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Open Source
563 posts about Open Source. Every summary links to the original.
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Introducing 🤗 Accelerate
🤗 Accelerate enables PyTorch developers to execute raw training loops across CPUs, multi-GPU setups, and TPUs with minimal boilerplate modifications. Standard distributed training typically demands manual device assignments, DistributedSampler configurations, and divergent code paths that break single-device portability. By wrapping models, optimizers, and dataloaders through a unified prepare call, Accelerate automatically manages hardware placement, mixed precision operations, and sampler batch slicing without requiring custom distributed samplers. Distributed evaluation is streamlined using a gather utility that consolidates prediction tensors across active worker processes. Furthermore, a dedicated command-line interface provides an interactive questionnaire to store runtime configuration defaults and orchestrate local or AWS SageMaker runs.
Supabase Beta March 2021
Supabase announced a series of platform updates for March 2021, headlined by the launch of Supabase Storage and new local development capabilities. The new storage offering integrates an open-source Fastify and TypeScript API backed by S3, while direct database connections now bundle PgBouncer for connection pooling. Developers can run the platform locally via the Supabase CLI, utilize Docker tooling for self-hosting, and access the open-sourced Supabase UI component library. Community contributions brought OAuth scopes to Supabase Auth for requesting elevated third-party provider permissions, alongside newly published free and pro pricing tiers. In addition, development is underway on an Elixir-based workflow engine compatible with the Amazon States Language for automated event handling.
Supabase Dot Com
Supabase announced a migration of its primary website from supabase.io to supabase.com over the upcoming week. User API endpoints will remain unchanged on supabase.co, requiring no configuration adjustments for active applications. The company originally selected supabase.io as a meme-inspired placeholder after releasing a Postgres Realtime engine on GitHub in 2019 and failing to find an available dot com name. Following advice from Y Combinator to secure a dot com address, the founders bypassed domain brokers charging percentage fees and negotiated directly with the domain owner, whose business address was located one hundred meters from the founder's home. The migration updates public website destinations while preserving existing developer infrastructure unaffected.
PgBouncer is now available in Supabase
Serverless JavaScript frameworks and developer tools frequently establish separate database connections during bursty traffic, rapidly exhausting PostgreSQL connection limits in the absence of traditional middleware. To resolve connection surges, Supabase integrated the open-source connection pooler PgBouncer directly onto the PostgreSQL server across all newly created projects. Rather than increasing the total number of connections PostgreSQL can open, PgBouncer recycles open connections and queues excess requests until active connections become available. Developers can manage pooling through the dashboard across Session, Transaction, and Statement modes, with Transaction mode recommended for serverless functions despite disabling session features like prepared statements. The default pool size is initially configured to 15 connections.
Workflows are coming to Supabase
Supabase is developing Workflows, an Elixir-based orchestration engine designed to coordinate complex serverless tasks and event-driven logic. Standard serverless and database-triggered functions often struggle with delayed execution and queuing without external cron processes. To solve this with native Postgres integration, the new engine adopts the open-source Amazon States Language specification to orchestrate functions across platforms including AWS, GCP, Azure, OpenFaaS, and Postgres itself. Execution states, jobs, queues, and logs are deeply integrated with Postgres using the Oban job processing library, though transient in-memory workers are also supported. The engine responds directly to HTTP calls or database change events delivered through Supabase Realtime.
Supabase CLI
Supabase announces its CLI as the first step toward running Supabase locally, managing database migrations, generating types and API or validation schemas from a database, managing projects, and eventually pushing local changes to production. The local stack runs through Docker Compose, using tools described as 100% compatible with every Supabase project, and a React setup can initialize it with supabase init before supabase start. For schema changes, the post favors database diffing over accumulating manual SQL migrations and adds a CLI mode and Docker image for pgAdmin Schema Diff, which tracks view-owner changes needed for Row Level Security and handles domains. The supabase eject command outputs Docker Compose, Kong, and Postgres configuration for self-hosting. An update dated 15 August 2022 states that CLI V1 is generally available and that a Management API is in beta.
Understanding BigBird's Block Sparse Attention
Transformer-based models face severe computational bottlenecks due to quadratic time and memory complexity, making sequences longer than 512 tokens impractical. BigBird addresses this limitation by approximating full attention with block sparse attention, scaling context lengths up to 4096 tokens at a lower computational cost. Instead of attending to all tokens, BigBird combines sliding attention for local context, global tokens for long-range relationships, and random tokens to accelerate information transfer. The model is integrated into HuggingFace Transformers, enabling fine-tuning for tasks like long-document summarization and extractive question answering. When sequences are shorter than 1024 tokens, using original full attention remains recommended.
Angels of Supabase
Supabase announced a 6 million dollar seed round led by Coatue, Mozilla, and Y Combinator, accompanied by over twenty technical angel investors. To guide its mission of providing an open-source alternative to Firebase, the company deliberately assembled a roster of angel backers comprising CTOs, developers, and developer advocates. The post features short interviews with several angel investors who share their career backgrounds, coding habits, and rationale for supporting the platform. Many investors highlighted the appeal of Supabase's developer ergonomics, its open-source foundation, and the advantages of utilizing Postgres directly alongside built-in authentication over proprietary NoSQL alternatives. Supabase also teased its upcoming Launch Week, during which the team plans to release one new feature daily for a week.
Launch week
Following its transition from Alpha to Beta, Supabase scheduled an intensive Launch Week to deliver multiple major features built over a three-month cycle. The event opened with a pricing model designed around predictable metrics like storage to prevent unexpected billing spikes from API requests. The team subsequently launched Supabase Storage, combining a Fastify Node.js API layer with Kong authentication and Postgres Row Level Security for policy evaluation. Additional launches delivered a CLI supporting database migrations and Docker self-hosting, alongside an open-source dashboard UI component library. Finally, Supabase added PgBouncer connection pooling for Postgres instances, previewed an Elixir workflow engine, and transitioned its primary domain to supabase.com.
The Partnership: Amazon SageMaker and Hugging Face
Hugging Face and Amazon formed a strategic partnership establishing Amazon Web Services as Hugging Face's preferred cloud provider to simplify training and deploying Transformer models. To facilitate this collaboration, the teams introduced Hugging Face Deep Learning Containers optimized for PyTorch and TensorFlow in Amazon SageMaker. The integration provides an extension to the SageMaker Python SDK, enabling data science teams to configure distributed training jobs across single-node and multi-node GPU clusters. Workflows integrate directly with SageMaker Automatic Model Tuning, SageMaker Studio, and Amazon S3 for managed experiment tracking and artifact storage. The open-source containers allow trained models to be exported for inference via SageMaker or published directly to the Hugging Face Model Hub while utilizing EC2 Spot instances to reduce compute expenses.
Developers stay up to date with intheloop.dev
Freelance engineer Kevin Grüneberg created intheloop.dev as an open-source aggregator to help developers track software updates, release notes, changelogs, tweets, and community discussions from a single platform. The initial release provides dedicated coverage for React, Kotlin, and Vite, with plans to introduce user accounts, notifications, and personalized digests. Although Grüneberg previously used Firebase for rapid prototyping, he found relational requirements like count queries, normalization, and foreign keys difficult or impossible to implement. He selected Supabase as the backend to combine fast integration with PostgreSQL capabilities, including SQL queries, constraints, views, and materialized views. This database architecture allows the application to handle structured data relationships efficiently while scaling future feature additions without restructuring core models.
Fine-Tune Wav2Vec2 for English ASR in Hugging Face with 🤗 Transformers
Wav2Vec2 is a pretrained Automatic Speech Recognition model that learns speech representations from over 50,000 hours of unlabeled audio using a contrastive objective and masked feature vectors. The model can be fine-tuned end-to-end on labeled speech datasets using Connectionist Temporal Classification without requiring an external language model. To demonstrate this process, a base-sized checkpoint is fine-tuned on the Timit dataset, which contains five hours of training data. Data preparation involves normalizing transcriptions to lowercase, removing special punctuation characters, and configuring both a specialized feature extractor and tokenizer. Evaluating the fine-tuned acoustic model without padding on the Timit test dataset yields a word error rate of 22.1%.
Hugging Face Reads, Feb. 2021 - Long-range Transformers
Standard transformer models face severe memory and compute bottlenecks due to the quadratic scaling of self-attention with sequence length. To scale beyond short sequence limits, efficient architectures introduce varied mechanisms such as custom sparse attention patterns, compressed recurrence, low-rank projections, and kernel approximations. Longformer integrates local dilated windowed attention with task-specific global tokens, functioning as a drop-in replacement that enables standard pre-trained models to process long inputs without full retraining. Concurrently, Compressive Transformers extend temporal memory by compressing discarded activations, while models like Linformer and Performer reduce computational complexity through projection and kernel estimation. Progress in long-range modeling also reveals that incrementally training models from short to longer sequences accelerates convergence and improves downstream performance.
Supabase Beta February 2021
Supabase published its February 2021 beta update marking one year of development as an open-source Firebase alternative. The release introduces dashboard interface improvements across the Table view, Auth section, and SQL Editor, which now features autocomplete suggestions for tables and columns. Authentication updates allow user redirection to specific site routes upon invoking signIn and signUp, while a new database management interface lets users configure Postgres replication settings and events for the realtime API. Infrastructure additions include a new deployment region in South Africa and a Docker Compose configuration intended to power local development in the forthcoming CLI. Documentation expanded with resources for GoTrue and Google OAuth, alongside ongoing RFCs covering connection pooling and local workflows.
Hugging Face on PyTorch / XLA TPUs
PyTorch / XLA integrates Cloud TPU training into Hugging Face transformers while preserving the standard Trainer interface. The implementation adds an xla device type and uses lazy execution to trace intermediate representation graphs before lowering them to XLA Higher Level Operations for compilation. Parallel data loading through MpDeviceLoader overlaps CPU tracing with TPU execution, while xm.optimizer_step coordinates gradient consolidation across data-parallel replicas. Checkpoints use xm.save to transfer tensors via CPU storage and restrict disk writes to the master process. Benchmarks on a Cloud TPU v3-8 system training bert-large-uncased on WikiText-103 demonstrate completion times of 178.4 minutes in FP32 precision and 106.4 minutes in BF16 precision.
Supabase Beta January 2021
Supabase released a collection of platform updates during January 2021 covering database tools, authentication integrations, regional availability, and infrastructure performance. Client libraries added support for PostgREST exact, planned, and estimated count queries to eliminate counting friction commonly experienced in platforms like Firebase. Authentication capabilities expanded to include Facebook and Azure login providers, an in-dashboard GoTrue audit trail, a helper function for user emails, and a prebuilt React Auth Widget. The platform also enabled new database hosting regions in London and Sydney while transitioning storage to GP3 for improved throughput. Furthermore, migrating API subdomains to Route53 alongside custom Let's Encrypt certificates yielded read performance benchmark gains of up to twelve percent.
Fit More and Train Faster With ZeRO via DeepSpeed and FairScale
Machine learning model sizes frequently outpace consumer GPU memory, preventing practitioners from loading or fine-tuning large architectures on standard hardware. To address these resource constraints, the Hugging Face Trainer introduced experimental support for Zero Redundancy Optimizer features from DeepSpeed and FairScale starting in transformers v4.2.0. In dual-GPU benchmarks with a t5-large model, FairScale and DeepSpeed integrations expanded allowable batch sizes and shortened training durations compared to baseline DistributedDataParallel runs. Furthermore, DeepSpeed permitted single-GPU fine-tuning of a t5-3b model on a 24GB RTX-3090 card at batch size 20, whereas the baseline execution crashed immediately with an out-of-memory error. These optimizations manage memory allocation internally to curb memory fragmentation while requiring changes only to training scripts rather than model code.
How we sped up transformer inference 100x for 🤗 API customers
Deploying large transformer models in production poses severe latency and scaling hurdles for machine learning engineers. Hugging Face achieved a cumulative 100x inference acceleration on its Accelerated Inference API by pairing library-level algorithmic adjustments with low-level hardware compilation. The initial 10x improvement reduces forward-pass computations, such as restricting attention calculations to the latest token in GPT generation tasks, while utilizing Rust-based tokenizers and caching mechanisms. The subsequent 10x speedup requires custom static graph compilation targeted to selected CPU or GPU hardware profiles, applying unused flow removal, instruction-specific layer fusion, and precision quantization via ONNX Runtime. These coordinated techniques enable real-time prediction latency across varied model sizes and demand profiles without sacrificing task accuracy.
Supabase Beta December 2020
Supabase transitioned to Beta following ten months of development concentrated on performance, security, and reliability. The platform introduced inline table documentation updates, storing descriptions directly as PostgreSQL comments and exposing them through OpenAPI specifications. Developers can now utilize a table view featuring immediate realtime update reflection alongside pagination for handling large datasets. Performance measurements show a 26 percent increase to 1460 requests per second in regions supporting AWS Graviton instances. Furthermore, the update introduces a new region in Sao Paulo, editing support for native Postgres arrays in the grid editor and side panel, and broader compatibility for custom database types.
Supabase Partners With Strive School To Help Teach Open Source
Supabase has partnered with fellow Y Combinator Summer 20 alumni Strive School to teach upcoming software engineers open-source development practices. Strive School operates an eight-month training program in Europe covering full-stack web and artificial intelligence engineering, with students paying tuition only after securing employment. Through this collaboration, Supabase aims to improve relational database instruction using Postgres while producing exclusive educational content for the program. In addition to creating learning materials, Supabase is hiring a Strive School graduate as an intern to integrate new engineering talent into its team. Supabase is also introducing a Student Developer Pack offering two years of free platform credits, Slack community access, founder office hours, and merchandise.
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