What is Bacalhau?

Bacalhau is a platform for fast, cost efficient, and secure computation by running jobs where the data is generated and stored. With Bacalhau, you can streamline your existing workflows without the need of extensive rewriting by running arbitrary Docker containers and WebAssembly (wasm) images as tasks. This architecture is also referred to as Compute Over Data (or CoD). Bacalhau was coined from the Portuguese word for salted Cod fish.

Bacalhau seeks to transform data processing for large-scale datasets to improve cost and efficiency, and to open up data processing to larger audiences. Our goals is to create an open, collaborative compute ecosystem that enables unparalleled collaboration. We (Expanso.io) offer a demo network so you can try out jobs without even installing. Give it a shot!

Why Bacalhau?

⚡️ Bacalhau simplifies the process of managing compute jobs by providing a unified platform for managing jobs across different regions, clouds, and edge devices.

🤝 Bacalhau provides reliable and network-partition resistant orchestration, ensuring that your jobs will complete even if there are network disruptions.

🚨 Bacalhau provides a complete and permanent audit log of exactly what happened, so you can be confident that your jobs are being executed securely.

🔐 You can run private workloads to reduce the chance of leaking private information or inadvertently sharing your data outside of your organization.

💸 Bacalhau reduces ingress/egress costs since jobs are processed closer to the source.

🤓 You can mount your data anywhere on your machine, and Bacalhau will be able to run against that data.

💥 You can integrate with services running on nodes to run a jobs, such as on DuckDB.

📚 Bacalhau operates at scale over parallel jobs. You can batch process petabytes (quadrillion bytes) of data.

How it works

Bacalhau concists of a peer-to-peer network of nodes that enables decentralized communication between computers. The network consists of two types of nodes:

Requester Node: responsible for handling user requests, discovering and ranking compute nodes, forwarding jobs to compute nodes, and monitoring the job lifecycle.

Compute Node: responsible for executing jobs and producing results. Different compute nodes can be used for different types of jobs, depending on their capabilities and resources.

The goal of the Bacalhau project is to make it easy to perform distributed computation next to where the data resides. In order to do this, first you need to ingest some data.

Data ingestion

Data is identified by its content identifier (CID) and can be accessed by anyone who knows the CID. Here are some options that can help you mount your data:

• Copy data from a URL to public storage

• Pin Data to public storage

• Copy Data from S3 Bucket to public storage

Security in Bacalhau

All workloads run under restricted Docker or WASM permissions on the node. Additionally, you can use existing (locked down) binaries that are pre-installed through Pluggable Executors.

Best practices in 12-factor apps is to use environment variables to store sensitive data such as access tokens, API keys, or passwords. These variables can be accessed by Bacalhau at runtime and are not visible to anyone who has access to the code or the server.

Alternatively, you can pre-provision credentials to the nodes and access those on a node by node basis.

Finally, endpoints (such as vaults) can also be used to provide secure access to Bacalhau. This way, the client can authenticate with Bacalhau using the token without exposing their credentials.

Workloads Bacalhau is best suited for

Bacalhau can be used for a variety of data processing workloads, including machine learning, data analytics, and scientific computing. It is well-suited for workloads that require processing large amounts of data in a distributed and parallelized manner.

Use Cases

Once you have more than 10 devices generating or storing around 100GB of data, you're likely to face challenges with processing that data efficiently. Traditional computing approaches may struggle to handle such large volumes, and that's where distributed computing solutions like Bacalhau can be extremely useful. Bacalhau can be used in various industries, including security, web serving, financial services, IoT, Edge, Fog, and multi-cloud. Bacalhau shines when it comes to data-intensive applications like data engineering, model training, model inference, molecular dynamics, etc.

Here are some example tutorials on how you can process your data with Bacalhau:

• Stable Diffusion AI

• Generate Realistic Images using StyleGAN3 and Bacalhau

• Object Detection with YOLOv5 on Bacalhau

• Running Genomics on Bacalhau

• Training Pytorch Model with Bacalhau

Community

Bacalhau has a very friendly community and we are always happy to help you get started:

• GitHub Discussions – ask anything about the project, give feedback or answer questions that will help other users.

• Join the Slack Community and go to #bacalhau channel – it is the easiest way engage with other members in the community and get help.

• Contributing – learn how to contribute to the Bacalhau project.

Next Steps

👉 Continue with Bacalhau Getting Started guide to learn how to install and run a job with the Bacalhau client.

👉 Or jump directly to try out the different Examples that showcases Bacalhau abilities.

Docker Workloads

Bacalhau executes jobs by running them within containers. Bacalhau employs a syntax closely resembling Docker, allowing you to utilize the same containers. The key distinction lies in how input and output data are transmitted to the container via IPFS, enabling scalability on a global level.

This section describes how to migrate a workload based on a Docker container into a format that will work with the Bacalhau client.

Requirements

Here are few things to note before getting started:

1. Container Registry: Ensure that the container is published to a public container registry that is accessible from the Bacalhau network.

2. Architecture Compatibility: Bacalhau supports only images that match the host node's architecture. Typically, most nodes run on linux/amd64, so containers in arm64 format are not able to run.

3. Input Flags: The --input ipfs://... flag supports only directories and does not support CID subpaths. The --input https://... flag supports only single files and does not support URL directories. The --input s3://... flag supports S3 keys and prefixes. For example, s3://bucket/logs-2023-04* includes all logs for April 2023.

Runtime Restrictions

To help provide a safe, secure network for all users, we add the following runtime restrictions:

1. Limited Ingress/Egress Networking:

All ingress/egress networking is limited as described in the networking documentation. You won't be able to pull data/code/weights/ etc. from an external source.

1. Data Passing with Docker Volumes:

A job includes the concept of input and output volumes, and the Docker executor implements support for these. This means you can specify your CIDs, URLs, and/or S3 objects as input paths and also write results to an output volume. This can be seen in the following example:

bacalhau docker run \
  -i s3://mybucket/logs-2023-04*:/input \
  -o apples:/output_folder \
  ubuntu \
  bash -c 'ls /input > /output_folder/file.txt'

The above example demonstrates an input volume flag -i s3://mybucket/logs-2023-04*, which mounts all S3 objects in bucket mybucket with logs-2023-04 prefix within the docker container at location /input (root).

Output volumes are mounted to the Docker container at the location specified. In the example above, any content written to /output_folder will be made available within the apples folder in the job results CID.

Once the job has run on the executor, the contents of stdout and stderr will be added to any named output volumes the job has used (in this case apples), and all those entities will be packaged into the results folder which is then published to a remote location by the publisher.

Onboarding Your Workload

Step 1 - Read Data From Your Directory

If you need to pass data into your container you will do this through a Docker volume. You'll need to modify your code to read from a local directory.

We make the assumption that you are reading from a directory called /inputs, which is set as the default.

Step 2 - Write Data to the Your Directory

If you need to return data from your container you will do this through a Docker volume. You'll need to modify your code to write to a local directory.

We make the assumption that you are writing to a directory called /outputs, which is set as the default.

Step 3 - Build and Push Your Image To a Registry

At this step, you create (or update) a Docker image that Bacalhau will use to perform your task. You build your image from your code and dependencies, then push it to a public registry so that Bacalhau can access it. This is necessary for other Bacalhau nodes to run your container and execute the task.

For example:

export IMAGE=myuser/myimage:latest
docker build -t ${IMAGE} .
docker image push ${IMAGE}

Step 4 - Test Your Container

To test your docker image locally, you'll need to execute the following command, changing the environment variables as necessary:

export LOCAL_INPUT_DIR=$PWD
export LOCAL_OUTPUT_DIR=$PWD
export CMD=(sh -c 'ls /inputs; echo do something useful > /outputs/stdout')
docker run --rm \
  -v ${LOCAL_INPUT_DIR}:/inputs  \
  -v ${LOCAL_OUTPUT_DIR}:/outputs \
  ${IMAGE} \
  ${CMD}

Let's see what each command will be used for:

export LOCAL_INPUT_DIR=$PWD

Exports the current working directory of the host system to the LOCAL_INPUT_DIR variable. This variable will be used for binding a volume and transferring data into the container.

export LOCAL_OUTPUT_DIR=$PWD

Exports the current working directory of the host system to the LOCAL_OUTPUT_DIR variable. Similarly, this variable will be used for binding a volume and transferring data from the container.

export CMD=(sh -c 'ls /inputs; echo do something useful > /outputs/stdout')

Creates an array of commands CMD that will be executed inside the container. In this case, it is a simple command executing 'ls' in the /inputs directory and writing text to the /outputs/stdout file.

docker run ... ${IMAGE} ${CMD}

Launches a Docker container using the specified variables and commands. It binds volumes to facilitate data exchange between the host and the container.

For example:

export LOCAL_INPUT_DIR=$PWD
export LOCAL_OUTPUT_DIR=$PWD
export CMD=(sh -c 'ls /inputs; echo "do something useful" > /outputs/stdout')
export IMAGE=ubuntu
docker run --rm \
  -v ${LOCAL_INPUT_DIR}:/inputs  \
  -v ${LOCAL_OUTPUT_DIR}:/outputs \
  ${IMAGE} \
  ${CMD}
cat stdout

The result of the commands' execution is shown below:

do something useful

Step 5 - Upload the Input Data

Data is identified by its content identifier (CID) and can be accessed by anyone who knows the CID. You can use either of these methods to upload your data:

You can choose to

• Copy data from a URL to public storage

• Pin Data to public storage

• Copy Data from S3 Bucket to public storage.

Step 6 - Run the Workload on Bacalhau

To launch your workload in a Docker container, using the specified image and working with input data specified via IPFS CID, run the following command.

bacalhau docker run --input ipfs://${CID} ${IMAGE} ${CMD}

To check the status of your job, run the following command.

bacalhau list --id-filter JOB_ID

To get more information on your job, you can run the following command.

bacalhau describe JOB_ID

To download your job, run.

bacalhau get JOB_ID

To put this all together into one would look like the following.

JOB_ID=$(bacalhau docker run ubuntu echo hello | grep 'Job ID:' | sed 's/.*Job ID: \([^ ]*\).*/\1/')
echo "The job ID is: $JOB_ID"
bacalhau list --id-filter $JOB_ID
sleep 5
bacalhau list --id-filter $JOB_ID
bacalhau get $JOB_ID
ls shards

This outputs the following.

CREATED   ID        JOB                      STATE      VERIFIED  PUBLISHED
 10:26:00  24440f0d  Docker ubuntu echo h...  Verifying
 CREATED   ID        JOB                      STATE      VERIFIED  PUBLISHED
 10:26:00  24440f0d  Docker ubuntu echo h...  Published            /ipfs/bafybeiflj3kha...
11:26:09.107 | INF bacalhau/get.go:67 > Fetching results of job '24440f0d-3c06-46af-9adf-cb524aa43961'...
11:26:10.528 | INF ipfs/downloader.go:115 > Found 1 result shards, downloading to temporary folder.
11:26:13.144 | INF ipfs/downloader.go:195 > Combining shard from output volume 'outputs' to final location: '/Users/phil/source/filecoin-project/docs.bacalhau.org'
job-24440f0d-3c06-46af-9adf-cb524aa43961-shard-0-host-QmYgxZiySj3MRkwLSL4X2MF5F9f2PMhAE3LV49XkfNL1o3

Alternatively, you can run your workload with a publicly accessible http(s) URL, which will download the data temporarily into your public storage:

export URL=https://download.geofabrik.de/antarctica-latest.osm.pbf
bacalhau docker run --input ${URL} ${IMAGE} ${CMD}

bacalhau list

bacalhau get JOB_ID

Troubleshooting

If you run into this compute error while running your docker image

Creating job for submission ... done ✅
Finding node(s) for the job ... done ✅
Node accepted the job ... done ✅
Error while executing the job.

This can often be resolved by re-tagging your docker image

Support

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).

Install the Bacalhau CLI

In this tutorial, you'll learn how to install and run a job with the Bacalhau client using the Bacalhau CLI or Docker.

Step 1 - Install the Bacalhau Client

The Bacalhau client is a command-line interface (CLI) that allows you to submit jobs to the Bacalhau. The client is available for Linux, macOS, and Windows. You can also run the Bacalhau client in a Docker container.

Step 1.1 - Install the Bacalhau CLI

curl -sL https://get.bacalhau.org/install.sh | bash

docker image rm -f ghcr.io/bacalhau-project/bacalhau:latest # Remove old image if it exists
docker pull ghcr.io/bacalhau-project/bacalhau:latest

Step 1.2 - Verify the Installation

To verify installation and check the version of the client and server, use the version command. To run a Bacalhau client command with Docker, prefix it with docker run ghcr.io/bacalhau-project/bacalhau:latest.

bacalhau version

docker run -it ghcr.io/bacalhau-project/bacalhau:latest version

If you're wondering which server is being used, the Bacalhau Project has a demo network that's shared with the community. This network allows you to familiarize with Bacalhau's capabilities and launch jobs from your computer without maintaining a compute cluster on your own.

Step 2 - Submit a Hello World job

To submit a job in Bacalhau, we will use the bacalhau docker run command. The command runs a job using the Docker executor on the node. Let's take a quick look at its syntax:

bacalhau docker run [FLAGS] IMAGE[:TAG] [COMMAND]

bacalhau docker run ubuntu echo Hello World

Job successfully submitted. Job ID: f8e7789d-8e76-4e6c-8e71-436e2d76c72e
Checking job status... (Enter Ctrl+C to exit at any time, your job will continue running):

    Communicating with the network  ................  done ✅  0.2s
       Creating job for submission  ................  done ✅  0.7s
                   Job in progress  ................  done ✅  2.1s

To download the results, execute:
    bacalhau get f8e7789d-8e76-4e6c-8e71-436e2d76c72e

To get more details about the run, execute:
    bacalhau describe f8e7789d-8e76-4e6c-8e71-436e2d76c72e

Job successfully submitted. Job ID: 9d20bbad-c3fc-48f8-907b-1da61c927fbd
Checking job status...

docker run -t ghcr.io/bacalhau-project/bacalhau:latest \
                docker run \
                --id-only \
                --wait \
                ubuntu:latest -- \
                sh -c 'uname -a && echo "Hello from Docker Bacalhau!"'

14:02:25.992 | INF pkg/repo/fs.go:81 > Initializing repo at '/root/.bacalhau' for environment 'production'
19b105c9-4cb5-43bd-a12f-d715d738addd

Step 3 - Checking the State of your Jobs

After having deployed the job, we now can use the CLI for the interaction with the network. The jobs were sent to the public demo network, where it was processed and we can call the following functions. The job_id will differ for every submission.

Step 3.1 - Job status:

You can check the status of the job using bacalhau list command adding the --id-filter flag and specifying your job id.

bacalhau list --id-filter 9d20bbad

Let's take a look at the results of the command execution in the terminal:

CREATED   ID          JOB                                       STATE      PUBLISHED
15:24:31  0ed7617d    Type:"docker",Params:"map[Entrypoint:<ni  Completed
                       l> EnvironmentVariables:[] Image:ubuntu:
                       latest Parameters:[sh -c uname -a && ech
                       o "Hello from Docker Bacalhau!"] Working
                       Directory:]"

When it says Completed, that means the job is done, and we can get the results.

Step 3.2 - Job information:

You can find out more information about your job by using bacalhau describe.

bacalhau describe 9d20bbad

Let's take a look at the results of the command execution in the terminal:

Job:
    APIVersion: V1beta2
    Metadata:
        ClientID: 0ff57b2521334a92e9ddab4b2f8202c887b1eaa35d2aa945ab0e247d3bc0aa88
        CreatedAt: "2023-12-21T15:24:31.750306239Z"
        ID: 0ed7617d-d5ff-40f7-8411-89830b3f3058
        Requester:
        RequesterNodeID: QmbxGSsM6saCTyKkiWSxhJCt6Fgj7M9cns1vzYtfDbB5Ws
        RequesterPublicKey: CAASpgIwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDEHTUAD1JzO0130W9vsaDGhU0PVgpcNjG3fYlE0sJ1BiBWENFuP4jx3Q9alcjNGhdRFdju0Mb/fidTOtJcPhxTdb+H6JxFP6HsADGes9jU4ylBU2SL2vfdb0KXzKdXjNHGGf4BuCGTcH07Oqxp209diK/cT7takL2fLjcgs1tM+6PzlfGzFqCPxvh9Sa0ek34mdmHjcp1XH8yjF1OKOuHvD+pYphqvOBL/2LEN+EBC4fz/QUnhUajCmKYO83MJcNUXSGxb4AN6K3DpVV+cJph7fj9ADdP7i996o2S4Gkz8W4Wpt/jICaPpkUjmyU3Jgcw7MHkZaYEzWxnnO2J936+pAgMBAAE=
    Spec:
    ...

This outputs all information about the job, including stdout, stderr, where the job was scheduled, and so on.

Step 3.3 - Job download:

You can download your job results directly by using bacalhau get.

bacalhau get 9d20bbad

This results in

Fetching results of job '0ed7617d'...
Results for job '0ed7617d' have been written to...
/Users/test/job-0ed7617d

In the command below, we created a directory called myfolder and download our job output to be stored in that directory.

Fetching results of job '0ed7617d'...
Results for job '0ed7617d' have been written to...
/myfolder

After the download has finished you should see the following contents in the results directory.

job-0ed7617d
├── exitCode
├── outputs
├── stderr
└── stdout

Step 4 - Viewing your Job Output

cat job-9d20bbad/stdout

That should print out the string Hello World.

Hello world

Step 5 - Where to go next?

Here are few resources that provide a deeper dive into running jobs with Bacalhau:

How Bacalhau works, Setting up Bacalhau, Examples & Use Cases

Support

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).

Bacalhau employs the and libraries for configuration management. Users can configure their Bacalhau node through a combination of command-line flags, environment variables, and the dedicated configuration file.

The Bacalhau Repo

Bacalhau manages its configuration, metadata, and internal state within a specialized repository named .bacalhau. Serving as the heart of the Bacalhau node, this repository holds the data and settings that determine node behavior. It's located on the filesystem, and by default, Bacalhau initializes this repository at $HOME/.bacalhau, where $HOME is the home directory of the user running the bacalhau process.

To customize this location, users can:

1. Set the BACALHAU_DIR environment variable to specify their desired path.

2. Utilize the --repo command line flag to specify their desired path.

Upon executing a Bacalhau command for the first time, the system will initialize the .bacalhau repository. If such a repository already exists, Bacalhau will seamlessly access its contents.

Structure of a Newly Initialized .bacalhau Repository

Below is the structure of a freshly initialized .bacalhau repository:

This repository comprises four directories and seven files:

Files

1. user_id.pem:

   • This file houses the Bacalhau node user's cryptographic private key, used for signing requests sent to a Requester Node.

   • Format: PEM.

2. repo.version:

   • Indicates the version of the Bacalhau node's repository.

   • Format: JSON, e.g., {"Version":1}.

3. libp2p_private_key:

   • Format: Base64 encoded RSA private key.

4. config.yaml:

   • Contains configuration settings for the Bacalhau node.

   • Format: YAML.

5. update.json:

   • A file containing the date/time when the last version check was made.

   • Format: JSON, e.g., {"LastCheck":"2024-01-24T11:06:14.631816Z"}

6. tokens.json:

   • A file containing the tokens obtained through authenticating with bacalhau clusters.

Directories

1. QmdGUjsMHEgtAfdtw7U62yPEcAZFtA33tKMsczLToegZtv-compute:

   • Note: The segment QmdGUjsMHEgtAfdtw7U62yPEcAZFtA33tKMsczLToegZtv is a unique NodeID for each Bacalhau node, derived from the libp2p_private_key.

2. QmdGUjsMHEgtAfdtw7U62yPEcAZFtA33tKMsczLToegZtv-requester:

   • Note: NodeID derivation is similar to the Compute directory.

3. executor_storages:

   • Storage for data handled by Bacalhau storage drivers.

4. plugins:

   • Houses binaries that allow the Compute node to execute specific tasks.

   • Note: This feature is currently experimental and isn't active during standard node operations.

Configuring a Bacalhau Node

Within a .bacalhau repository, a config.yaml file may be present. This file serves as the configuration source for the bacalhau node and adheres to the YAML format.

Although the config.yaml file is optional, its presence allows Bacalhau to load custom configurations; otherwise, Bacalhau is configured with built-in default values, environment variables and command line flags.

Modifications to the config.yaml file will not be dynamically loaded by the Bacalhau node. A restart of the node is required for any changes to take effect. Bacalhau determines its configuration based on the following precedence order, with each item superseding the subsequent:

1. Command-line Flag

2. Environment Variable

3. Config File

4. Defaults

Relationship Between config.yaml and Bacalhau Environment Variables

Bacalhau establishes a direct relationship between the value-bearing keys within the config.yaml file and corresponding environment variables. For these keys that have no further sub-keys, the environment variable name is constructed by capitalizing each segment of the key, and then joining them with underscores, prefixed with BACALHAU_.

For example, a YAML key with the path Node.IPFS.Connect translates to the environment variable BACALHAU_NODE_IPFS_CONNECT and is represented in a file like:

There is no corresponding environment variable for either Node or Node.IPFS. Config values may also have other environment variables that set them for simplicity or to maintain backwards compatibility.

Environments

• Bacalhau leverages the BACALHAU_ENVIRONMENT environment variable to determine the specific environment configuration when initializing a repository. Notably, if a .bacalhau repository has already been initialized, the BACALHAU_ENVIRONMENT setting will be ignored.

  By default, if the BACALHAU_ENVIRONMENT variable is not explicitly set by the user, Bacalhau will adopt the production environment settings.

  Below is a breakdown of the configurations associated with each environment:

  1. Production (public network)

  • Environment Variable: BACALHAU_ENVIRONMENT=production

  • Configurations:

    • Node.ClientAPI.Host: "bootstrap.production.bacalhau.org"

    • Node.Client.API.Host: 1234

    • ...other configurations specific to this environment...

  2. Staging (staging network)

  • Environment Variable: BACALHAU_ENVIRONMENT=staging

  • Configurations:

    • Node.ClientAPI.Host: "bootstrap.staging.bacalhau.org"

    • Node.Client.API.Host: 1234

    • ...other configurations specific to this environment...

  3. Development (development network)

  • Environment Variable: BACALHAU_ENVIRONMENT=development

  • Configurations:

    • Node.ClientAPI.Host: "bootstrap.development.bacalhau.org"

    • Node.Client.API.Host: 1234

    • ...other configurations specific to this environment...

  4. Local (private or local networks)

  • Environment Variable: BACALHAU_ENVIRONMENT=local

  • Configurations:

    • Node.ClientAPI.Host: "0.0.0.0"

    • Node.Client.API.Host: 1234

    • ...other configurations specific to this environment...

  ---

Usage Examples

How to initialize a Bacalhau Server for a local private network

How to initialize a Bacalhau Server with a custom repo path

Or

How to start a Bacalhau Server with DEBUG logs

Different jobs may require different amounts of resources to execute. Some jobs may have specific hardware requirements, such as GPU. This page describes how to specify hardware requirements for your job.

Docker Executor

The following table describes how to specify hardware requirements for the Docker executor.

How it Works

When you specify hardware requirements, the job will be offered out to the network to see if there are any nodes that can satisfy the requirements. If there are, the job will be scheduled on the node and the executor will be started.

GPU Setup

Bacalhau supports GPU workloads. Learn how to run a job using GPU workloads with the Bacalhau client.

Prerequisites

• The Bacalhau network must have an executor node with a GPU exposed

• Your container must include the CUDA runtime (cudart) and must be compatible with the CUDA version running on the node

Usage

Use following command to see available resources amount:

To submit a request for a job that requires more than the standard set of resources, add the --cpu and --memory flags. For example, for a job that requires 2 CPU cores and 4Gb of RAM, use --cpu=2 --memory=4Gb, e.g.:

To submit a GPU job request, use the --gpu flag under the docker run command to select the number of GPUs your job requires. For example:

Limitations

The following limitations currently exist within Bacalhau.

1. Maximum CPU and memory limits depend on the participants in the network

2. For GPU:

   1. NVIDIA, Intel or AMD GPUs only

   2. Only the Docker Executor supports GPUs

When running a node, you can choose which jobs you want to run by using configuration options, environment variables or flags to specify a job selection policy.

Job selection probes

If you want more control over making the decision to take on jobs, you can use the --job-selection-probe-exec and --job-selection-probe-http flags.

These are external programs that are passed the following data structure so that they can make a decision about whether or not to take on a job:

The exec probe is a script to run that will be given the job data on stdin, and must exit with status code 0 if the job should be run.

The http probe is a URL to POST the job data to. The job will be rejected if the HTTP request returns a non-positive status code (e.g. >= 400).

For example, the following response will reject the job:

If the HTTP response is not a JSON blob, the content is ignored and any non-error status code will accept the job.

Bacalhau supports the three main 'pillars' of observability - logging, metrics, and tracing. Bacalhau uses the for metrics and tracing, which can be configured using the . Exporting metrics and traces can be as simple as setting the OTEL_EXPORTER_OTLP_PROTOCOL and OTEL_EXPORTER_OTLP_ENDPOINT environment variables. Custom code is used for logging as the .

Logging

Logging in Bacalhau outputs in human-friendly format to stderr at INFO level by default, but this can be changed by two environment variables:

• LOG_LEVEL - Can be one of trace, debug, error, warn or fatal to output more or fewer logging messages as required

• LOG_TYPE - Can be one of the following values:

  • default - output logs to stderr in a human-friendly format

  • json - log messages outputted to stdout in JSON format

  • combined - log JSON formatted messages to stdout and human-friendly format to stderr

Log statements should include the relevant trace, span and job ID so it can be tracked back to the work being performed.

Metrics

Bacalhau produces a number of different metrics including those around the libp2p resource manager (rcmgr), performance of the requester HTTP API and the number of jobs accepted/completed/received.

Tracing

Traces are produced for all major pieces of work when processing a job, although the naming of some spans is still being worked on. You can find relevant traces covering working on a job by searching for the jobid attribute.

Viewing

The metrics and traces can easily be forwarded to a variety of different services as we use OpenTelemetry, such as Honeycomb or Datadog.

To view the data locally, or simply to not use a SaaS offering, you can start up Jaeger and Prometheus placing these three files into a directory then running docker compose start while running Bacalhau with the OTEL_EXPORTER_OTLP_PROTOCOL=grpc and OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 environment variables.

Authentication and authorization

Bacalhau includes a flexible auth system that supports multiple methods of auth that are appropriate for different deployment environments.

By default

With no specific authentication configuration supplied, Bacalhau runs in "anonymous mode" – which allows unidentified users limited control over the system. "Anonymous mode" is only appropriate for testing or evaluation setups.

In anonymous mode, Bacalhau will allow:

• Users identified by a self-generated private key to submit any job and cancel their own jobs.

• Users not identified by any key to access other read-only endpoints, such as to read job lists, describe jobs, and query node or agent information.

Restricting anonymous access

Bacalhau auth is controlled by policies. Configuring the auth system is done by supplying a different policy file.

Restricting API access to only users that have authenticated requires specifying a new authorization policy. You can download a policy that restricts anonymous access and install it by using:

Once the node is restarted, accessing the node APIs will require the user to be authenticated, but by default will still allow users with a self-generated key to authenticate themselves.

Restricting the list of keys that can authenticate to only a known set requires specifying a new authentication policy. You can download a policy that restricts key-based access and install it by using:

Then, modify the allowed_clients variable in challange_ns_no_anon.rego to include acceptable client IDs, found by running bacalhau id.

Once the node is restarted, only keys in the allowed list will be able to access any API.

Username and password access

Users can authenticate using a username and password instead of specifying a private key for access. Again, this requires installation of an appropriate policy on the server.

Passwords are not stored in plaintext and are salted. The downloaded policy expects password hashes and salts generated by scrypt. To generate a salted password, the helper script in pkg/authn/ask/gen_password can be used:

This will ask for a password and generate a salt and hash to authenticate with it. Add the encoded username, salt and hash into the ask_ns_password.rego.

Writing custom policies

In principle, Bacalhau can implement any auth scheme that can be described in a structured way by a policy file.

Custom authentication policies

Bacalhau will pass information pertinent to the current request into every authentication policy query as a field on the input variable. The exact information depends on the type of authentication used.

challenge authentication

challenge authentication uses identifies the user by the presence of a private key. The user is asked to sign an input phrase to prove they have the key they are identifying with.

Policies used for challenge authentication do not need to actually implement the challenge verification logic as this is handled by the core code. Instead, they will only be invoked if this verification passes.

Policies for this type will need to implement these rules:

• bacalhau.authn.token: if the user should be authenticated, an access token they should use in subsequent requests. If the user should not be authenticated, should be undefined.

They should expect as fields on the input variable:

• clientId: an ID derived from the user's private key that identifies them uniquely

• nodeId: the ID of the requester node that this user is authenticating with

• signingKey: the private key (as a JWK) that should be used to sign any access tokens to be returned

The simplest possible policy might therefore be this policy that returns the same opaque token for all users:

ask authentication

ask authentication uses credentials supplied manually by the user as identification. For example, an ask policy could require a username and password as input and check these against a known list. ask policies do all the verification of the supplied credentials.

Policies for this type will need to implement these rules:

• bacalhau.authn.token: if the user should be authenticated, an access token they should use in subsequent requests. If the user should not be authenticated, should be undefined.

• bacalhau.authn.schema: a static JSON schema that should be used to collect information about the user. The type of declared fields may be used to pick the input method, and if a field is marked as writeOnly then it will be collected in a secure way (e.g. not shown on screen). The schema rule does not receive any input data.

They should expect as fields on the input variable:

• ask: a map of field names from the JSON schema to strings supplied by the user. The policy should validate these credentials.

• nodeId: the ID of the requester node that this user is authenticating with

• signingKey: the private key (as a JWK) that should be used to sign any access tokens to be returned

The simplest possible policy might therefore be one that asks for no data and returns the same opaque token for every user:

Custom authorization policies

Authorization policies do not vary depending on the type of authentication used – Bacalhau uses one authz policy for all API requests.

Authz policies are invoked for every API request. Authz policies should check the validity of any supplied access tokens and issue an authz decision for the requested API endpoint. It is not required that authz policies enforce that an access token is present – they may choose to grant access to unauthorized users.

Policies will need to implement these rules:

• bacalhau.authz.token_valid: true if the access token in the request is "valid" (but does not necessarily grant access for this request), or false if it is invalid for every request (e.g. because it has expired) and should be discarded.

• bacalhau.authz.allow: true if the user should be permitted to carry out the input request, false otherwise.

They should expect as fields on the input variable for both rules:

• http: details of the user's HTTP request:

  • host: the hostname used in the HTTP request

  • method: the HTTP method (e.g. GET, POST)

  • path: the path requested, as an array of path components without slashes

  • query: a map of URL query parameters to their values

  • headers: a map of HTTP header names to arrays representing their values

  • body: a blob of any content submitted as the body

• constraints: details about the receiving node that should be used to validate any supplied tokens:

  • cert: keys that the input token should have been signed with

  • iss: the name of a node that this node will recognize as the issuer of any signed tokens

  • aud: the name of this node that is receiving the request

Notably, the constraints data is appropriate to be passed directly to the Rego io.jwt.decode_verify method which will validate the access token as a JWT against the given constraints.

The simplest possible authz policy might be this one that allows all users to access all endpoints:

Bacalhau supports GPUs out of the box and defaults to allowing execution on all GPUs installed on the node.

Prerequisites

Bacalhau makes the assumption that you have installed all the necessary drivers and tools on your node host and have appropriately configured them for use by Docker.

In general for GPUs from any vendor, the Bacalhau client requires:

Nvidia

• nvidia-smi installed and functional

You can test whether you have a working GPU setup with the following command:

AMD

Bacalhau requires AMD drivers to be appropriately installed and access to the rocm-smi tool.

You can test whether you have a working GPU setup with the following command, which should print details of your GPUs:

Intel

Bacalhau requires appropriate Intel drivers to be installed and access to the xpu-smi tool.

You can test whether you have a working GPU setup with the following command, which should print details of your GPUs:

GPU Node Configuration

Bacalhau supports running programs that are compiled to . With the Bacalhau client, you can upload WASM programs, retrieve data from public storage, read and write data, receive program arguments, and access environment variables.

Prerequisites and Limitations

1. Supported WebAssembly System Interface (WASI) Bacalhau can run compiled WASM programs that expect the WebAssembly System Interface (WASI) Snapshot 1. Through this interface, WebAssembly programs can access data, environment variables, and program arguments.

2. Networking Restrictions All ingress/egress networking is disabled – you won't be able to pull data/code/weights etc. from an external source. WASM jobs can say what data they need using URLs or CIDs (Content IDentifier) and can then access the data by reading from the filesystem.

3. Single-Threading There is no multi-threading as WASI does not expose any interface for it.

Onboarding Your Workload

Step 1: Replace network operations with filesystem reads and writes

If your program typically involves reading from and writing to network endpoints, follow these steps to adapt it for Bacalhau:

1. Replace Network Operations: Instead of making HTTP requests to external servers (e.g., example.com), modify your program to read data from the local filesystem.

2. Input Data Handling: Specify the input data location in Bacalhau using the --input flag when running the job. For instance, if your program used to fetch data from example.com, read from the /inputs folder locally, and provide the URL as input when executing the Bacalhau job. For example, --input http://example.com.

3. Output Handling: Adjust your program to output results to standard output (stdout) or standard error (stderr) pipes. Alternatively, you can write results to the filesystem, typically into an output mount. In the case of WASM jobs, a default folder at /outputs is available, ensuring that data written there will persist after the job concludes.

By making these adjustments, you can effectively transition your program to operate within the Bacalhau environment, utilizing filesystem operations instead of traditional network interactions.

Step 2: Configure your compiler to output WASI-compliant WebAssembly

You will need to compile your program to WebAssembly that expects WASI. Check the instructions for your compiler to see how to do this.

Step 3: Upload the input data

Data is identified by its content identifier (CID) and can be accessed by anyone who knows the CID. You can use either of these methods to upload your data:

Step 4: Run your program

You can run a WebAssembly program on Bacalhau using the bacalhau wasm run command.

Run Locally Compiled Program:

If your program is locally compiled, specify it as an argument. For instance, running the following command will upload and execute the main.wasm program:

Alternative Program Specification:

You can use a Content IDentifier (CID) for a specific WebAssembly program.

Input Data Specification:

Make sure to specify any input data using --input flag.

This ensures the necessary data is available for the program's execution.

Program arguments

You can give the WASM program arguments by specifying them after the program path or CID. If the WASM program is already compiled and located in the current directory, you can run it by adding arguments after the file name:

For a specific WebAssembly program, run:

Environment variables

You can also specify environment variables using the -e flag.

Examples

Support

In this tutorial we will go over the components and the architecture of Bacalhau. You will learn how it is built, what components are used, how you could interact and how you could use Bacalhau.

Chapter 1 - Architecture

Bacalhau is a peer-to-peer network of nodes that enables decentralized communication between computers. The network consists of two types of nodes, which can communicate with each other.

The requester and compute nodes together form a p2p network and use gossiping to discover each other, share information about node capabilities, available resources and health status. Bacalhau is a peer-to-peer network of nodes that enables decentralized communication between computers.

To interact with the Bacalhau network, users can use the Bacalhau CLI (command-line interface) to send requests to a requester node in the network. These requests are sent using the JSON format over HTTP, a widely-used protocol for transmitting data over the internet. Bacalhau's architecture involves two main sections which are the core components and interfaces.

Core Components

The core components are responsible for handling requests and connecting different nodes. The network includes two different components:

Interfaces

The interfaces handle the distribution, execution, storage and publishing of jobs. In the following all the different components are described and their respective protocols are shown.

Chapter 2 - Job cycle

Job preparation

Job Submission

Bacalhau provides an interface to interact with the server via a REST API. Bacalhau uses 127.0.0.1 as the localhost and 1234 as the port by default.

Job Acceptance

When a job is submitted to a requester node, it selects compute nodes that are capable and suitable to execute the job, and communicate with them directly. The compute node has a collection of named executors, storage sources, and publishers, and it will choose the most appropriate ones based on the job specifications.

Job execution

Results publishing

When the Compute node completes the job, it publishes the results to S3's remote storage, IPFS.

Chapter 3 - Returning Information

The Bacalhau client receives updates on the task execution status and results. A user can access the results and manage tasks through the command line interface.

Get Job Results

To Get the results of a job you can run the following command.

One can choose from a wide range of flags, from which a few are shown below.

Describe a Job

To describe a specific job, inserting the ID to the CLI or API gives back an overview of the job.

List of Jobs

If you run more then one job or you want to find a specific job ID

Job Executions

To list executions follow the following commands.

Chapter 4 - Monitoring and Management

Stop a Job

Job History

Job Logs

By default, the requester node APIs used by the Bacalhau CLI are accessible over HTTP, but it is possible to configure it to use Transport Level Security (TLS) so that they are accessible over HTTPS instead. There are several ways to obtain the necessary certificates and keys, and Bacalhau supports obtaining them via ACME and Certificate Authorities or even self-signing them.

Getting a certificate from Let's Encrypt with ACME

Automatic Certificate Management Environment (ACME) is a protocol that allows for automating the deployment of Public Key Infrastructure, and is the protocol used to obtain a free certificate from the Certificate Authority.

Using the --autocert [hostname] parameter to the CLI (in the serve and devstack commands), a certificate is obtained automatically from Lets Encrypt. The provided hostname should be a comma-separated list of hostnames, but they should all be publicly resolvable as Lets Encrypt will attempt to connect to the server to verify ownership (using the challenge). On the very first request this can take a short time whilst the first certificate is issued, but afterwards they are then cached in the bacalhau repository.

Alternatively, you may set these options via the environment variable, BACALHAU_AUTO_TLS. If you are using a configuration file, you can set the values inNode.ServerAPI.TLS.AutoCert instead.

A cache of ACME data is held in the config repository, by default ~/.bacalhau/autocert-cache, and this will be used to manage renewals to avoid rate limits.

Getting a certificate from a Certificate Authority

Obtaining a TLS certificate from a Certificate Authority (CA) without using the Automated Certificate Management Environment (ACME) protocol involves a manual process that typically requires the following steps:

1. Choose a Certificate Authority: First, you need to select a trusted Certificate Authority that issues TLS certificates. Popular CAs include DigiCert, GlobalSign, Comodo (now Sectigo), and others. You may also consider whether you want a free or paid certificate, as CAs offer different pricing models.

2. Generate a Certificate Signing Request (CSR): A CSR is a text file containing information about your organization and the domain for which you need the certificate. You can generate a CSR using various tools or directly on your web server. Typically, this involves providing details such as your organization's name, common name (your domain name), location, and other relevant information.

3. Submit the CSR: Access your chosen CA's website and locate their certificate issuance or order page. You'll typically find an option to "Submit CSR" or a similar option. Paste the contents of your CSR into the provided text box.

4. Verify Domain Ownership: The CA will usually require you to verify that you own the domain for which you're requesting the certificate. They may send an email to one of the standard domain-related email addresses (e.g., admin@yourdomain.com, webmaster@yourdomain.com). Follow the instructions in the email to confirm domain ownership.

5. Complete Additional Verification: Depending on the CA's policies and the type of certificate you're requesting (e.g., Extended Validation or EV certificates), you may need to provide additional documentation to verify your organization's identity. This can include legal documents or phone calls from the CA to confirm your request.

6. Payment and Processing: If you're obtaining a paid certificate, you'll need to make the payment at this stage. Once the CA has received your payment and completed the verification process, they will issue the TLS certificate.

Once you have obtained your certificates, you will need to put two files in a location that bacalhau can read them. You need the server certificate, often called something like server.cert or server.cert.pem, and the server key which is often called something like server.key or server.key.pem.

Once you have these two files available, you must start bacalhau serve which two new flags. These are tlscert and tlskey flags, whose arguments should point to the relevant file. An example of how it is used is:

Alternatively, you may set these options via the environment variables, BACALHAU_TLS_CERT and BACALHAU_TLS_KEY. If you are using a configuration file, you can set the values inNode.ServerAPI.TLS.ServerCertificate and Node.ServerAPI.TLS.ServerKey instead.

Self-signed certificates

Once you have generated the necessary files, the steps are much like above, you must start bacalhau serve which two new flags. These are tlscert and tlskey flags, whose arguments should point to the relevant file. An example of how it is used is:

Alternatively, you may set these options via the environment variables, BACALHAU_TLS_CERT and BACALHAU_TLS_KEY. If you are using a configuration file, you can set the values inNode.ServerAPI.TLS.ServerCertificate and Node.ServerAPI.TLS.ServerKey instead.

If you use self-signed certificates, it is unlikely that any clients will be able to verify the certificate when connecting to the Bacalhau APIs. There are three options available to work around this problem:

1. Provide a CA certificate file of trusted certificate authorities, which many software libraries support in addition to system authorities.

2. Install the CA certificate file in the system keychain of each machine that needs access to the Bacalhau APIs.

3. Instruct the software library you are using not to verify HTTPS requests.

Both compute nodes, and requester nodes, maintain state. How that state is maintained is configurable, although the defaults are likely adequate for most use-cases. This page describes how to configure the persistence of compute and requester nodes should the defaults not be suitable.

Compute node persistence

The computes nodes maintain information about the work that has been allocated to them, including:

• The current state of the execution, and

• The original job that resulted in this allocation

This information is used by the compute and requester nodes to ensure allocated jobs are completed successfully. By default, compute nodes store their state in a bolt-db database and this is located in the bacalhau repository along with configuration data. For a compute node whose ID is "abc", the database can be found in ~/.bacalhau/abc-compute/executions.db.

In some cases, it may be preferable to maintain the state in memory, with the caveat that should the node restart, all state will be lost. This can be configured using the environment variables in the table below.

Requester node persistence

When running a requester node, it maintains state about the jobs it has been requested to orchestrate and schedule, the evaluation of those jobs, and the executions that have been allocated. By default, this state is stored in a bolt db database that, with a node ID of "xyz" can be found in ~/.bacalhau/xyz-requester/jobs.db.

Good news everyone! You can now run your Bacalhau-IPFS stack in Docker.

This page describes several ways in which to operate Bacalhau. You can choose the method that best suits your needs. The methods are:

Pre-Prerequisites

• This guide works best on a Linux machine. If you're trying to run this on a Mac, you may encounter issues. Remember that network host mode doesn't work.

• You need to have Docker installed. If you don't have it, you can .

Connect to the Public Bacalhau Network Using Docker

This method is appropriate for those who:

• Provide compute resources to the public Bacalhau network

This is not appropriate for:

• Testing and development

• Running a private network

Prerequisites

(Optional) Start a Public IPFS Node

This will start a local IPFS node and connect it to the public DHT. If you already have an IPFS node running, then you can skip this step.

Some notes about this command:

• It wipes the $(pwd)/ipfs directory to make sure you have a clean slate

• It runs the IPFS container in the specified Docker network

• It exposes the IPFS API port to the world on port 4002, to avoid clashes with Bacalhau

• It exposes the admin RPC API to the local host only, on port 5001

• We are not specifying or removing the bootstrap nodes, so it will default to connecting to public machines

Start a Public Bacalhau Node

Bacalhau consists of two parts: a "requester" that is responsible for operating the API and managing jobs, and a "compute" element that is responsible for executing jobs. In a public context, you'd typically just run a compute node, and allow the public requesters to handle the traffic.

Notes about the command:

• It runs the Bacalhau container in "host" mode. This means that the container will use the same network as the host.

• It uses the root user, which is the default system user that has access to the Docker socket on a Mac. You may need to change this to suit your environment.

• It mounts the Docker Socket

• It mounts the /tmp directory

• It exposes the Bacalhau API ports to the world

• The container version should match that of the current release

• The IPFS connect string points to the RPC port of the IPFS node in Docker. Because Bacalhau is running in the same network, it can use DNS to find the IPFS container IP. If you're running your own node, replace it

• The --node-type flag is set to compute because we only want to run a compute node

• The --labels flag is used to set a human-readable label for the node, and so we can run jobs on our machine later

• We specify the --peer env flag so that it uses the environment specified by BACALHAU_ENVIRONMENT=production and therefore connects to the public network peers

There are several ways to ensure that the Bacalhau compute node is connected to the network.

First, check that the Bacalhau libp2p port is open and connected. On Linux you can run lsof and it should look something like this:

Note the three established connections at the bottom. These are the production bootstrap nodes that Bacalhau is now connected to.

You can also check that the node is connected by listing the current network peers and grepping for your IP address or node ID. The node ID can be obtained from the Bacalhau logs. It will look something like this:

Finally, submit a job with the label you specified when you ran the compute node. If this label is unique, there should be only one node with this label. The job should succeed. Run the following:

If instead, your job fails with the following error, it means that the compute node is not connected to the network:

Run a Private Bacalhau Network Using Docker (Insecure)

This method is appropriate for:

• Testing and development

• Evaluating the Bacalhau platform before scaling jobs via the public network

This method is useful for testing and development. It's easier to use because it doesn't require a secret IPFS swarm key -- this is essentially an authentication token that allows you to connect to the node.

This method is not appropriate for:

• Secure, private use

• Production use

Prerequisites

Start a Local IPFS Node (Insecure)

To run an insecure, private node, you need to initialize your IPFS configuration by removing all of the default public bootstrap nodes. Then we run the node in the normal way, without the special LIBP2P_FORCE_PNET flag that checks for a secure private connection.

Some notes about this command:

• It wipes the $(pwd)/ipfs directory to make sure you have a clean slate

• It removes the default bootstrap nodes

• It runs the IPFS container in the specified Docker network

• It exposes the IPFS API port to the local host only, to prevent accidentally exposing the IPFS node, on 4002, to avoid clashes with Bacalhau

• It exposes the admin RPC API to the local host only, on port 5001

Start a Private Bacalhau Node

Bacalhau consists of two parts: a "requester" that is responsible for operating the API and managing jobs, and a "compute" element that is responsible for executing jobs. In a public context, you'd typically just run a compute node, and allow the public requesters to handle the traffic. But in a private context, you'll want to run both.

Notes about the command:

• It runs the Bacalhau container in the specified Docker network

• It uses the root user, which is the default system user that has access to the Docker socket on a Mac. You may need to change this to suit your environment

• It mounts the Docker Socket

• It mounts the /tmp directory and specifies this as the location where Bacalhau will write temporary execution data (BACALHAU_NODE_COMPUTESTORAGEPATH)

• It exposes the Bacalhau API ports to the local host only, to prevent accidentally exposing the API to the public internet

• The container version should match that of the Bacalhau installed on your system

• The IPFS connect string points to the RPC port of the IPFS node. Because Bacalhau is running in the same network, it can use DNS to find the IPFS container IP.

• The --node-type flag is set to requester,compute because we want to run both a requester and a compute node

Run a Job on the Private Network

Now it's time to run a job. Recall that you exposed the Bacalhau API on the default ports to the local host only. So you'll need to use the --api-host flag to tell Bacalhau where to find the API. Everything else is a standard part of the Bacalhau CLI.

The job should succeed. Run it again but this time capture the job ID to make it easier to retrieve the results.

Retrieve the Results on the Private Network (Insecure)

To retrieve the results using the Bacalhau CLI, you need to know the p2p swarm multiaddress of the IPFS node because you don't want to connect to the public global IPFS network. To do that you can run the IPFS id command (and parse to remove the trub at the bottom of the barrel):

Note that the command above changes the reported port from 4001 to 4002. This is because the IPFS node is running on port 4002, but the IPFS id command reports the port as 4001.

Now get the results:

Alternatively, you can use the Docker container, mount the results volume, and change the --api-host to the name of the Bacalhau container and the --ipfs-swarm-addrs back to port 4001:

Run a Private Bacalhau Network Using Docker (Secure)

Running a private secure network is useful in a range of scenarios, including:

• Running a private network for a private project

You need two things. A private IPFS node to store data and a Bacalhau node to execute over that data. To keep the nodes private you need to tell the nodes to shush and use a secret key. This is a bit harder to use, and a bit more involved than the insecure version.

Prerequisites

Start a Private IPFS Node (Secure)

First, you need to bootstrap a new IPFS cluster for your own private use. This consists of a process of generating a swarm key, removing any bootstrap nodes, and then starting the IPFS node.

Some notes about this command:

• It wipes the $(pwd)/ipfs directory to make sure you have a clean slate

• It generates a new swarm key -- this is the token that is required to connect to this node

• It removes the default bootstrap nodes

• It runs the IPFS container in the specified Docker network

• It exposes the IPFS API port to the local host only, to prevent accidentally exposing the IPFS node, on 4002, to avoid clashes with Bacalhau

• It exposes the admin RPC API to the local host only, on port 5001

Start a Private Bacalhau Node (Secure)

Run a Job on the Private Network (Secure)

Retrieve the Results on the Private Network (Secure)

The same process as above can be used to retrieve results from the IPFS node as long as the Bacalhau get command has access to the IPFS swarm key.

Running the Bacalhau binary from outside of Docker:

Alternatively, you can use the Docker container, mount the results volume, and change the --api-host to the name of the Bacalhau container and the --ipfs-swarm-addrs back to port 4001:

Common Prerequisites

Create a New Docker Network

Without this, inter-container DNS will not work, and internet access may not work either.

Test that the IPFS Node is Working

You can now browse the IPFS web UI at http://127.0.0.1:5001/webui.

Test that the Bacalhau Node is Working

Ensure that the Bacalhau logs (docker logs bacalhau) have no errors.

Check that your Bacalhau installation is the same version:

The versions should match. Alternatively, you can use the Docker container:

Perform a list command to ensure you can connect to the Bacalhau API.

It should return empty.

Authenticate with docker hub

Should you wish to authenticate with Docker Hub when pulling images, you can do so by specifying credentials as environment variables wherever your compute node is running.

These are the flags that control the capacity of the Bacalhau node, and the limits for jobs that might be run.

  --limit-job-cpu string                 Job CPU core limit for single job (e.g. 500m, 2, 8).
  --limit-job-gpu string                 Job GPU limit for single job (e.g. 1, 2, or 8).
  --limit-job-memory string              Job Memory limit for single job  (e.g. 500Mb, 2Gb, 8Gb).
  --limit-total-cpu string               Total CPU core limit to run all jobs (e.g. 500m, 2, 8).
  --limit-total-gpu string               Total GPU limit to run all jobs (e.g. 1, 2, or 8).
  --limit-total-memory string            Total Memory limit to run all jobs  (e.g. 500Mb, 2Gb, 8Gb).

The --limit-total-* flags control the total system resources you want to give to the network. If left blank, the system will attempt to detect these values automatically.

The --limit-job-* flags control the maximum amount of resources a single job can consume for it to be selected for execution.

Resource limits are not supported for Docker jobs running on Windows. Resource limits will be applied at the job bid stage based on reported job requirements but will be silently unenforced. Jobs will be able to access as many resources as requested at runtime.

Docker Workloads

Bacalhau executes jobs by running them within containers. Bacalhau employs a syntax closely resembling Docker, allowing you to utilize the same containers. The key distinction lies in how input and output data are transmitted to the container via IPFS, enabling scalability on a global level.

This section describes how to migrate a workload based on a Docker container into a format that will work with the Bacalhau client.

Requirements

Here are few things to note before getting started:

1. Container Registry: Ensure that the container is published to a public container registry that is accessible from the Bacalhau network.

2. Architecture Compatibility: Bacalhau supports only images that match the host node's architecture. Typically, most nodes run on linux/amd64, so containers in arm64 format are not able to run.

3. Input Flags: The --input ipfs://... flag supports only directories and does not support CID subpaths. The --input https://... flag supports only single files and does not support URL directories. The --input s3://... flag supports S3 keys and prefixes. For example, s3://bucket/logs-2023-04* includes all logs for April 2023.

Runtime Restrictions

To help provide a safe, secure network for all users, we add the following runtime restrictions:

1. Limited Ingress/Egress Networking:

All ingress/egress networking is limited as described in the networking documentation. You won't be able to pull data/code/weights/ etc. from an external source.

1. Data Passing with Docker Volumes:

A job includes the concept of input and output volumes, and the Docker executor implements support for these. This means you can specify your CIDs, URLs, and/or S3 objects as input paths and also write results to an output volume. This can be seen in the following example:

bacalhau docker run \
  -i s3://mybucket/logs-2023-04*:/input \
  -o apples:/output_folder \
  ubuntu \
  bash -c 'ls /input > /output_folder/file.txt'

The above example demonstrates an input volume flag -i s3://mybucket/logs-2023-04*, which mounts all S3 objects in bucket mybucket with logs-2023-04 prefix within the docker container at location /input (root).

Output volumes are mounted to the Docker container at the location specified. In the example above, any content written to /output_folder will be made available within the apples folder in the job results CID.

Once the job has run on the executor, the contents of stdout and stderr will be added to any named output volumes the job has used (in this case apples), and all those entities will be packaged into the results folder which is then published to a remote location by the publisher.

Onboarding Your Workload

Step 1 - Read Data From Your Directory

If you need to pass data into your container you will do this through a Docker volume. You'll need to modify your code to read from a local directory.

We make the assumption that you are reading from a directory called /inputs, which is set as the default.

Step 2 - Write Data to the Your Directory

If you need to return data from your container you will do this through a Docker volume. You'll need to modify your code to write to a local directory.

We make the assumption that you are writing to a directory called /outputs, which is set as the default.

Step 3 - Build and Push Your Image To a Registry

At this step, you create (or update) a Docker image that Bacalhau will use to perform your task. You build your image from your code and dependencies, then push it to a public registry so that Bacalhau can access it. This is necessary for other Bacalhau nodes to run your container and execute the given task.

For example:

$ export IMAGE=myuser/myimage:latest
$ docker build -t ${IMAGE} .
$ docker image push ${IMAGE}

Step 4 - Test Your Container

To test your docker image locally, you'll need to execute the following command, changing the environment variables as necessary:

$ export LOCAL_INPUT_DIR=$PWD
$ export LOCAL_OUTPUT_DIR=$PWD
$ export CMD=(sh -c 'ls /inputs; echo do something useful > /outputs/stdout')
$ docker run --rm \
  -v ${LOCAL_INPUT_DIR}:/inputs  \
  -v ${LOCAL_OUTPUT_DIR}:/outputs \
  ${IMAGE} \
  ${CMD}

Let's see what each command will be used for:

$ export LOCAL_INPUT_DIR=$PWD
Exports the current working directory of the host system to the LOCAL_INPUT_DIR variable. This variable will be used for binding a volume and transferring data into the container.

$ export LOCAL_OUTPUT_DIR=$PWD
Exports the current working directory of the host system to the LOCAL_OUTPUT_DIR variable. Similarly, this variable will be used for binding a volume and transferring data from the container.

$ export CMD=(sh -c 'ls /inputs; echo do something useful > /outputs/stdout')
Creates an array of commands CMD that will be executed inside the container. In this case, it is a simple command executing 'ls' in the /inputs directory and writing text to the /outputs/stdout file.

$ docker run ... ${IMAGE} ${CMD}
Launches a Docker container using the specified variables and commands. It binds volumes to facilitate data exchange between the host and the container.