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

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.

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).

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:

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:

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.

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.

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.

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:

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:

You can choose to

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, you can run the following command.

To download your job, run.

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

This outputs the following.

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

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

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:

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.

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 server --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 server --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]

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:

• Docker

• Permission to access Docker

Nvidia

• Drivers for your GPU

• NVIDIA Container Toolkit (nvidia-docker2)

• nvidia-smi installed and functional

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

docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi

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:

docker run --rm --device=/dev/kfd --device=/dev/dri --entrypoint=rocm-smi rocm/rocm-terminal

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:

docker run --rm --device=/dev/dri --entrypoint=/bin/bash intel/xpumanager -- -c 'xpumd & sleep 5; xpumcli discovery'

GPU Node Configuration

Access to GPUs can be controlled using resource limits. To limit the number of GPUs that can be used per job, set a job resource limit. To limit access to GPUs from all jobs, set a total resource limit.

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

You can create jobs in the Bacalhau network using various job types introduced in version 1.2. Each job may need specific variables, resource requirements and data details that are described in the Job Specification.

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.

bacalhau create [flags]

Endpoint: `PUT /api/v1/orchestrator/jobs` 

You can use the bacalhau docker run command to start a job in a Docker container. Below, you can see an excerpt of the commands:

bacalhau docker run [flags] IMAGE[:TAG|@DIGEST] [COMMAND] [ARG...]

Flags:
    --concurrency int                  How many nodes should run the job (default 1)
    --cpu string                       Job CPU cores (e.g. 500m, 2, 8).
    --disk string                      Job Disk requirement (e.g. 500Gb, 2Tb, 8Tb).
    --do-not-track                     When true the job will not be tracked(?) TODO BETTER DEFINITION
    --domain stringArray               Domain(s) that the job needs to access (for HTTP networking)
    --download                         Should we download the results once the job is complete?
    --download-timeout-secs duration   Timeout duration for IPFS downloads. (default 5m0s)
    --dry-run                          Do not submit the job, but instead print out what will be submitted
    --entrypoint strings               Override the default ENTRYPOINT of the image
-e, --env strings                      The environment variables to supply to the job (e.g. --env FOO=bar --env BAR=baz)
-f, --follow                           When specified will follow the output from the job as it runs
-g, --gettimeout int                   Timeout for getting the results of a job in --wait (default 10)
    --gpu string                       Job GPU requirement (e.g. 1, 2, 8).
-h, --help                             help for run
    --id-only                          Print out only the Job ID on successful submission.
-i, --input storage                    Mount URIs as inputs to the job. Can be specified multiple times. Format: src=URI,dst=PATH[,opt=key=value]
                                    Examples:
                                    # Mount IPFS CID to /inputs directory
                                    -i ipfs://QmeZRGhe4PmjctYVSVHuEiA9oSXnqmYa4kQubSHgWbjv72
                                    # Mount S3 object to a specific path
                                    -i s3://bucket/key,dst=/my/input/path
                                    # Mount S3 object with specific endpoint and region
                                    -i src=s3://bucket/key,dst=/my/input/path,opt=endpoint=https://s3.example.com,opt=region=us-east-1
    --ipfs-connect string              The ipfs host multiaddress to connect to, otherwise an in-process IPFS node will be created if not set.
    --ipfs-serve-path string           path local Ipfs node will persist data to
    --ipfs-swarm-addrs strings         IPFS multiaddress to connect the in-process IPFS node to - cannot be used with --ipfs-connect. (default [/ip4/35.245.161.250/tcp/4001/p2p/12D3KooWAQpZzf3qiNxpwizXeArGjft98ZBoMNgVNNpoWtKAvtYH,/ip4/35.245.161.250/udp/4001/quic/p2p/12D3KooWAQpZzf3qiNxpwizXeArGjft98ZBoMNgVNNpoWtKAvtYH,/ip4/34.86.254.26/tcp/4001/p2p/12D3KooWLfFBjDo8dFe1Q4kSm8inKjPeHzmLBkQ1QAjTHocAUazK,/ip4/34.86.254.26/udp/4001/quic/p2p/12D3KooWLfFBjDo8dFe1Q4kSm8inKjPeHzmLBkQ1QAjTHocAUazK,/ip4/35.245.215.155/tcp/4001/p2p/12D3KooWH3rxmhLUrpzg81KAwUuXXuqeGt4qyWRniunb5ipjemFF,/ip4/35.245.215.155/udp/4001/quic/p2p/12D3KooWH3rxmhLUrpzg81KAwUuXXuqeGt4qyWRniunb5ipjemFF,/ip4/34.145.201.224/tcp/4001/p2p/12D3KooWBCBZnXnNbjxqqxu2oygPdLGseEbfMbFhrkDTRjUNnZYf,/ip4/34.145.201.224/udp/4001/quic/p2p/12D3KooWBCBZnXnNbjxqqxu2oygPdLGseEbfMbFhrkDTRjUNnZYf,/ip4/35.245.41.51/tcp/4001/p2p/12D3KooWJM8j97yoDTb7B9xV1WpBXakT4Zof3aMgFuSQQH56rCXa,/ip4/35.245.41.51/udp/4001/quic/p2p/12D3KooWJM8j97yoDTb7B9xV1WpBXakT4Zof3aMgFuSQQH56rCXa])
    --ipfs-swarm-key string            Optional IPFS swarm key required to connect to a private IPFS swarm
-l, --labels strings                   List of labels for the job. Enter multiple in the format '-l a -l 2'. All characters not matching /a-zA-Z0-9_:|-/ and all emojis will be stripped.
    --memory string                    Job Memory requirement (e.g. 500Mb, 2Gb, 8Gb).
    --network network-type             Networking capability required by the job. None, HTTP, or Full (default None)
    --node-details                     Print out details of all nodes (overridden by --id-only).
-o, --output strings                   name:path of the output data volumes. 'outputs:/outputs' is always added unless '/outputs' is mapped to a different name. (default [outputs:/outputs])
    --output-dir string                Directory to write the output to.
    --private-internal-ipfs            Whether the in-process IPFS node should auto-discover other nodes, including the public IPFS network - cannot be used with --ipfs-connect. Use "--private-internal-ipfs=false" to disable. To persist a local Ipfs node, set BACALHAU_SERVE_IPFS_PATH to a valid path. (default true)
-p, --publisher publisher              Where to publish the result of the job (default ipfs)
    --raw                              Download raw result CIDs instead of merging multiple CIDs into a single result
-s, --selector string                  Selector (label query) to filter nodes on which this job can be executed, supports '=', '==', and '!='.(e.g. -s key1=value1,key2=value2). Matching objects must satisfy all of the specified label constraints.
    --target all|any                   Whether to target the minimum number of matching nodes ("any") (default) or all matching nodes ("all") (default any)
    --timeout int                      Job execution timeout in seconds (e.g. 300 for 5 minutes)
    --wait                             Wait for the job to finish. Use --wait=false to return as soon as the job is submitted. (default true)
    --wait-timeout-secs int            When using --wait, how many seconds to wait for the job to complete before giving up. (default 600)
-w, --workdir string                   Working directory inside the container. Overrides the working directory shipped with the image (e.g. via WORKDIR in Dockerfile).

You can also use the bacalhau wasm run command to run a job compiled into the (WASM) format. Below, you can find an excerpt of the commands in the Bacalhau CLI:

bacalhau wasm run {cid-of-wasm | } [--entry-point ] [wasm-args ...] [flags]

Flags:
    --concurrency int                  How many nodes should run the job (default 1)
    --cpu string                       Job CPU cores (e.g. 500m, 2, 8).
    --disk string                      Job Disk requirement (e.g. 500Gb, 2Tb, 8Tb).
    --do-not-track                     When true the job will not be tracked(?) TODO BETTER DEFINITION
    --domain stringArray               Domain(s) that the job needs to access (for HTTP networking)
    --download                         Should we download the results once the job is complete?
    --download-timeout-secs duration   Timeout duration for IPFS downloads. (default 5m0s)
    --dry-run                          Do not submit the job, but instead print out what will be submitted
    --entry-point string               The name of the WASM function in the entry module to call. This should be a zero-parameter zero-result function that
                                will execute the job. (default "_start")
-e, --env strings                      The environment variables to supply to the job (e.g. --env FOO=bar --env BAR=baz)
-f, --follow                           When specified will follow the output from the job as it runs
-g, --gettimeout int                   Timeout for getting the results of a job in --wait (default 10)
    --gpu string                       Job GPU requirement (e.g. 1, 2, 8).
-h, --help                             help for run
    --id-only                          Print out only the Job ID on successful submission.
-U, --import-module-urls url           URL of the WASM modules to import from a URL source. URL accept any valid URL supported by the 'wget' command, and supports both HTTP and HTTPS.
-I, --import-module-volumes cid:path   CID:path of the WASM modules to import from IPFS, if you need to set the path of the mounted data.
-i, --input storage                    Mount URIs as inputs to the job. Can be specified multiple times. Format: src=URI,dst=PATH[,opt=key=value]
                                        Examples:
                                        # Mount IPFS CID to /inputs directory
                                        -i ipfs://QmeZRGhe4PmjctYVSVHuEiA9oSXnqmYa4kQubSHgWbjv72
                                        # Mount S3 object to a specific path
                                        -i s3://bucket/key,dst=/my/input/path
                                        # Mount S3 object with specific endpoint and region
                                        -i src=s3://bucket/key,dst=/my/input/path,opt=endpoint=https://s3.example.com,opt=region=us-east-1
    --ipfs-connect string              The ipfs host multiaddress to connect to, otherwise an in-process IPFS node will be created if not set.
    --ipfs-serve-path string           path local Ipfs node will persist data to
    --ipfs-swarm-addrs strings         IPFS multiaddress to connect the in-process IPFS node to - cannot be used with --ipfs-connect. (default [/ip4/35.245.161.250/tcp/4001/p2p/12D3KooWAQpZzf3qiNxpwizXeArGjft98ZBoMNgVNNpoWtKAvtYH,/ip4/35.245.161.250/udp/4001/quic/p2p/12D3KooWAQpZzf3qiNxpwizXeArGjft98ZBoMNgVNNpoWtKAvtYH,/ip4/34.86.254.26/tcp/4001/p2p/12D3KooWLfFBjDo8dFe1Q4kSm8inKjPeHzmLBkQ1QAjTHocAUazK,/ip4/34.86.254.26/udp/4001/quic/p2p/12D3KooWLfFBjDo8dFe1Q4kSm8inKjPeHzmLBkQ1QAjTHocAUazK,/ip4/35.245.215.155/tcp/4001/p2p/12D3KooWH3rxmhLUrpzg81KAwUuXXuqeGt4qyWRniunb5ipjemFF,/ip4/35.245.215.155/udp/4001/quic/p2p/12D3KooWH3rxmhLUrpzg81KAwUuXXuqeGt4qyWRniunb5ipjemFF,/ip4/34.145.201.224/tcp/4001/p2p/12D3KooWBCBZnXnNbjxqqxu2oygPdLGseEbfMbFhrkDTRjUNnZYf,/ip4/34.145.201.224/udp/4001/quic/p2p/12D3KooWBCBZnXnNbjxqqxu2oygPdLGseEbfMbFhrkDTRjUNnZYf,/ip4/35.245.41.51/tcp/4001/p2p/12D3KooWJM8j97yoDTb7B9xV1WpBXakT4Zof3aMgFuSQQH56rCXa,/ip4/35.245.41.51/udp/4001/quic/p2p/12D3KooWJM8j97yoDTb7B9xV1WpBXakT4Zof3aMgFuSQQH56rCXa])
    --ipfs-swarm-key string            Optional IPFS swarm key required to connect to a private IPFS swarm
-l, --labels strings                   List of labels for the job. Enter multiple in the format '-l a -l 2'. All characters not matching /a-zA-Z0-9_:|-/ and all emojis will be stripped.
    --memory string                    Job Memory requirement (e.g. 500Mb, 2Gb, 8Gb).
    --network network-type             Networking capability required by the job. None, HTTP, or Full (default None)
    --node-details                     Print out details of all nodes (overridden by --id-only).
-o, --output strings                   name:path of the output data volumes. 'outputs:/outputs' is always added unless '/outputs' is mapped to a different name. (default [outputs:/outputs])
    --output-dir string                Directory to write the output to.
    --private-internal-ipfs            Whether the in-process IPFS node should auto-discover other nodes, including the public IPFS network - cannot be used with --ipfs-connect. Use "--private-internal-ipfs=false" to disable. To persist a local Ipfs node, set BACALHAU_SERVE_IPFS_PATH to a valid path. (default true)
-p, --publisher publisher              Where to publish the result of the job (default ipfs)
    --raw                              Download raw result CIDs instead of merging multiple CIDs into a single result
-s, --selector string                  Selector (label query) to filter nodes on which this job can be executed, supports '=', '==', and '!='.(e.g. -s key1=value1,key2=value2). Matching objects must satisfy all of the specified label constraints.
    --target all|any                   Whether to target the minimum number of matching nodes ("any") (default) or all matching nodes ("all") (default any)
    --timeout int                      Job execution timeout in seconds (e.g. 300 for 5 minutes)
    --wait                             Wait for the job to finish. Use --wait=false to return as soon as the job is submitted. (default true)
    --wait-timeout-secs int            When using --wait, how many seconds to wait for the job to complete before giving up. (default 600)

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

The selected compute node receives the job and starts its execution inside a container. The container can use different executors to work with the data and perform the necessary actions. A job can use the docker executor, WASM executor or a library storage volumes. Use Docker Engine Specification to view the parameters to configure the Docker Engine. If you want tasks to be executed in a WebAssembly environment, pay attention to WebAssembly Engine Specification.

Results publishing

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

Bacalhau's seamless integration with IPFS ensures that users have a decentralized option for publishing their task results, enhancing accessibility and resilience while reducing dependence on a single point of failure. View IPFS Publisher Specification to get the detailed information.

Bacalhau's S3 Publisher provides users with a secure and efficient method to publish task results to any S3-compatible storage service. This publisher supports not just AWS S3, but other S3-compatible services offered by cloud providers like Google Cloud Storage and Azure Blob Storage, as well as open-source options like MinIO. View S3 Publisher Specification to get the detailed information.

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.

bacalhau get [id] [flags]

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

Usage:
  bacalhau get [id] [flags]

Flags:
      --download-timeout-secs duration   Timeout duration for IPFS downloads. (default 5m0s)
  -h, --help                             help for get
      --ipfs-connect string              The ipfs host multiaddress to connect to, otherwise an in-process IPFS node will be created if not set.
      --ipfs-serve-path string           path local Ipfs node will persist data to
      --ipfs-swarm-addrs strings         IPFS multiaddress to connect the in-process IPFS node to - cannot be used with --ipfs-connect. (default [/ip4/35.245.161.250/tcp/4001/p2p/12D3KooWAQpZzf3qiNxpwizXeArGjft98ZBoMNgVNNpoWtKAvtYH,/ip4/35.245.161.250/udp/4001/quic/p2p/12D3KooWAQpZzf3qiNxpwizXeArGjft98ZBoMNgVNNpoWtKAvtYH,/ip4/34.86.254.26/tcp/4001/p2p/12D3KooWLfFBjDo8dFe1Q4kSm8inKjPeHzmLBkQ1QAjTHocAUazK,/ip4/34.86.254.26/udp/4001/quic/p2p/12D3KooWLfFBjDo8dFe1Q4kSm8inKjPeHzmLBkQ1QAjTHocAUazK,/ip4/35.245.215.155/tcp/4001/p2p/12D3KooWH3rxmhLUrpzg81KAwUuXXuqeGt4qyWRniunb5ipjemFF,/ip4/35.245.215.155/udp/4001/quic/p2p/12D3KooWH3rxmhLUrpzg81KAwUuXXuqeGt4qyWRniunb5ipjemFF,/ip4/34.145.201.224/tcp/4001/p2p/12D3KooWBCBZnXnNbjxqqxu2oygPdLGseEbfMbFhrkDTRjUNnZYf,/ip4/34.145.201.224/udp/4001/quic/p2p/12D3KooWBCBZnXnNbjxqqxu2oygPdLGseEbfMbFhrkDTRjUNnZYf,/ip4/35.245.41.51/tcp/4001/p2p/12D3KooWJM8j97yoDTb7B9xV1WpBXakT4Zof3aMgFuSQQH56rCXa,/ip4/35.245.41.51/udp/4001/quic/p2p/12D3KooWJM8j97yoDTb7B9xV1WpBXakT4Zof3aMgFuSQQH56rCXa])
      --ipfs-swarm-key string            Optional IPFS swarm key required to connect to a private IPFS swarm
      --output-dir string                Directory to write the output to.
      --private-internal-ipfs            Whether the in-process IPFS node should auto-discover other nodes, including the public IPFS network - cannot be used with --ipfs-connect. Use "--private-internal-ipfs=false" to disable. To persist a local Ipfs node, set BACALHAU_SERVE_IPFS_PATH to a valid path. (default true)
      --raw                              Download raw result CIDs instead of merging multiple CIDs into a single result

Describe a Job

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

bacalhau describe [id] [flags]

Endpoint: `GET /api/v1/orchestrator/jobs/:jobID`

List of Jobs

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

bacalhau list [flags]

Endpoint: `GET /api/v1/orchestrator/jobs`

Job Executions

To list executions follow the following commands.

bacalhau job executions [id] [flags]

Endpoint: `GET /api/v1/orchestrator/jobs/:jobID/executions`

Chapter 4 - Monitoring and Management

The Bacalhau client provides the user with tools to monitor and manage the execution of jobs. You can get information about status, progress and decide on next steps. View the Bacalhau Agent APIs if you want to know the node's health, capabilities, and deployed Bacalhau version. To get information about the status and characteristics of the nodes in the cluster use Nodes API Documentation.

Stop a Job

bacalhau cancel [id] [flags]

Endpoint: `DELETE /api/v1/orchestrator/jobs/:jobID`

Job History

bacalhau job history [id] [flags]

Endpoint: `GET /api/v1/orchestrator/jobs/:jobID/history`

Job Logs

bacalhau logs [flags] [id]

You can use this command to retrieve the log output (stdout, and stderr) from a job. If the job is still running it is possible to follow the logs after the previously generated logs are retrieved.

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:

{
  "node_id": "XXX",
  "job_id": "XXX",
  "spec": {
    "engine": "docker",
    "verifier": "ipfs",
    "job_spec_vm": {
      "image": "ubuntu:latest",
      "entrypoint": ["cat", "/file.txt"]
    },
    "inputs": [{
      "engine": "ipfs",
      "cid": "XXX",
      "path": "/file.txt"
    }]
  }
}

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).

If the HTTP response is a JSON blob, it should match the following schema and will be used to respond to the bid directly:

{
  "$schema": "http://json-schema.org/draft-04/schema#",
  "type": "object",
  "properties": {
    "shouldBid": {
      "description": "If the job should be accepted",
      "type": "boolean"
    },
    "shouldWait": {
      "description": "If the node should wait for an async response that will come later. `shouldBid` will be ignored",
      "type": "boolean",
      "default": false,
    },
    "reason": {
      "description": "Human-readable string explaining why the job should be accepted or rejected, or why the wait is required",
      "type": "string"
    }
  },
  "required": [
    "shouldBid",
    "reason"
  ]
}

For example, the following response will reject the job:

{
  "shouldBid": false,
  "reason": "The job did not pass this specific validation: ...",
}

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

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.

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.

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.

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.

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

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

Bacalhau is a peer-to-peer network of computing providers that will run jobs submitted by users. A Compute Provider (CP) is anyone who is running a Bacalhau compute node participating in the Bacalhau compute network, regardless of whether they are hosting any Filecoin data.

This section will show you how to configure and run a Bacalhau node and start accepting and running jobs.

Quick start (Ubuntu 22.04)

Estimated time for completion: 10 min. Tested on: Ubuntu 22.04 LTS (x86/64) running on a GCP e2-standard-4 (4 vCPU, 16 GB memory) instance with 40 GB disk size.

Prerequisites

• Docker Engine - to take on Docker workloads

• Connection to storage provider - for storing job's results

• Firewall - to ensure your node can communicate with the rest of the network

• Physical hardware, Virtual Machine, or cloud-based host. A Bacalhau compute node is not intended to be run from within a Docker container.

Install Docker

To run docker-based workloads, you should have docker installed and running.

If you already have it installed and want to configure the connection to Docker with the following environment variables:

• DOCKER_HOST to set the URL to the docker server.

• DOCKER_API_VERSION to set the version of the API to reach, leave empty for "latest".

• DOCKER_CERT_PATH to load the TLS certificates from.

• DOCKER_TLS_VERIFY to enable or disable TLS verification, off by default.

Ensure your Storage Server is Running

We will need to connect our Bacalhau node to a storage server for this we will be using IPFS server so we can run jobs that consume CIDs as inputs.

You can either install and run it locally or you can connect to a remote IPFS server.

Using the command below:

Install:

Configure:

Now launch the IPFS daemon in a separate terminal (make sure to export the IPFS_PATH environment variable there as well):

Use the following command to print out a number of addresses.

I pick the record that combines 127.0.0.1 and tcp but I replace port 4001 with 5001:

Configure firewall

To ensure that our node can communicate with other nodes on the network - we need to make sure the 1235 port is open.

(Optional) To ensure the CLI can communicate with our node directly (i.e. bacalhau --api-host <MY_NODE_PUBLIC_IP> version) - we need to make sure the 1234 port is open.

Install the Bacalhau Binary

Run bacalhau

Now we can run our bacalhau node:

Alternatively, you can run the following Docker command:

Check your node works

Even though the CLI (by default) submits jobs, each node listens for events on the global network and possibly bids for taking a job: your logs should therefore show the activity of your node bidding for incoming jobs.

To quickly check your node runs properly, let's submit the following dummy job:

If you see logs of your compute node bidding for the job above it means you've successfully joined Bacalhau as a Compute Provider!

What's next?

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.

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

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.

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 results in their jobs waiting indefinitely.

Configuring execution time limits for a job

Job submitters can pass the --timeout flag to any Bacalhau job submission CLI to set a maximum job execution time. The supplied value should be a whole number of seconds with no unit.

The timeout can also be added to an existing job spec by adding the Timeout property to the Spec.

Configuring execution time limits for a node

Node operators can pass the --max-job-execution-timeout flag to bacalhau serve to configure the maximum job time limit. The supplied value should be a numeric value followed by a time unit (one of s for seconds, m for minutes or h for hours).

Node operators can also use configuration properties to configure execution limits.

Compute nodes will use the properties:

Requester nodes will use the properties:

Bacalhau has two ways to make use of external storage providers:

• Inputs storage resources consumed as inputs to jobs

• Publishers storage resources created with the results of jobs

Inputs

IPFS

To start, you'll need to connect the Bacalhau node to an IPFS server so that you can run jobs that consume CIDs as inputs.

You can then configure your Bacalhau node to use this IPFS server by passing the --ipfs-connect argument to the serve command:

Or, set the Node.IPFS.Connect property in the Bacalhau configuration file.

Publishers

IPFS

The IPFS publisher works using the same setup as above - you'll need to have an IPFS server running and a multiaddress for it. You'll then you pass that multiaddress using the --ipfs-connect argument to the serve command.

If you are publishing to a public IPFS node, you can use bacalhau get with no further arguments to download the results. However, you may experience a delay in results becoming available as indexing of new data by public nodes takes time.

To speed up the download or to retrieve results from a private IPFS node, pass the swarm multiaddress to bacalhau get to download results.

Pass the swarm key to bacalhau get if the IPFS swarm is a private swarm.

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

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.

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.

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.

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:

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,run:

$ bacalhau describe JOB_ID

To download your job, run:

$ bacalhau get JOB_ID

For example, running:

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

outputs:

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)

Bacalhau supports running programs that are compiled to WebAssembly (WASM). 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.

For example, Rust users can specify the wasm32-wasi target to rustup and cargo to get programs compiled for WASI WebAssembly. See the Rust example for more information on 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:

Copy data from a URL to public storage Pin Data to public storage Copy Data from S3 Bucket to public storage

Step 4: Run your program

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

$ bacalhau wasm run

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:

$ bacalhau wasm run main.wasm

Alternative Program Specification:

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

$ bacalhau wasm run Qmajb9T3jBdMSp7xh2JruNrqg3hniCnM6EUVsBocARPJRQ

Input Data Specification:

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

$ bacalhau wasm run --input http://example.com

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:

$ bacalhau wasm run echo.wasm hello world

For a specific WebAssembly program, run:

$ bacalhau wasm run Qmajb9T3jBdMSp7xh2JruNrqg3hniCnM6EUVsBocARPJRQ hello world

$ bacalhau wasm run prog.wasm /inputs/data.txt

Environment variables

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

$ bacalhau wasm run prog.wasm -e HELLO=world

Examples

See the Rust example for a workload that leverages WebAssembly support.

Support

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

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 here

2. This example requires Docker. If you don't have Docker installed, you can install it from here. 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:

%%bash
docker run docker/whalesay cowsay sup old fashioned container run

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

Expected output:
_________________________________
< sup old fashioned container run >
 ---------------------------------
    \
     \
      \
                    ##        .
              ## ## ##       ==
           ## ## ## ##      ===
       /""""""""""""""""___/ ===
  ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ /  ===- ~~~
       \______ o          __/
        \    \        __/
          \____\______/

Bacalhau Command

%%bash --out job_id
bacalhau docker run --wait --id-only docker/whalesay -- bash -c 'cowsay hello web3 uber-run'

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):

7e41b9b9-a9e2-4866-9fce-17020d8ec9e0

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

%env JOB_ID={job_id}

You can download your job results directly by using bacalhau 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.

%%bash
rm -rf results && mkdir -p results
bacalhau get ${JOB_ID}  --output-dir results

Viewing your job output

%%bash
cat ./results/stdout

Expected output:

 _____________________
< hello web3 uber-run >
 ---------------------
    \
     \
      \
                    ##        .
              ## ## ##       ==
           ## ## ## ##      ===
       /""""""""""""""""___/ ===
  ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ /  ===- ~~~
       \______ o          __/
        \    \        __/
          \____\______/

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 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.

%%writefile cod.cow
$the_cow = <<"EOC";
   $thoughts
    $thoughts
                               ,,,,_
                            ┌Φ▓╬▓╬▓▓▓W      @▓▓▒,
                           ╠▓╬▓╬╣╬╬▓╬▓▓   ╔╣╬╬▓╬╣▓,
                    __,┌╓═╠╬╠╬╬╬Ñ╬╬╬Ñ╬╬¼,╣╬╬▓╬╬▓╬▓▓▓┐        ╔W_             ,φ▓▓
               ,«@▒╠╠╠╠╩╚╙╙╩Ü╚╚╚╚╩╙╙╚╠╩╚╚╟▓▒╠╠╫╣╬╬╫╬╣▓,   _φ╬▓╬╬▓,        ,φ╣▓▓╬╬
          _,φÆ╩╬╩╙╚╩░╙╙░░╩`=░╙╚»»╦░=╓╙Ü1R░│░╚Ü░╙╙╚╠╠╠╣╣╬≡Φ╬▀╬╣╬╬▓▓▓_   ╓▄▓▓▓▓▓▓╬▌
      _,φ╬Ñ╩▌▐█[▒░░░░R░░▀░`,_`!R`````╙`-'╚Ü░░Ü░░░░░░░│││░╚╚╙╚╩╩╩╣Ñ╩╠▒▒╩╩▀▓▓╣▓▓╬╠▌
     '╚╩Ü╙│░░╙Ö▒Ü░░░H░░R ▒¥╣╣@@@▓▓▓  := '`   `░``````````````````````````]▓▓▓╬╬╠H
       '¬═▄ `\░╙Ü░╠DjK` Å»»╙╣▓▓▓▓╬Ñ     -»`       -`      `  ,;╓▄╔╗∞  ~▓▓▓▀▓▓╬╬╬▌
             '^^^`   _╒Γ   `╙▀▓▓╨                     _, ⁿD╣▓╬╣▓╬▓╜      ╙╬▓▓╬╬▓▓
                 ```└                           _╓▄@▓▓▓╜   `╝╬▓▓╙           ²╣╬▓▓
                        %φ▄╓_             ~#▓╠▓▒╬▓╬▓▓^        `                ╙╙
                         `╣▓▓▓              ╠╬▓╬▓╬▀`
                           ╚▓▌               '╨▀╜
EOC

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

%%writefile Dockerfile
FROM debian:stretch
RUN apt-get update && apt-get install -y cowsay
# "cowsay" installs to /usr/games
ENV PATH $PATH:/usr/games
RUN echo '#!/bin/bash\ncowsay "${@:1}"' > /usr/bin/codsay && \
    chmod +x /usr/bin/codsay
COPY cod.cow /usr/share/cowsay/cows/default.cow

Now let's build and test the container locally.

%%bash
docker build -t ghcr.io/bacalhau-project/examples/codsay:latest . 2> /dev/null

%%bash
docker run --rm ghcr.io/bacalhau-project/examples/codsay:latest codsay I like swimming in data

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.

%%bash
# docker buildx build --platform linux/amd64,linux/arm64 --push -t ghcr.io/bacalhau-project/examples/codsay:latest .

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.

:::tip The bacalhau docker run command strips the default entry point, so don't forget to run your entry point in the command line arguments. :::

%%bash --out job_id
bacalhau docker run \
    --wait \
    --id-only \
    ghcr.io/bacalhau-project/examples/codsay:v1.0.0 \
    -- bash -c 'codsay Look at all this data'

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 get command.

%%bash
rm -rf results && mkdir -p results
bacalhau get ${JOB_ID}  --output-dir results

View your job output

%%bash
cat ./results/stdout

Expected output:

_______________________
< Look at all this data >
 -----------------------
   \
    \
                               ,,,,_
                            ┌Φ▓╬▓╬▓▓▓W      @▓▓▒,
                           ╠▓╬▓╬╣╬╬▓╬▓▓   ╔╣╬╬▓╬╣▓,
                    __,┌╓═╠╬╠╬╬╬Ñ╬╬╬Ñ╬╬¼,╣╬╬▓╬╬▓╬▓▓▓┐        ╔W_             ,φ▓▓
               ,«@▒╠╠╠╠╩╚╙╙╩Ü╚╚╚╚╩╙╙╚╠╩╚╚╟▓▒╠╠╫╣╬╬╫╬╣▓,   _φ╬▓╬╬▓,        ,φ╣▓▓╬╬
          _,φÆ╩╬╩╙╚╩░╙╙░░╩`=░╙╚»»╦░=╓╙Ü1R░│░╚Ü░╙╙╚╠╠╠╣╣╬≡Φ╬▀╬╣╬╬▓▓▓_   ╓▄▓▓▓▓▓▓╬▌
      _,φ╬Ñ╩▌▐█[▒░░░░R░░▀░`,_`!R`````╙`-'╚Ü░░Ü░░░░░░░│││░╚╚╙╚╩╩╩╣Ñ╩╠▒▒╩╩▀▓▓╣▓▓╬╠▌
     '╚╩Ü╙│░░╙Ö▒Ü░░░H░░R ▒¥╣╣@@@▓▓▓  := '`   `░``````````````````````````]▓▓▓╬╬╠H
       '¬═▄ `░╙Ü░╠DjK` Å»»╙╣▓▓▓▓╬Ñ     -»`       -`      `  ,;╓▄╔╗∞  ~▓▓▓▀▓▓╬╬╬▌
             '^^^`   _╒Γ   `╙▀▓▓╨                     _, ⁿD╣▓╬╣▓╬▓╜      ╙╬▓▓╬╬▓▓
                 ```└                           _╓▄@▓▓▓╜   `╝╬▓▓╙           ²╣╬▓▓
                        %φ▄╓_             ~#▓╠▓▒╬▓╬▓▓^        `                ╙╙
                         `╣▓▓▓              ╠╬▓╬▓╬▀`
                           ╚▓▌               '╨▀╜

Support

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

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 here) and Docker.

1. Pull the Bacalhau Docker image

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

docker pull ghcr.io/bacalhau-project/bacalhau:latest

Expected output:
latest: Pulling from bacalhau-project/bacalhau
d14ccdd25413: Pull complete
621f190d05c8: Pull complete
Digest: sha256:3cda5619984de9b56c738c50f94188684170f54f7e417f8dcbe74ff8ec8eb434
Status: Downloaded newer image for ghcr.io/bacalhau-project/bacalhau:latest
ghcr.io/bacalhau-project/bacalhau:latest

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

docker pull ghcr.io/bacalhau-project/bacalhau:v0.3.16

:::warning Remember that the "latest" tag is just a string. It doesn't refer to the latest version of the Bacalhau client, it refers to an image that has the "latest" tag. Therefore, if your machine has already downloaded the "latest" image, it won't download it again. To force a download, you can use the --no-cache flag. :::

2. Check version

To check the version of the Bacalhau client, run:

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

Expected Output:
13:38:54.518 | INF pkg/repo/fs.go:81 > Initializing repo at '/root/.bacalhau' for environment 'production'
CLIENT  SERVER  UPDATE MESSAGE
v1.2.0  v1.2.0

3. Running a Bacalhau Job

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

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!"'

Structure of the command

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

--id-only: Output only the job id

--wait: Wait for the job to finish

ubuntu:latest. Ubuntu container

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

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:

13:53:46.478 | INF pkg/repo/fs.go:81 > Initializing repo at '/root/.bacalhau' for environment 'production'
ab95a5cc-e6b7-40f1-957d-596b02251a66

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:

docker run -t ghcr.io/bacalhau-project/bacalhau:latest \
    describe ab95a5cc-e6b7-40f1-957d-596b02251a66 \
        | grep -A 2 "stdout: |"

Expected Output:
stdout: |
        Linux fff680719453 6.2.0-1019-gcp #21~22.04.1-Ubuntu SMP Thu Nov 16 18:18:34 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
        Hello from Docker Bacalhau!

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.

docker run -t ghcr.io/bacalhau-project/bacalhau:latest \
    docker run \
        --id-only \
        --wait \
        --gpu 1 \
        ghcr.io/bacalhau-project/examples/stable-diffusion-gpu:0.0.1 -- \
            python main.py --o ./outputs --p "A Docker whale and a cod having a conversation about the state of the ocean"

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

09:05:58.434 | INF pkg/repo/fs.go:81 > Initializing repo at '/root/.bacalhau' for environment 'production'
a46a9aa9-63ef-486a-a2f8-6457d7bafd2e

5. Checking the State of your Jobs

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

%%bash
docker run -t ghcr.io/bacalhau-project/bacalhau:latest \
    list $JOB_ID \

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 describe.

%%bash
docker run -t ghcr.io/bacalhau-project/bacalhau:latest \
    describe $JOB_ID \

Job download: You can download your job results directly by using bacalhau 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.

%%bash
bacalhau get ${JOB_ID} --output-dir result

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

Support

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

Introduction

Jupyter Notebooks have become an essential tool for data scientists, researchers, and developers for interactive computing and the development of data-driven projects. They provide an efficient way to share code, equations, visualizations, and narrative text with support for multiple programming languages. In this tutorial, we will introduce you to running Jupyter Notebooks on Bacalhau, a powerful and flexible container orchestration platform. By leveraging Bacalhau, you can execute Jupyter Notebooks in a scalable and efficient manner using Docker containers, without the need for manual setup or configuration.

In the following sections, we will explore two examples of executing Jupyter Notebooks on Bacalhau:

1. Executing a Simple Hello World Notebook: We will begin with a basic example to familiarize you with the process of running a Jupyter Notebook on Bacalhau. We will execute a simple "Hello, World!" notebook to demonstrate the steps required for running a notebook in a containerized environment.

2. Notebook to Train an MNIST Model: In this section, we will dive into a more advanced example. We will execute a Jupyter Notebook that trains a machine-learning model on the popular MNIST dataset. This will showcase the potential of Bacalhau to handle more complex tasks while providing you with insights into utilizing containerized environments for your data science projects.

Prerequisite

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

1. Executing a Simple Hello World Notebook

There are no external dependencies that we need to install. All dependencies are already there in the container.

%%bash --out job_id
bacalhau docker run \
    --wait \
    --id-only \
    --timeout 3600 \
    --wait-timeout-secs 3600 \
    -w /inputs \
    -i https://raw.githubusercontent.com/js-ts/hello-notebook/main/hello.ipynb \
    jsacex/jupyter \
    -- jupyter nbconvert --execute --to notebook --output /outputs/hello_output.ipynb hello.ipynb

/inputs/hello.ipynb: This is the path of the input Jupyter Notebook inside the Docker container.

-i: This flag stands for "input" and is used to provide the URL of the input Jupyter Notebook you want to execute.

https://raw.githubusercontent.com/js-ts/hello-notebook/main/hello.ipynb: This is the URL of the input Jupyter Notebook.

jsacex/jupyter: This is the name of the Docker image used for running the Jupyter Notebook. It is a minimal Jupyter Notebook stack based on the official Jupyter Docker Stacks.

--: This double dash is used to separate the Bacalhau command options from the command that will be executed inside the Docker container.

jupyter nbconvert: This is the primary command used to convert and execute Jupyter Notebooks. It allows for the conversion of notebooks to various formats, including execution.

--execute: This flag tells nbconvert to execute the notebook and store the results in the output file.

--to notebook: This option specifies the output format. In this case, we want to keep the output as a Jupyter Notebook.

--output /outputs/hello_output.ipynb: This option specifies the path and filename for the output Jupyter Notebook, which will contain the results of the executed input notebook.

Checking the State of your Jobs

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

%%bash
bacalhau list --id-filter=${JOB_ID} --no-style

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 describe.

%%bash
bacalhau describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau 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.

%%bash
rm -rf results && mkdir results # Temporary directory to store the results
bacalhau get ${JOB_ID} --output-dir results # Download the results

After the download has finished you can see the contents in the results directory, running the command below:

%%bash
ls results/outputs

hello_output.nbconvert.ipynb

2. Running Notebook to Train an MNIST Model

Building the container (optional)

Prerequisite

1. Install Docker on your local machine.

2. Sign up for a DockerHub account if you don't already have one.

Step 1: Create a Dockerfile

Create a new file named Dockerfile in your project directory with the following content:

# Use the official Python image as the base image
FROM tensorflow/tensorflow:nightly-gpu

# Set the working directory in the container
WORKDIR /

RUN apt-get update -y

COPY mnist.ipynb .
# Install the Python packages
COPY requirements.txt .

RUN python3 -m pip install --upgrade pip

# Install the Python packages
RUN pip install --no-cache-dir -r requirements.txt

RUN pip install -U scikit-learn

This Dockerfile creates a Docker image based on the official TensorFlow GPU-enabled image, sets the working directory to the root, updates the package list, and copies an IPython notebook (mnist.ipynb) and a requirements.txt file. It then upgrades pip and installs Python packages from the requirements.txt file, along with scikit-learn. The resulting image provides an environment ready for running the mnist.ipynb notebook with TensorFlow and scikit-learn, as well as other specified dependencies.

Step 2: Build the Docker Image

In your terminal, navigate to the directory containing the Dockerfile and run the following command to build the Docker image:

docker build -t your-dockerhub-username/jupyter-mnist-tensorflow:latest .

Replace "your-dockerhub-username" with your actual DockerHub username. This command will build the Docker image and tag it with your DockerHub username and the name "your-dockerhub-username/jupyter-mnist-tensorflow".

Step 3: Push the Docker Image to DockerHub

Once the build process is complete, push the Docker image to DockerHub using the following command:

docker push your-dockerhub-username/jupyter-mnist-tensorflow

Again, replace "your-dockerhub-username" with your actual DockerHub username. This command will push the Docker image to your DockerHub repository.

Running the job on Bacalhau

Prerequisite

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

%%bash --out job_id
bacalhau docker run \
    --wait \
    --id-only \
    --timeout 3600 \
    --wait-timeout-secs 3600 \
    --gpu 1 \
    -i gitlfs://huggingface.co/datasets/VedantPadwal/mnist.git \
    jsacex/jupyter-tensorflow-mnist:v02 \
    -- jupyter nbconvert --execute --to notebook --output /outputs/mnist_output.ipynb mnist.ipynb