Best Application Development Software for PyTorch

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Giskard provides interfaces to AI & Business teams for evaluating and testing ML models using automated tests and collaborative feedback. Giskard accelerates teamwork to validate ML model validation and gives you peace-of-mind to eliminate biases, drift, or regression before deploying ML models into production.

TorchMetrics contains over 90+ PyTorch metrics and an easy-to use API to create custom metrics. Standardized interface to improve reproducibility. It reduces boilerplate. distributed-training compatible. It has been thoroughly tested. Automatic accumulation of batches. Automatic synchronization between multiple devices. TorchMetrics can be used in any PyTorch model or within PyTorch Lightning for additional benefits. Your data will always be on the same device that your metrics. Lightning allows you to log Metric objects directly, which reduces the amount of boilerplate. Like torch.nn's, most metrics can be logged in Lightning with both a class-based or functional version. The functional versions perform the basic operations necessary to calculate each metric. They are simple python functions which take torch.tensors as input and return the corresponding metrics as torch.tensors. Nearly all functional metrics include a class-based counterpart.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

Data scientists and developers can collaborate on ML projects by empowering them. Before publishing to end-users, build, load, optimize, and test models and their integrations. Data science workload can be reduced and decision-making improved by sharing and storing ML models throughout the organization. Publish updates to automatically push any changes to production models. ML-powered insights can be instantly provided within any web-based business app to increase efficiency and boost productivity. Our browser extension allows analysts and business users to seamlessly integrate models into any web application using drag-and-drop.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

Optimize ML models with real-time training metrics capture and alerting when anomalies are detected. To reduce the time and costs of training ML models, stop training when the desired accuracy has been achieved. To continuously improve resource utilization, automatically profile and monitor the system's resource utilization. Amazon SageMaker Debugger reduces troubleshooting time from days to minutes. It automatically detects and alerts you when there are common errors in training, such as too large or too small gradient values. You can view alerts in Amazon SageMaker Studio, or configure them through Amazon CloudWatch. The SageMaker Debugger SDK allows you to automatically detect new types of model-specific errors like data sampling, hyperparameter value, and out-of bound values.

Amazon SageMaker Studio (IDE) is an integrated development environment that allows you to access purpose-built tools to execute all steps of machine learning (ML). This includes preparing data, building, training and deploying your models. It can improve data science team productivity up to 10x. Quickly upload data, create notebooks, tune models, adjust experiments, collaborate within your organization, and then deploy models to production without leaving SageMaker Studio. All ML development tasks can be performed in one web-based interface, including preparing raw data and monitoring ML models. You can quickly move between the various stages of the ML development lifecycle to fine-tune models. SageMaker Studio allows you to replay training experiments, tune model features, and other inputs, and then compare the results.