ModalCLI
Execute Modal commands from the Command Line Interface
type: "io.kestra.plugin.modal.cli.ModalCLI"Run a Hello-World Modal example. Make sure to replace the MODAL_TOKEN_ID and MODAL_TOKEN_SECRET with your Modal credentials.
id: modal_hello_world
namespace: company.team
tasks:
- id: hello
type: io.kestra.plugin.modal.cli.ModalCLI
env:
MODAL_TOKEN_ID: "your_modal_token_id"
MODAL_TOKEN_SECRET: "your_modal_token_secret"
commands:
- modal run hello.py
inputFiles:
hello.py: |
import modal
app = modal.App("hello-world")
@app.function()
def hello():
print("hello from modal")
return "Success!"
Pass environment variables to the Modal CLI task from Kestra's inputs.
id: env_vars_modal
namespace: company.team
inputs:
- id: run_modal
displayName: Whether to run the Modal task
type: BOOLEAN
defaults: true
- id: cpu
type: SELECT
displayName: CPU request
description: The number of CPU cores to allocate to the container
defaults: "0.25"
values: ["0.25", "0.5", "0.75", "1.0", "1.5", "2.0", "4.0", "8.0", "16.0", "32.0"]
dependsOn:
inputs:
- run_modal
condition: "{{ inputs.run_modal equals true }}"
- id: memory
type: SELECT
displayName: Memory request
description: Amount of memory in MiB to allocate to the container
defaults: "512"
values: ["512", "1024", "2048", "4096", "8192", "16384", "32768"]
dependsOn:
inputs:
- run_modal
condition: "{{ inputs.run_modal }}"
tasks:
- id: set_compute_resources
type: io.kestra.plugin.modal.cli.ModalCLI
env:
MODAL_TOKEN_ID: "{{ secret('MODAL_TOKEN_ID') }}"
MODAL_TOKEN_SECRET: "{{ secret('MODAL_TOKEN_SECRET') }}"
CPU: "{{ inputs.cpu }}"
MEMORY: "{{ inputs.memory }}"
commands:
- modal run script.py
inputFiles:
script.py: |
import os
import modal
app = modal.App(
"env-vars",
secrets=[modal.Secret.from_local_environ(env_keys=["CPU", "MEMORY"])],
)
@app.function(cpu=float(os.getenv("CPU")), memory=int(os.getenv("MEMORY")))
def generate_data():
cpu = os.getenv("CPU")
memory = os.getenv("MEMORY")
resources = dict(cpu=cpu, memory=memory)
print(f"Running the function with CPU={cpu} and MEMORY={memory}")
return resources
@app.local_entrypoint()
def main():
output = generate_data.remote()
print(f"hello from main function - output is: {output}")
Execute a Python script from Git on a cloud VM using Modal.
id: modal_git
namespace: company.team
tasks:
- id: repository
type: io.kestra.plugin.core.flow.WorkingDirectory
tasks:
- id: clone
type: io.kestra.plugin.git.Clone
branch: main
url: https://github.com/kestra-io/scripts
- id: modal_cli
type: io.kestra.plugin.modal.cli.ModalCLI
commands:
- modal run modal/getting_started.py
env:
MODAL_TOKEN_ID: "{{ secret('MODAL_TOKEN_ID') }}"
MODAL_TOKEN_SECRET: "{{ secret('MODAL_TOKEN_SECRET') }}"
Execute a Python script on a GPU-powered instance in the cloud using Modal. First, add the script that you want to orchestrate as a Namespace File in the Editor and point to it in the commands section.
id: modal
namespace: company.team
tasks:
- id: modal_cli
type: io.kestra.plugin.modal.cli.ModalCLI
namespaceFiles:
enabled: true
commands:
- modal run gpu.py
env:
MODAL_TOKEN_ID: "{{ secret('MODAL_TOKEN_ID') }}"
MODAL_TOKEN_SECRET: "{{ secret('MODAL_TOKEN_SECRET') }}"
The commands to run
The commands to execute before the main list of commands
The task runner container image, only used if the task runner is container-based.
Deprecated, use 'taskRunner' instead
Additional environment variables for the current process.
The files to create on the local filesystem. It can be a map or a JSON object.
Inject namespace files.
Inject namespace files to this task. When enabled, it will, by default, load all namespace files into the working directory. However, you can use the include or exclude properties to limit which namespace files will be injected.
The files from the local filesystem to send to Kestra's internal storage.
Must be a list of glob expressions relative to the current working directory, some examples: my-dir/**, my-dir/*/** or my-dir/my-file.txt.
The task runner to use.
Task runners are provided by plugins, each have their own properties.
The exit code of the entire flow execution.
The output files' URIs in Kestra's internal storage.
The value extracted from the output of the executed commands.
A list of filters to exclude matching glob patterns. This allows you to exclude a subset of the Namespace Files from being downloaded at runtime. You can combine this property together with include to only inject a subset of files that you need into the task's working directory.
A list of filters to include only matching glob patterns. This allows you to only load a subset of the Namespace Files into the working directory.
The maximum amount of kernel memory the container can use.
The minimum allowed value is 4MB. Because kernel memory cannot be swapped out, a container which is starved of kernel memory may block host machine resources, which can have side effects on the host machine and on other containers. See the kernel-memory docs for more details.
The maximum amount of memory resources the container can use.
Make sure to use the format number + unit (regardless of the case) without any spaces.
The unit can be KB (kilobytes), MB (megabytes), GB (gigabytes), etc.
Given that it's case-insensitive, the following values are equivalent:
"512MB""512Mb""512mb""512000KB""0.5GB"
It is recommended that you allocate at least 6MB.
Allows you to specify a soft limit smaller than memory which is activated when Docker detects contention or low memory on the host machine.
If you use memoryReservation, it must be set lower than memory for it to take precedence. Because it is a soft limit, it does not guarantee that the container doesn’t exceed the limit.
The total amount of memory and swap that can be used by a container.
If memory and memorySwap are set to the same value, this prevents containers from using any swap. This is because memorySwap includes both the physical memory and swap space, while memory is only the amount of physical memory that can be used.
A setting which controls the likelihood of the kernel to swap memory pages.
By default, the host kernel can swap out a percentage of anonymous pages used by a container. You can set memorySwappiness to a value between 0 and 100 to tune this percentage.
Docker image to use.
Docker configuration file.
Docker configuration file that can set access credentials to private container registries. Usually located in ~/.docker/config.json.
Limits the CPU usage to a given maximum threshold value.
By default, each container’s access to the host machine’s CPU cycles is unlimited. You can set various constraints to limit a given container’s access to the host machine’s CPU cycles.
Docker entrypoint to use.
Extra hostname mappings to the container network interface configuration.
Docker API URI.
Limits memory usage to a given maximum threshold value.
Docker can enforce hard memory limits, which allow the container to use no more than a given amount of user or system memory, or soft limits, which allow the container to use as much memory as it needs unless certain conditions are met, such as when the kernel detects low memory or contention on the host machine. Some of these options have different effects when used alone or when more than one option is set.
Docker network mode to use e.g. host, none, etc.
The image pull policy for a container image and the tag of the image, which affect when Docker attempts to pull (download) the specified image.
Size of /dev/shm in bytes.
The size must be greater than 0. If omitted, the system uses 64MB.
User in the Docker container.
List of volumes to mount.
Must be a valid mount expression as string, example : /home/user:/app.
Volumes mount are disabled by default for security reasons; you must enable them on server configuration by setting kestra.tasks.scripts.docker.volume-enabled to true.
The registry authentication.
The auth field is a base64-encoded authentication string of username: password or a token.
The identity token.
The registry password.
The registry URL.
If not defined, the registry will be extracted from the image name.
The registry token.
The registry username.
A list of capabilities; an OR list of AND lists of capabilities.
Driver-specific options, specified as key/value pairs.
These options are passed directly to the driver.