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v1.6.x
  • Welcome
  • Getting Started
    • How Bacalhau Works
    • Getting Started
      • Step 1: Install the Bacalhau CLI
      • Step 2: Running Your Own Job
      • Step 3: Checking on the Status of Your Job
    • Creating Your Own Bacalhau Network
      • Setting Up a Cluster on Amazon Web Services (AWS) with Terraform 🚀
      • Setting Up a Cluster on Google Cloud Platform (GCP) With Terraform 🚀
      • Setting Up a Cluster on Azure with Terraform 🚀
    • Hardware Setup
    • Container Onboarding
      • Docker Workloads
      • WebAssembly (Wasm) Workloads
  • Setting Up
    • Running Nodes
      • Node Onboarding
      • GPU Installation
      • Job selection policy
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      • Configuring Your Input Sources
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    • Workload Onboarding
      • Container
        • Docker Workload Onboarding
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      • Python
        • Building and Running Custom Python Container
        • Running Pandas on Bacalhau
        • Running a Python Script
        • Running Jupyter Notebooks on Bacalhau
        • Scripting Bacalhau with Python
      • R (language)
        • Building and Running your Custom R Containers on Bacalhau
        • Running a Simple R Script on Bacalhau
      • Run CUDA programs on Bacalhau
      • Running a Prolog Script
      • Reading Data from Multiple S3 Buckets using Bacalhau
      • Running Rust programs as WebAssembly (WASM)
      • Generate Synthetic Data using Sparkov Data Generation technique
    • Networking Instructions
      • Accessing the Internet from Jobs
      • Utilizing NATS.io within Bacalhau
    • GPU Workloads Setup
    • Automatic Update Checking
    • Marketplace Deployments
      • Google Cloud Marketplace
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  • Guides
    • Configuration Management
    • Write a config.yaml
    • Write a SpecConfig
    • Using Labels and Constraints
  • Examples
    • Table of Contents for Bacalhau Examples
    • Data Engineering
      • Using Bacalhau with DuckDB
      • Ethereum Blockchain Analysis with Ethereum-ETL and Bacalhau
      • Convert CSV To Parquet Or Avro
      • Simple Image Processing
      • Oceanography - Data Conversion
      • Video Processing
      • Bacalhau and BigQuery
    • Data Ingestion
      • Copy Data from URL to Public Storage
      • Pinning Data
      • Running a Job over S3 data
    • Model Inference
      • EasyOCR (Optical Character Recognition) on Bacalhau
      • Running Inference on Dolly 2.0 Model with Hugging Face
      • Speech Recognition using Whisper
      • Stable Diffusion on a GPU
      • Stable Diffusion on a CPU
      • Object Detection with YOLOv5 on Bacalhau
      • Generate Realistic Images using StyleGAN3 and Bacalhau
      • Stable Diffusion Checkpoint Inference
      • Running Inference on a Model stored on S3
    • Model Training
      • Training Pytorch Model with Bacalhau
      • Training Tensorflow Model
      • Stable Diffusion Dreambooth (Finetuning)
    • Molecular Dynamics
      • Running BIDS Apps on Bacalhau
      • Coresets On Bacalhau
      • Genomics Data Generation
      • Gromacs for Analysis
      • Molecular Simulation with OpenMM and Bacalhau
    • Systems Engineering
      • Ad-hoc log query using DuckDB
  • References
    • Jobs Guide
      • Job Specification
        • Job Types
        • Task Specification
          • Engines
            • Docker Engine Specification
            • WebAssembly (WASM) Engine Specification
          • Publishers
            • IPFS Publisher Specification
            • Local Publisher Specification
            • S3 Publisher Specification
          • Sources
            • IPFS Source Specification
            • Local Source Specification
            • S3 Source Specification
            • URL Source Specification
          • Network Specification
          • Input Source Specification
          • Resources Specification
          • ResultPath Specification
        • Constraint Specification
        • Labels Specification
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    • Running Locally In Devstack
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  • Help & FAQ
    • Bacalhau FAQs
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      • v1.5.0 Release Notes
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  • Integrations
    • Apache Airflow Provider for Bacalhau
    • Lilypad
    • Bacalhau Python SDK
    • Observability for WebAssembly Workloads
  • Community
    • Social Media
    • Style Guide
    • Ways to Contribute
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  1. References
  2. Jobs Guide
  3. Job Specification
  4. Task Specification

Resources Specification

The Resources provides a structured way to detail the computational resources a Task requires. By specifying these requirements, you ensure that the task is scheduled on a node with adequate resources, optimizing performance and avoiding potential issues linked to resource constraints.

Resources Parameters:

  1. CPU (string: <optional>): Defines the CPU resources required for the task. Units can be specified in cores (e.g., 2 for 2 CPU cores) or in milliCPU units (e.g., 250m or 0.25 for 250 milliCPU units). For instance, if you have half a CPU core, you can represent it as 500m or 0.5.

  2. Memory (string: <optional>): Highlights the amount of RAM needed for the task. You can specify the memory in various units such as:

    • Kb for Kilobytes

    • Mb for Megabytes

    • Gb for Gigabytes

    • Tb for Terabytes

  3. Disk (string: <optional>): States the disk storage space needed for the task. Similarly, the disk space can be expressed in units like Gb for Gigabytes, Mb for Megabytes, and so on. As an example, 10Gb indicates 10 Gigabytes of storage space.

  4. GPU (string: <optional>): Denotes the number of GPU units required. For example, 2 signifies the requirement of 2 GPU units. This is crucial for tasks involving heavy computational processes, machine learning models, or tasks that leverage GPU acceleration.

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