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Quickstart

This is a self contained guide on how to write a simple app and start launching distributed jobs on local and remote clusters.

Installation

First thing we need to do is to install the TorchX python package which includes the CLI and the library.

# install torchx with all dependencies
$ pip install torchx[dev]

See the README for more information on installation.

[1]:
%%sh
torchx --help
usage: torchx [-h] [--log_level LOG_LEVEL] [--version]
              {builtins,cancel,configure,describe,list,log,run,runopts,status,tracker}
              ...

torchx CLI

optional arguments:
  -h, --help            show this help message and exit
  --log_level LOG_LEVEL
                        Python logging log level
  --version             show program's version number and exit

sub-commands:
  Use the following commands to run operations, e.g.: torchx run ${JOB_NAME}

  {builtins,cancel,configure,describe,list,log,run,runopts,status,tracker}

Hello World

Lets start off with writing a simple “Hello World” python app. This is just a normal python program and can contain anything you’d like.

Note

This example uses Jupyter Notebook %%writefile to create local files for example purposes. Under normal usage you would have these as standalone files.

[2]:
%%writefile my_app.py

import sys

print(f"Hello, {sys.argv[1]}!")
Overwriting my_app.py

Launching

We can execute our app via torchx run. The local_cwd scheduler executes the app relative to the current directory.

For this we’ll use the utils.python component:

[3]:
%%sh
torchx run --scheduler local_cwd utils.python --help
usage: torchx run <run args...> python  [--help] [-m M] [-c C]
                                        [--script SCRIPT] [--image IMAGE]
                                        [--name NAME] [--cpu CPU] [--gpu GPU]
                                        [--memMB MEMMB] [-h H]
                                        [--num_replicas NUM_REPLICAS]
                                        ...

Runs ``python`` with the specified module, command or script on the specified
...

positional arguments:
  args                  arguments passed to the program in sys.argv[1:]
                        (ignored with `--c`) (required)

optional arguments:
  --help                show this help message and exit
  -m M, --m M           run library module as a script (default: None)
  -c C, --c C           program passed as string (may error if scheduler has a
                        length limit on args) (default: None)
  --script SCRIPT       .py script to run (default: None)
  --image IMAGE         image to run on (default:
                        ghcr.io/pytorch/torchx:0.4.0dev0)
  --name NAME           name of the job (default: torchx_utils_python)
  --cpu CPU             number of cpus per replica (default: 1)
  --gpu GPU             number of gpus per replica (default: 0)
  --memMB MEMMB         cpu memory in MB per replica (default: 1024)
  -h H, --h H           a registered named resource (if specified takes
                        precedence over cpu, gpu, memMB) (default: None)
  --num_replicas NUM_REPLICAS
                        number of copies to run (each on its own container)
                        (default: 1)

The component takes in the script name and any extra arguments will be passed to the script itself.

[4]:
%%sh
torchx run --scheduler local_cwd utils.python --script my_app.py "your name"
torchx 2022-11-22 07:54:20 INFO     Log directory not set in scheduler cfg. Creating a temporary log dir that will be deleted on exit. To preserve log directory set the `log_dir` cfg option
torchx 2022-11-22 07:54:20 INFO     Log directory is: /tmp/torchx_1w58w7nq
torchx 2022-11-22 07:54:20 INFO     Waiting for the app to finish...
python/0 Hello, your name!
torchx 2022-11-22 07:54:21 INFO     Job finished: SUCCEEDED
local_cwd://torchx/torchx_utils_python-h9dvvhtqchlcwc

We can run the exact same app via the local_docker scheduler. This scheduler will package up the local workspace as a layer on top of the specified image. This provides a very similar environment to the container based remote schedulers.

Note

This requires Docker installed and won’t work in environments such as Google Colab. See the Docker install instructions: https://docs.docker.com/get-docker/

[5]:
%%sh
torchx run --scheduler local_docker utils.python --script my_app.py "your name"
torchx 2022-11-22 07:54:21 INFO     Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2022-11-22 07:54:21 INFO     To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2022-11-22 07:54:21 INFO     Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2022-11-22 07:54:22 INFO     Building workspace docker image (this may take a while)...
torchx 2022-11-22 07:54:30 INFO     Built new image `sha256:d55861c2659a2bf39724533fa7b5c930303bb72ca91b58962e7a6acfc951587a` based on original image `ghcr.io/pytorch/torchx:0.4.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=python.
torchx 2022-11-22 07:54:30 INFO     Waiting for the app to finish...
python/0 Hello, your name!
torchx 2022-11-22 07:54:31 INFO     Job finished: SUCCEEDED
local_docker://torchx/torchx_utils_python-f5qghnnfkrmxlc

TorchX defaults to using the ghcr.io/pytorch/torchx Docker container image which contains the PyTorch libraries, TorchX and related dependencies.

Distributed

TorchX’s dist.ddp component uses TorchElastic to manage the workers. This means you can launch multi-worker and multi-host jobs out of the box on all of the schedulers we support.

[6]:
%%sh
torchx run --scheduler local_docker dist.ddp --help
usage: torchx run <run args...> ddp  [--help] [--script SCRIPT] [-m M]
                                     [--image IMAGE] [--name NAME] [-h H]
                                     [--cpu CPU] [--gpu GPU] [--memMB MEMMB]
                                     [-j J] [--env ENV]
                                     [--max_retries MAX_RETRIES]
                                     [--rdzv_port RDZV_PORT] [--mounts MOUNTS]
                                     [--debug DEBUG]
                                     ...

Distributed data parallel style application (one role, multi-replica). ...

positional arguments:
  script_args           arguments to the main module (required)

optional arguments:
  --help                show this help message and exit
  --script SCRIPT       script or binary to run within the image (default:
                        None)
  -m M, --m M           the python module path to run (default: None)
  --image IMAGE         image (e.g. docker) (default:
                        ghcr.io/pytorch/torchx:0.4.0dev0)
  --name NAME           job name override (uses the script name if not
                        specified) (default: None)
  -h H, --h H           a registered named resource (if specified takes
                        precedence over cpu, gpu, memMB) (default: None)
  --cpu CPU             number of cpus per replica (default: 2)
  --gpu GPU             number of gpus per replica (default: 0)
  --memMB MEMMB         cpu memory in MB per replica (default: 1024)
  -j J, --j J           [{min_nnodes}:]{nnodes}x{nproc_per_node}, for gpu
                        hosts, nproc_per_node must not exceed num gpus
                        (default: 1x2)
  --env ENV             environment varibles to be passed to the run (e.g.
                        ENV1=v1,ENV2=v2,ENV3=v3) (default: None)
  --max_retries MAX_RETRIES
                        the number of scheduler retries allowed (default: 0)
  --rdzv_port RDZV_PORT
                        the port on rank0's host to use for hosting the c10d
                        store used for rendezvous. Only takes effect when
                        running multi-node. When running single node, this
                        parameter is ignored and a random free port is chosen.
                        (default: 29500)
  --mounts MOUNTS       mounts to mount into the worker environment/container
                        (ex.
                        type=<bind/volume>,src=/host,dst=/job[,readonly]). See
                        scheduler documentation for more info. (default: None)
  --debug DEBUG         whether to run with preset debug flags enabled
                        (default: False)

Lets create a slightly more interesting app to leverage the TorchX distributed support.

[7]:
%%writefile dist_app.py

import torch
import torch.distributed as dist

dist.init_process_group(backend="gloo")
print(f"I am worker {dist.get_rank()} of {dist.get_world_size()}!")

a = torch.tensor([dist.get_rank()])
dist.all_reduce(a)
print(f"all_reduce output = {a}")
Writing dist_app.py

Let launch a small job with 2 nodes and 2 worker processes per node:

[8]:
%%sh
torchx run --scheduler local_docker dist.ddp -j 2x2 --script dist_app.py
torchx 2022-11-22 07:54:33 INFO     Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2022-11-22 07:54:33 INFO     To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2022-11-22 07:54:33 INFO     Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2022-11-22 07:54:33 INFO     Building workspace docker image (this may take a while)...
torchx 2022-11-22 07:54:41 INFO     Built new image `sha256:640ff13a54e700a2364edc563ca64e3516e1d5ec2c59bdfa7f34e4a2cdb07d5f` based on original image `ghcr.io/pytorch/torchx:0.4.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=dist_app.
torchx 2022-11-22 07:54:42 INFO     Waiting for the app to finish...
dist_app/0 WARNING:__main__:
dist_app/0 *****************************************
dist_app/0 Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
dist_app/0 *****************************************
dist_app/1 WARNING:__main__:
dist_app/1 *****************************************
dist_app/1 Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
dist_app/1 *****************************************
dist_app/0 [1]:I am worker 1 of 4!
dist_app/0 [0]:I am worker 0 of 4!
dist_app/0 [0]:all_reduce output = tensor([6])
dist_app/0 [1]:all_reduce output = tensor([6])
dist_app/1 [1]:I am worker 3 of 4!
dist_app/1 [1]:all_reduce output = tensor([6])
dist_app/1 [0]:I am worker 2 of 4!
dist_app/1 [0]:all_reduce output = tensor([6])
torchx 2022-11-22 07:54:49 INFO     Job finished: SUCCEEDED
local_docker://torchx/dist_app-rmhwfj3q6p9npc

Workspaces / Patching

For each scheduler there’s a concept of an image. For local_cwd and slurm it uses the current working directory. For container based schedulers such as local_docker, kubernetes and aws_batch it uses a docker container.

To provide the same environment between local and remote jobs, TorchX CLI uses workspaces to automatically patch images for remote jobs on a per scheduler basis.

When you launch a job via torchx run it’ll overlay the current directory on top of the provided image so your code is available in the launched job.

For docker based schedulers you’ll need a local docker daemon to build and push the image to your remote docker repository.

.torchxconfig

Arguments to schedulers can be specified either via a command line flag to torchx run -s <scheduler> -c <args> or on a per scheduler basis via a .torchxconfig file.

[9]:
%%writefile .torchxconfig

[kubernetes]
queue=torchx
image_repo=<your docker image repository>

[slurm]
partition=torchx
Writing .torchxconfig

Remote Schedulers

TorchX supports a large number of schedulers. Don’t see yours? Request it!

Remote schedulers operate the exact same way the local schedulers do. The same run command for local works out of the box on remote.

$ torchx run --scheduler slurm dist.ddp -j 2x2 --script dist_app.py
$ torchx run --scheduler kubernetes dist.ddp -j 2x2 --script dist_app.py
$ torchx run --scheduler aws_batch dist.ddp -j 2x2 --script dist_app.py
$ torchx run --scheduler ray dist.ddp -j 2x2 --script dist_app.py

Depending on the scheduler there may be a few extra configuration parameters so TorchX knows where to run the job and upload built images. These can either be set via -c or in the .torchxconfig file.

All config options:

[10]:
%%sh
torchx runopts
local_docker:
    usage:
        [copy_env=COPY_ENV],[image_repo=IMAGE_REPO]

    optional arguments:
        copy_env=COPY_ENV (typing.List[str], None)
            list of glob patterns of environment variables to copy if not set in AppDef. Ex: FOO_*
        image_repo=IMAGE_REPO (str, None)
            (remote jobs) the image repository to use when pushing patched images, must have push access. Ex: example.com/your/container

local_cwd:
    usage:
        [log_dir=LOG_DIR],[prepend_cwd=PREPEND_CWD],[auto_set_cuda_visible_devices=AUTO_SET_CUDA_VISIBLE_DEVICES]

    optional arguments:
        log_dir=LOG_DIR (str, None)
            dir to write stdout/stderr log files of replicas
        prepend_cwd=PREPEND_CWD (bool, False)
            if set, prepends CWD to replica's PATH env var making any binaries in CWD take precedence over those in PATH
        auto_set_cuda_visible_devices=AUTO_SET_CUDA_VISIBLE_DEVICES (bool, False)
            sets the `CUDA_AVAILABLE_DEVICES` for roles that request GPU resources. Each role replica will be assigned one GPU. Does nothing if the device count is less than replicas.

slurm:
    usage:
        [partition=PARTITION],[time=TIME],[comment=COMMENT],[constraint=CONSTRAINT],[mail-user=MAIL-USER],[mail-type=MAIL-TYPE],[job_dir=JOB_DIR]

    optional arguments:
        partition=PARTITION (str, None)
            The partition to run the job in.
        time=TIME (str, None)
            The maximum time the job is allowed to run for. Formats:             "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours",             "days-hours:minutes" or "days-hours:minutes:seconds"
        comment=COMMENT (str, None)
            Comment to set on the slurm job.
        constraint=CONSTRAINT (str, None)
            Constraint to use for the slurm job.
        mail-user=MAIL-USER (str, None)
            User to mail on job end.
        mail-type=MAIL-TYPE (str, None)
            What events to mail users on.
        job_dir=JOB_DIR (str, None)
            The directory to place the job code and outputs. The
            directory must not exist and will be created. To enable log
            iteration, jobs will be tracked in ``.torchxslurmjobdirs``.


kubernetes:
    usage:
        queue=QUEUE,[namespace=NAMESPACE],[service_account=SERVICE_ACCOUNT],[priority_class=PRIORITY_CLASS],[image_repo=IMAGE_REPO]

    required arguments:
        queue=QUEUE (str)
            Volcano queue to schedule job in

    optional arguments:
        namespace=NAMESPACE (str, default)
            Kubernetes namespace to schedule job in
        service_account=SERVICE_ACCOUNT (str, None)
            The service account name to set on the pod specs
        priority_class=PRIORITY_CLASS (str, None)
            The name of the PriorityClass to set on the job specs
        image_repo=IMAGE_REPO (str, None)
            (remote jobs) the image repository to use when pushing patched images, must have push access. Ex: example.com/your/container

aws_batch:
    usage:
        queue=QUEUE,[share_id=SHARE_ID],[priority=PRIORITY],[image_repo=IMAGE_REPO]

    required arguments:
        queue=QUEUE (str)
            queue to schedule job in

    optional arguments:
        share_id=SHARE_ID (str, None)
            The share identifier for the job. This must be set if and only if the job queue has a scheduling policy.
        priority=PRIORITY (int, None)
            The scheduling priority for the job within the context of share_id. Higher number (between 0 and 9999) means higher priority. This will only take effect if the job queue has a scheduling policy.
        image_repo=IMAGE_REPO (str, None)
            (remote jobs) the image repository to use when pushing patched images, must have push access. Ex: example.com/your/container

gcp_batch:
    usage:
        [project=PROJECT],[location=LOCATION]

    optional arguments:
        project=PROJECT (str, None)
            Name of the GCP project. Defaults to the configured GCP project in the environment
        location=LOCATION (str, us-central1)
            Name of the location to schedule the job in. Defaults to us-central1

ray:
    usage:
        [cluster_config_file=CLUSTER_CONFIG_FILE],[cluster_name=CLUSTER_NAME],[dashboard_address=DASHBOARD_ADDRESS],[requirements=REQUIREMENTS]

    optional arguments:
        cluster_config_file=CLUSTER_CONFIG_FILE (str, None)
            Use CLUSTER_CONFIG_FILE to access or create the Ray cluster.
        cluster_name=CLUSTER_NAME (str, None)
            Override the configured cluster name.
        dashboard_address=DASHBOARD_ADDRESS (str, 127.0.0.1:8265)
            Use ray status to get the dashboard address you will submit jobs against
        requirements=REQUIREMENTS (str, None)
            Path to requirements.txt

lsf:
    usage:
        [lsf_queue=LSF_QUEUE],[jobdir=JOBDIR],[container_workdir=CONTAINER_WORKDIR],[host_network=HOST_NETWORK],[shm_size=SHM_SIZE]

    optional arguments:
        lsf_queue=LSF_QUEUE (str, None)
            queue name to submit jobs
        jobdir=JOBDIR (str, None)
            The directory to place the job code and outputs. The directory must not exist and will be created.
        container_workdir=CONTAINER_WORKDIR (str, None)
            working directory in container jobs
        host_network=HOST_NETWORK (bool, False)
            True if using the host network for jobs
        shm_size=SHM_SIZE (str, 64m)
            size of shared memory (/dev/shm) for jobs

Custom Images

Docker-based Schedulers

If you want more than the standard PyTorch libraries you can add custom Dockerfile or build your own docker container and use it as the base image for your TorchX jobs.

[11]:
%%writefile timm_app.py

import timm

print(timm.models.resnet18())
Writing timm_app.py
[12]:
%%writefile Dockerfile.torchx

FROM pytorch/pytorch:1.10.0-cuda11.3-cudnn8-runtime

RUN pip install timm

COPY . .
Writing Dockerfile.torchx

Once we have the Dockerfile created we can launch as normal and TorchX will automatically build the image with the newly provided Dockerfile instead of the default one.

[13]:
%%sh
torchx run --scheduler local_docker utils.python --script timm_app.py
torchx 2022-11-22 07:54:51 INFO     loaded configs from /home/runner/work/torchx/torchx/docs/source/.torchxconfig
torchx 2022-11-22 07:54:52 INFO     Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2022-11-22 07:54:52 INFO     To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2022-11-22 07:54:52 INFO     Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2022-11-22 07:54:52 INFO     Building workspace docker image (this may take a while)...
torchx 2022-11-22 07:56:12 INFO     Built new image `sha256:c24bcf48387b9b691cefb5a5ff434955a6cbbf4240e8efcabf28e1e126678a05` based on original image `ghcr.io/pytorch/torchx:0.4.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=python.
torchx 2022-11-22 07:56:13 INFO     Waiting for the app to finish...
python/0 ResNet(
python/0   (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
python/0   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0   (act1): ReLU(inplace=True)
python/0   (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
python/0   (layer1): Sequential(
python/0     (0): BasicBlock(
python/0       (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0     )
python/0     (1): BasicBlock(
python/0       (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0     )
python/0   )
python/0   (layer2): Sequential(
python/0     (0): BasicBlock(
python/0       (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0       (downsample): Sequential(
python/0         (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
python/0         (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       )
python/0     )
python/0     (1): BasicBlock(
python/0       (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0     )
python/0   )
python/0   (layer3): Sequential(
python/0     (0): BasicBlock(
python/0       (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0       (downsample): Sequential(
python/0         (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
python/0         (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       )
python/0     )
python/0     (1): BasicBlock(
python/0       (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0     )
python/0   )
python/0   (layer4): Sequential(
python/0     (0): BasicBlock(
python/0       (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0       (downsample): Sequential(
python/0         (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
python/0         (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       )
python/0     )
python/0     (1): BasicBlock(
python/0       (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (drop_block): Identity()
python/0       (act1): ReLU(inplace=True)
python/0       (aa): Identity()
python/0       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
python/0       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
python/0       (act2): ReLU(inplace=True)
python/0     )
python/0   )
python/0   (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))
python/0   (fc): Linear(in_features=512, out_features=1000, bias=True)
python/0 )
torchx 2022-11-22 07:56:15 INFO     Job finished: SUCCEEDED
local_docker://torchx/torchx_utils_python-l0f0kn53t1c46c

Slurm

The slurm and local_cwd use the current environment so you can use pip and conda as normal.

Next Steps

  1. Checkout other features of the torchx CLI

  2. Take a look at the list of schedulers supported by the runner

  3. Browse through the collection of builtin components

  4. See which ML pipeline platforms you can run components on

  5. See a training app example

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