gran escala y desplegar inferencia optimizada con Torch Script y ONNX.
Hemos puesto en producción 210 modelos PyTorch que sirven 11 mil M de predicciones anuales, manteniendo un SLA 99,95 % en entornos regulados.
Pilares del ecosistema PyTorch
| Componente | Propósito | Ventaja clave |
|---|---|---|
| PyTorch Core | Tensors & Autograd | Define‑by‑run dinámico |
| torch.nn | Bloques de red | Modular y extensible |
| torch.distributed | Entrenamiento multinodo | Collective API NCCL |
| Torch Script | Graficado & compilación | Inference C++ sin Python |
| ONNX Runtime | Portabilidad | CPU/GPU/Edge acelerado |
Pipeline PyTorch Lightning en Itrion
Lightning automatiza horas de "boilerplate" y nos permite centrarnos en la investigación.
PyTorch vs TensorFlow – foco en producción
| Aspecto | PyTorch | TensorFlow |
|---|---|---|
| Ejecución | Dinámica (eager) | Gráfico + Eager |
| Expresión de modelos | nn.Module nativo Python | Keras / TF low level |
| Compilación | Torch Compile (ℹ️ TorchInductor) | XLA |
| Portabilidad | ONNX, TorchScript | SavedModel, TF Lite |
| Distribuido | Gloo / NCCL / RPC | ps, mirror, TPU |
Fortalezas de Itrion con PyTorch
Compilación avanzada Torch‑Inductor
Optimizamos kernels con Triton IR y scheduling multi‑stream, logrando un 33 % más de throughput en GPU A100.
Triton Inference Server
Batch dinámico, multi‑model & backend Python/C++; latencia P95 12 ms en vision API.
Entrenamiento multinodo + FSDP
Implementamos Fully Sharded Data Parallel y gradient checkpointing para modelos >10 B parámetros.
MLOps GitOps + Kubeflow
Pipelines Argo + LightningCLI, validación de datos y A/B testing automatizado.
210+
Modelos IA en producción
11 B
Predicciones al año
6 semanas
Media MVP delivery
Por qué elegir a Itrion
- • Especialistas en investigación aplicada: participamos en el programa PyTorch Edge Pilot.
- • Cost‑efficiency: ahorramos hasta un 38 % del gasto GPU mediante perfilado nvprof.
- • Compliance: pipelines auditados ISO 27017, ENS y HIPAA.
- • Soporte 24/7: respuesta S1 <15 min y retraining en caliente.
PyTorch stands out for its define‑by‑run execution, flexibility for research, and official support from the Linux Foundation community. At Itrion, we use it to prototype quickly, train large-scale models, and deploy optimized inference with Torch Script and ONNX.
We have put into production 210 PyTorch models serving 11 billion annual predictions, maintaining 99.95% SLA in regulated environments.
PyTorch ecosystem pillars
| Component | Purpose | Key advantage |
|---|---|---|
| PyTorch Core | Tensors & Autograd | Dynamic define‑by‑run |
| torch.nn | Network blocks | Modular and extensible |
| torch.distributed | Multi-node training | Collective API NCCL |
| Torch Script | Graphing & compilation | C++ inference without Python |
| ONNX Runtime | Portability | CPU/GPU/Edge accelerated |
PyTorch Lightning pipeline at Itrion
Lightning automates hours of boilerplate and lets us focus on research.
PyTorch vs TensorFlow – production focus
| Aspect | PyTorch | TensorFlow |
|---|---|---|
| Execution | Dynamic (eager) | Graph + Eager |
| Model expression | Native Python nn.Module | Keras / TF low level |
| Compilation | Torch Compile (ℹ️ TorchInductor) | XLA |
| Portability | ONNX, TorchScript | SavedModel, TF Lite |
| Distributed | Gloo / NCCL / RPC | ps, mirror, TPU |
Itrion strengths with PyTorch
Advanced Torch‑Inductor compilation
We optimize kernels with Triton IR and multi-stream scheduling, achieving 33% more throughput on GPU A100.
Triton Inference Server
Dynamic batching, multi-model & Python/C++ backend; P95 latency 12 ms on vision API.
Multi-node training + FSDP
We implement Fully Sharded Data Parallel and gradient checkpointing for models >10B parameters.
MLOps GitOps + Kubeflow
Argo pipelines + LightningCLI, data validation, and automated A/B testing.
210+
Models in production
11B
Predictions per year
6 weeks
Average MVP delivery
Why choose Itrion
- • Applied research specialists: we participate in the PyTorch Edge Pilot program.
- • Cost‑efficiency: saving up to 38% GPU spend with nvprof profiling.
- • Compliance: pipelines audited ISO 27017, ENS and HIPAA.
- • 24/7 support: S1 response <15 min and hot retraining.
At Itrion, we provide direct, professional communication aligned with the objectives of each organisation. We diligently address all requests for information, evaluation, or collaboration that we receive, analysing each case with the seriousness it deserves.
If you wish to present us with a project, evaluate a potential solution, or simply gain a qualified insight into a technological or business challenge, we will be delighted to assist you. Your enquiry will be handled with the utmost care by our team.
At Itrion, we provide direct, professional communication aligned with the objectives of each organisation. We diligently address all requests for information, evaluation, or collaboration that we receive, analysing each case with the seriousness it deserves.
If you wish to present us with a project, evaluate a potential solution, or simply gain a qualified insight into a technological or business challenge, we will be delighted to assist you. Your enquiry will be handled with the utmost care by our team.