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:0.3.0dev0)
--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 2022-10-27 02:03:26 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 2022-10-27 02:03:26 INFO Log directory is: /tmp/torchx_ubcxi1ru
torchx 2022-10-27 02:03:26 INFO Waiting for the app to finish...
python/0 Hello, your name!
torchx 2022-10-27 02:03:27 INFO Job finished: SUCCEEDED
local_cwd://torchx/torchx_utils_python-xbnzqg55qm6jrd
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 2022-10-27 02:03:28 INFO Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2022-10-27 02:03:28 INFO To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2022-10-27 02:03:37 INFO Built new image `sha256:190afc5646e73ee036167638ba415a2df1654ce06476f3bda0880d890360f2d7` based on original image `ghcr.io/pytorch/torchx:0.3.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=python.
torchx 2022-10-27 02:03:38 INFO Waiting for the app to finish...
python/0 Hello, your name!
torchx 2022-10-27 02:03:39 INFO Job finished: SUCCEEDED
local_docker://torchx/torchx_utils_python-t95rf9632bfwxc
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:0.3.0dev0)
--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 2022-10-27 02:03:40 INFO Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2022-10-27 02:03:40 INFO To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2022-10-27 02:03:49 INFO Built new image `sha256:24fd700275f2a2fb6f4f47418fe3b0d6f2b8b8e81e98040ff32c7692d21b6444` based on original image `ghcr.io/pytorch/torchx:0.3.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=dist_app.
torchx 2022-10-27 02:03:50 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/1 [1]:I am worker 3 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]:I am worker 1 of 4!
dist_app/0 [1]:all_reduce output = tensor([6])
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 2022-10-27 02:03:58 INFO Job finished: SUCCEEDED
local_docker://torchx/dist_app-ct092s4kfd126
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
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 2022-10-27 02:04:00 INFO loaded configs from /home/runner/work/torchx/torchx/docs/source/.torchxconfig
torchx 2022-10-27 02:04:00 INFO Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2022-10-27 02:04:00 INFO To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2022-10-27 02:06:25 INFO Built new image `sha256:36e01c1a2096334d7e58dc76812793ad0f85b92786939bae87c6263c3a4ef63e` based on original image `ghcr.io/pytorch/torchx:0.3.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=python.
torchx 2022-10-27 02:06:25 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 2022-10-27 02:06:27 INFO Job finished: SUCCEEDED
local_docker://torchx/torchx_utils_python-qj33k0c6h3mtsd
Slurm¶
The slurm and local_cwd use the current environment so you can use pip and conda as normal.
Next Steps¶
Checkout other features of the torchx CLI
Take a look at the list of schedulers supported by the runner
Browse through the collection of builtin components
See which ML pipeline platforms you can run components on
See a training app example