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:

bacalhau node list --show=capacity

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.:

bacalhau docker run ubuntu echo Hello World --cpu=2 --memory=4Gb

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:

bacalhau docker run --gpu=1 nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi

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

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 job list --id-filter JOB_ID

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

bacalhau job describe JOB_ID

To download your job, run.

bacalhau job 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 job list --id-filter $JOB_ID
sleep 5

bacalhau job 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 job list

bacalhau job 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).

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?

How it works

Bacalhau consists of a network of nodes that enables orchestration between every compute resource, no matter whether it is a Cloud VM, an On-premise server or Edge devices. 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.

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.

Use Cases

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.

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 training and deployment with Bacalhau.

• 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.

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. WebAssembly programs can access data, environment variables, and program arguments through this interface.

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 say what data they need using URLs or CIDs (Content IDentifier) and then access the data by reading from the filesystem.

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

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

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

3. Verify installation by

4. nvidia-smi installed and functional

AMD

2. rocm-smi tool installed and functional

See the for guidance on how to run Docker workloads on AMD GPU.

Intel

2. xpu-smi tool installed and functional

GPU Node Configuration

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

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.

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:

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 job list command adding the --id-filter flag and specifying your job id.

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

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 job describe.

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

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 job get.

This results in

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

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

Step 4 - Viewing your Job Output

That should print out the string Hello World.

Step 5 - Where to go next?

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

Support

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.

Introduction

This tutorial describes how to add new nodes to an existing private network. Two basic scenarios will be covered:

1. Adding a machine as a new node.

2. Adding a as a new node. ​

Pre-Prerequisites

1. You should have an established private network consisting of at least one requester node. See the guide to set one up.

2. You should have a new host (physical/virtual machine, cloud instance or docker container) with installed.​

Add Host/Virtual Machine as a New Node

Let's assume that you already have a private network with at least one requester node. In this case, the process of adding new nodes follows the section. You will need to:

1. Set the token in the node.network.authsecret parameter

2. Execute bacalhau serve specifying the node type and orchestrator address via flags. You can find an example of such a command in the logs of the requester node, here is how it might look like:

Add a Cloud Instance as a New Node

To automate the process using Terraform follow these steps:

3. Determine the IP address of your requester node

4. Write a terraform script, which does the following:

   1. Adds a new instance

   2. Installs bacalhau on it

   3. Launches a compute node

5. Execute the script

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

You should use the Bacalhau client to send a task to the network. The client transmits the job information to the Bacalhau network via established protocols and interfaces. Jobs submitted via the Bacalhau CLI are forwarded to a Bacalhau network node at (http://bootstrap.production.bacalhau.org/) via port 1234 by default. This Bacalhau node will act as the requester node for the duration of the job lifecycle.

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

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.

Before you join the main Bacalhau network, you can test locally.

To test, you can use the bacalhau devstack command, which offers a way to get a 3 node cluster running locally.

export PREDICTABLE_API_PORT=1
bacalhau devstack

In another window, export the following environment variables so that the Bacalhau client binary connects to our local development cluster:

export BACALHAU_API_HOST=127.0.0.1
export BACALHAU_API_PORT=20000

You can now interact with Bacalhau - all jobs are running by the local devstack cluster.

bacalhau docker run ubuntu echo hello
bacalhau job list

Access Management

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:

curl -sL https://raw.githubusercontent.com/bacalhau-project/bacalhau/main/pkg/authz/policies/policy_ns_anon.rego -o ~/.bacalhau/no-anon.rego
bacalhau config set Auth.AccessPolicyPath ~/.bacalhau/no-anon.rego

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:

curl -sL https://raw.githubusercontent.com/bacalhau-project/bacalhau/main/pkg/authn/challenge/challenge_ns_no_anon.rego -o ~/.bacalhau/challenge_ns_no_anon.rego
bacalhau config set Auth.Methods '\{Method: ClientKey, Policy: \{Type: challenge, PolicyPath: ~/.bacalhau/challenge_ns_no_anon.rego\}\}'

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

bacalhau agent node | jq -rc .ClientID

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.

curl -sL https://raw.githubusercontent.com/bacalhau-project/bacalhau/main/pkg/authn/ask/ask_ns_password.rego -o ~/.bacalhau/ask_ns_password.rego
bacalhau config set Auth.Methods '\{Method: Password, Policy: \{Type: ask, PolicyPath: ~/.bacalhau/ask_ns_password.rego\}\}'

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:

cd pkg/authn/ask/gen_password && go run .

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.

Policies are written in a language called Rego, also used by Kubernetes. Users who want to write their own policies should get familiar with the Rego language.

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:

package bacalhau.authn

token := "anything"

A more realistic example that returns a signed JWT is in challenge_ns_anon.rego.

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:

package bacalhau.authn

schema := {}
token := "anything"

A more realistic example that returns a signed JWT is in ask_ns_example.rego.

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:

package bacalhau.authz

allow := true
token_valid := true

A more realistic example (which is the Bacalhau "anonymous mode" default) is in policy_ns_anon.rego.

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 Let's Encrypt 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 ACME HTTP-01 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.

sudo setcap CAP_NET_BIND_SERVICE+ep $(which bacalhau)

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:

bacalhau serve --node-type=requester --tlscert=server.cert --tlskey=server.key

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

If you wish, it is possible to use Bacalhau with a self-signed certificate which does not rely on an external Certificate Authority. This is an involved process and so is not described in detail here although there is a helpful script in the Bacalhau github repository which should provide a good starting point.

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:

bacalhau serve --node-type=requester --tlscert=server.cert --tlskey=server.key

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.

Bacalhau employs the viper and cobra 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:

$ tree ~/.bacalhau
├── QmdGUjsMHEgtAfdtw7U62yPEcAZFtA33tKMsczLToegZtv-compute/
│   ├── executions.db
│   └── jobStats.json
├── QmdGUjsMHEgtAfdtw7U62yPEcAZFtA33tKMsczLToegZtv-requester/
│   └── jobs.db
├── config.yaml
├── executor_storages/
├── libp2p_private_key
├── plugins/
├── repo.version
└── user_id.pem

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:

   • Stores the Bacalhau node's libp2p private key, essential for its network identity. The NodeID of a Bacalhau node is derived from this 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:

   • Contains the BoltDB executions.db database, which aids the Compute node in state persistence. Additionally, the jobStats.json file records the Compute Node's completed jobs tally.

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

2. QmdGUjsMHEgtAfdtw7U62yPEcAZFtA33tKMsczLToegZtv-requester:

   • Contains the BoltDB jobs.db database for the Requester node's state persistence.

   • 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:

Node:
    IPFS:
        Connect: value

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...

  ---

  Note: The above configurations provided for each environment are not exhaustive. Consult the specific environment documentation for a comprehensive list of configurations.

Usage Examples

How to initialize a Bacalhau Server for a local private network

$ env BACALHAU_ENVIRONMENT=local ./bin/darwin_arm64/bacalhau serve
INF pkg/repo/fs.go:187 > Initializing repo at '/Users/frrist/.bacalhau' for environment 'local'

How to initialize a Bacalhau Server with a custom repo path

$ bacalhau --repo=/path/to/repo serve
INF pkg/repo/fs.go:187 > Initializing repo at '/path/to/repo' for environment 'production'

Or

$ export BACALHAU_DIR=/path/to/repo
$ bacalhau serve
INF pkg/repo/fs.go:187 > Initializing repo at '/path/to/repo' for environment 'production'

How to start a Bacalhau Server with DEBUG logs

$ env LOG_LEVEL=debug ./bin/darwin_arm64/bacalhau serve
DBG pkg/system/environment.go:53 > Defaulting to production environment: os.Args: [./bin/darwin_arm64/bacalhau serve]

Resource Limits

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.​

Windows Support

Running a Windows-based node is not officially supported, so your mileage may vary. Some features (like resource limits) are not present in Windows-based nodes.

Bacalhau currently makes the assumption that all containers are Linux-based. Users of the Docker executor will need to manually ensure that their Docker engine is running and configured appropriately to support Linux containers, e.g. using the WSL-based backend.​

Timeouts

Bacalhau can limit the total time a job spends executing. A job that spends too long executing will be cancelled, and no results will be published.

By default, a Bacalhau node does not enforce any limit on job execution time. Both node operators and job submitters can supply a maximum execution time limit. If a job submitter asks for a longer execution time than permitted by a node operator, their job will be rejected.

Applying job timeouts allows node operators to more fairly distribute the work submitted to their nodes. It also protects users from transient errors that result in their jobs waiting indefinitely.​

Configuring Execution Time Limits

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 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:

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

For example:

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:

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

For example:

The result of the commands' execution is shown below:

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:

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:

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

To get more information on your job,run:

To download your job, run:

For example, running:

outputs:

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

Troubleshooting

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

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

Support

This documentation explains how to use the Bacalhau Docker image to run tasks and manage them using the Bacalhau client.

Prerequisites

To get started, you need to install the Bacalhau client (see more information ) and Docker.

1. Pull the Bacalhau Docker image

The first step is to pull the Bacalhau Docker image from the .

Expected output:

You can also pull a specific version of the image, e.g.:

2. Check version

To check the version of the Bacalhau client, run:

Expected Output:

3. Running a Bacalhau Job

In the example below, an Ubuntu-based job runs to print the message 'Hello from Docker Bacalhau':

Structure of the command

1. ghcr.io/bacalhau-project/bacalhau:latest : Name of the Bacalhau Docker image

2. --id-only: Output only the job id

3. --wait: Wait for the job to finish

4. ubuntu:latest. Ubuntu container

5. --: Separate Bacalhau parameters from the command to be executed inside the container

6. sh -c 'uname -a && echo "Hello from Docker Bacalhau!"': The command executed inside the container

Let's have a look at the command execution in the terminal:

The output you're seeing is in two parts: The first line: 13:53:46.478 | INF pkg/repo/fs.go:81 > Initializing repo at '/root/.bacalhau' for environment 'production' is an informational message indicating the initialization of a repository at the specified directory ('/root/.bacalhau') for the production environment. The second line: ab95a5cc-e6b7-40f1-957d-596b02251a66 is a job ID, which represents the result of executing a command inside a Docker container. It can be used to obtain additional information about the executed job or to access the job's results. We store that in an environment variable so that we can reuse it later on (env: JOB_ID=ab95a5cc-e6b7-40f1-957d-596b02251a66)

To print out the content of the Job ID, run the following command:

Expected Output:

4. Submit a Job With Output Files

One inconvenience that you'll see is that you'll need to mount directories into the container to access files. This is because the container is running in a separate environment from your host machine. Let's take a look at the example below:

The first part of the example should look familiar, except for the Docker commands.

When a job is submitted, Bacalhau prints out the related job_id (a46a9aa9-63ef-486a-a2f8-6457d7bafd2e):

5. Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau job list.

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

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

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in the result directory.

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

Support

Bacalhau operates by executing jobs within containers. This example shows you how to build and use a custom docker container.

Prerequisite

1. To get started, you need to install the Bacalhau client, see more information

2. This example requires Docker. If you don't have Docker installed, you can install it from . Docker commands will not work on hosted notebooks like Google Colab, but the Bacalhau commands will.

1. Running Containers

Docker Command

You're likely familiar with executing Docker commands to start a container:

This command runs a container from the docker/whalesay image. The container executes the cowsay sup old fashioned container run command:

Bacalhau Command

This command also runs a container from the docker/whalesay image, using Bacalhau. We use the bacalhau docker run command to start a job in a Docker container. It contains additional flags such as --wait to wait for job completion and --id-only to return only the job identifier. Inside the container, the bash -c 'cowsay hello web3 uber-run' command is executed.

When a job is submitted, Bacalhau prints out the related job_id (7e41b9b9-a9e2-4866-9fce-17020d8ec9e0):

We store that in an environment variable so that we can reuse it later on.

You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

Viewing your job output

Both commands execute cowsay in the docker/whalesay container, but Bacalhau provides additional features for working with jobs at scale.

Bacalhau Syntax

Bacalhau uses a syntax that is similar to Docker, and you can use the same containers. The main difference is that input and output data is passed to the container via IPFS, to enable planetary scale. In the example above, it doesn't make too much difference except that we need to download the stdout.

The --wait flag tells Bacalhau to wait for the job to finish before returning. This is useful in interactive sessions like this, but you would normally allow jobs to complete in the background and use the bacalhau job list command to check on their status.

Another difference is that by default Bacalhau overwrites the default entry point for the container, so you have to pass all shell commands as arguments to the run command after the -- flag.

2. Building Your Own Custom Container For Bacalhau

To use your own custom container, you must publish the container to a container registry that is accessible from the Bacalhau network. At this time, only public container registries are supported.

To demonstrate this, you will develop and build a simple custom container that comes from an old Docker example. I remember seeing cowsay at a Docker conference about a decade ago. I think it's about time we brought it back to life and distribute it across the Bacalhau network.

Next, the Dockerfile adds the script and sets the entry point.

Now let's build and test the container locally.

Once your container is working as expected then you should push it to a public container registry. In this example, I'm pushing to Github's container registry, but we'll skip the step below because you probably don't have permission. Remember that the Bacalhau nodes expect your container to have a linux/amd64 architecture.

3. Running Your Custom Container on Bacalhau

Now we're ready to submit a Bacalhau job using your custom container. This code runs a job, downloads the results, and prints the stdout.

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Download your job results directly by using bacalhau job get command.

View your job output

Support

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

Overview

The Bacalhau WebUI offers an intuitive interface for interacting with the Bacalhau network. This guide provides comprehensive instructions for setting up, deploying, and utilizing the WebUI.

For contributing to the WebUI's development, please refer to the .

Spinning Up the WebUI Locally

Prerequisites

• Ensure you have a Bacalhau v1.1.7 or later installed.

Running the WebUI

To launch the WebUI locally, execute the following command:

This command initializes a requester and compute node, configured to listen on HOST=0.0.0.0 and PORT=1234.

Accessing the Local WebUI

Once started, the WebUI is accessible at (http://127.0.0.1/). This local instance allows you to interact with your local Bacalhau network setup.

Accessing the WebUI from the Browser

N.b. The development version of the WebUI is for observation only and may not reflect the latest changes or features available in the local setup.

Introduction

In this tutorial example, we will walk you through building your own Python container and running the container on Bacalhau.

Prerequisites

To get started, you need to install the Bacalhau client, see more information

1. Sample Recommendation Dataset

We will be using a simple recommendation script that, when given a movie ID, recommends other movies based on user ratings. Assuming you want recommendations for the movie 'Toy Story' (1995), it will suggest movies from similar categories:

Downloading the dataset

In this example, we’ll be using 2 files from the MovieLens 1M dataset: ratings.dat and movies.dat. After the dataset is downloaded, extract the zip and place ratings.dat and movies.dat into a folder called input:

The structure of the input directory should be

Installing Dependencies

To create a requirements.txt for the Python libraries we’ll be using, create:

To install the dependencies, run:

Writing the Script

Create a new file called similar-movies.py and in it paste the following script

What the similar-movies.py script does

1. Read the files with pandas. The code uses Pandas to read data from the files ratings.dat and movies.dat.

2. Create the ratings matrix of shape (m×u) with rows as movies and columns as user

3. Normalise matrix (subtract mean off). The ratings matrix is normalized by subtracting the mean off.

4. Compute SVD: a singular value decomposition (SVD) of the normalized ratings matrix is performed.

5. Calculate cosine similarity, sort by most similar, and return the top N.

6. Select k principal components to represent the movies, a movie_id to find recommendations, and print the top_n results.

Running the Script

Running the script similar-movies.py using the default values:

You can also use other flags to set your own values.

2. Setting Up Docker

We will create a Dockerfile and add the desired configuration to the file. These commands specify how the image will be built, and what extra requirements will be included.

We will use the python:3.8 docker image and add our script similar-movies.py to copy the script to the docker image, similarly, we also add the dataset directory and also the requirements, after that run the command to install the dependencies in the image

The final folder structure will look like this:

Build the container

We will run docker build command to build the container:

Before running the command replace:

repo-name with the name of the container, you can name it anything you want

tag this is not required, but you can use the latest tag

In our case:

Push the container

Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.

In our case:

3. Running a Bacalhau Job

After the repo image has been pushed to Docker Hub, we can now use the container for running on Bacalhau. You can submit a Bacalhau job by running your container on Bacalhau with default or custom parameters.

Running the Container with Default Parameters

To submit a Bacalhau job by running your container on Bacalhau with default parameters, run the following Bacalhau command:

Structure of the command

1. bacalhau docker run: call to Bacalhau

2. jsace/python-similar-movies: the name and of the docker image we are using

3. -- python similar-movies.py: execute the Python script

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Running the Container with Custom Parameters

To submit a Bacalhau job by running your container on Bacalhau with custom parameters, run the following Bacalhau command:

Structure of the command

1. bacalhau docker run: call to Bacalhau

2. jsace/python-similar-movies: the name of the docker image we are using

3. -- python similar-movies.py --k 50 --id 10 --n 10: execute the python script. The script will use Singular Value Decomposition (SVD) and cosine similarity to find 10 movies most similar to the one with identifier 10, using 50 principal components.

4. Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau job list.

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

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

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

5. Viewing your Job Output

To view the file, run the following command:

Support

Introduction

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language. It is already well on its way towards this goal.

In this tutorial example, we will run Pandas script on Bacalhau.

Prerequisite

To get started, you need to install the Bacalhau client, see more information

1. Running Pandas Locally

To run the Pandas script on Bacalhau for analysis, first, we will place the Pandas script in a container and then run it at scale on Bacalhau.

To get started, you need to install the Pandas library from pip:

Importing data from CSV to DataFrame

Pandas is built around the idea of a DataFrame, a container for representing data. Below you will create a DataFrame by importing a CSV file. A CSV file is a text file with one record of data per line. The values within the record are separated using the “comma” character. Pandas provides a useful method, named read_csv() to read the contents of the CSV file into a DataFrame. For example, we can create a file named transactions.csv containing details of Transactions. The CSV file is stored in the same directory that contains the Python script.

The overall purpose of the command above is to read data from a CSV file (transactions.csv) using Pandas and print the resulting DataFrame.

To download the transactions.csv file, run:

To output a content of the transactions.csv file, run:

Running the script

Now let's run the script to read in the CSV file. The output will be a DataFrame object.

2. Ingesting Data

To run Pandas on Bacalhau you must store your assets in a location that Bacalhau has access to. We usually default to storing data on IPFS and code in a container, but you can also easily upload your script to IPFS too.

3. Running a Bacalhau Job

Now we're ready to run a Bacalhau job, whilst mounting the Pandas script and data from IPFS. We'll use the bacalhau docker run command to do this:

Structure of the command

1. bacalhau docker run: call to Bacalhau

2. amancevice/pandas : Docker image with pandas installed.

3. -i ipfs://QmfKJT13h5k1b23ja3ZCVg5nFL9oKz2bVXc8oXgtwiwhjz:/files: Mounting the uploaded dataset to path. The -i flag allows us to mount a file or directory from IPFS into the container. It takes two arguments, the first is the IPFS CID

4. QmfKJT13h5k1b23ja3ZCVg5nFL9oKz2bVXc8oXgtwiwhjz) and the second is the file path within IPFS (/files). The -i flag can be used multiple times to mount multiple directories.

   -w /files Our working directory is /files. This is the folder where we will save the model as it will automatically get uploaded to IPFS as outputs

5. python read_csv.py: python script to read pandas script

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

4. Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau job list.

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

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

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

5. Viewing your Job Output

To view the file, run the following command:

Support

Introduction

Support for the embedded node was in v1.4.0 to streamline communication and reduce overhead. Therefore, now in order to use a private IPFS network, it is necessary to create it yourself and then connect to it with nodes. This manual describes how to:

1. Install and configure IPFS

2. Create Private IPFS network

3. Configure your to use the private IPFS network

4. Pin your data to private IPFS network

TL;DR

1. Install on all nodes

2. Install

3. Initialize Private IPFS network

4. Connect all nodes to the same private network

5. Connect Bacalhau network to use private IPFS network

Download and Install