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 str] [-c str]
[--script str] [--image str]
[--name str] [--cpu int] [--gpu int]
[--memMB int] [-h str]
[--num_replicas int]
...
Runs ``python`` with the specified module, command or script on the specified
image and host. Use ``--`` to separate component args and program args
(e.g. ``torchx run utils.python --m foo.main -- --args to --main``)
Note: (cpu, gpu, memMB) parameters are mutually exclusive with ``h`` (named resource) where
``h`` takes precedence if specified for setting resource requirements.
See `registering named resources <https://pytorch.org/torchx/latest/advanced.html#registering-named-resources>`_.
positional arguments:
str arguments passed to the program in sys.argv[1:] (ignored
with `--c`) (required)
optional arguments:
--help show this help message and exit
-m str, --m str run library module as a script (default: None)
-c str, --c str program passed as string (may error if scheduler has a
length limit on args) (default: None)
--script str .py script to run (default: None)
--image str image to run on (default:
ghcr.io/pytorch/torchx:0.7.0dev0)
--name str name of the job (default: torchx_utils_python)
--cpu int number of cpus per replica (default: 1)
--gpu int number of gpus per replica (default: 0)
--memMB int cpu memory in MB per replica (default: 1024)
-h str, --h str a registered named resource (if specified takes
precedence over cpu, gpu, memMB) (default: None)
--num_replicas int 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 2024-04-19 18:57:23 INFO Tracker configurations: {}
torchx 2024-04-19 18:57:23 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 2024-04-19 18:57:23 INFO Log directory is: /tmp/torchx_hi2smnlt
torchx 2024-04-19 18:57:23 INFO Waiting for the app to finish...
python/0 Hello, your name!
torchx 2024-04-19 18:57:24 INFO Job finished: SUCCEEDED
local_cwd://torchx/torchx_utils_python-dl2xjb5rdbxvsc
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 2024-04-19 18:57:25 INFO Tracker configurations: {}
torchx 2024-04-19 18:57:25 INFO Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2024-04-19 18:57:25 INFO To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2024-04-19 18:57:25 INFO Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2024-04-19 18:57:25 INFO Building workspace docker image (this may take a while)...
torchx 2024-04-19 18:57:25 INFO Step 1/4 : ARG IMAGE
torchx 2024-04-19 18:57:25 INFO Step 2/4 : FROM $IMAGE
torchx 2024-04-19 18:57:25 INFO ---> 6631d167f8fb
torchx 2024-04-19 18:57:25 INFO Step 3/4 : COPY . .
torchx 2024-04-19 18:57:30 INFO ---> 247b7c5ae080
torchx 2024-04-19 18:57:30 INFO Step 4/4 : LABEL torchx.pytorch.org/version=0.7.0dev0
torchx 2024-04-19 18:57:30 INFO ---> Running in c82a93aad476
torchx 2024-04-19 18:57:34 INFO Removing intermediate container c82a93aad476
torchx 2024-04-19 18:57:34 INFO ---> 28df42d77459
torchx 2024-04-19 18:57:34 INFO [Warning] One or more build-args [WORKSPACE] were not consumed
torchx 2024-04-19 18:57:34 INFO Successfully built 28df42d77459
torchx 2024-04-19 18:57:34 INFO Built new image `sha256:28df42d774591b8c2b30520f10e51a8f6aa2f5685579e1c2d08d2a54f6318d7b` based on original image `ghcr.io/pytorch/torchx:0.7.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=python.
torchx 2024-04-19 18:57:34 INFO Waiting for the app to finish...
python/0 Hello, your name!
torchx 2024-04-19 18:57:35 INFO Job finished: SUCCEEDED
local_docker://torchx/torchx_utils_python-vlr37jmrh4ttw
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 str] [-m str]
[--image str] [--name str] [-h str]
[--cpu int] [--gpu int] [--memMB int]
[-j str] [--env str] [--max_retries int]
[--rdzv_port int] [--rdzv_backend str]
[--mounts str] [--debug str] [--tee int]
...
Distributed data parallel style application (one role, multi-replica).
Uses `torch.distributed.run <https://pytorch.org/docs/stable/distributed.elastic.html>`_
to launch and coordinate PyTorch worker processes. Defaults to using ``c10d`` rendezvous backend
on rendezvous_endpoint ``$rank_0_host:$rdzv_port``. Note that ``rdzv_port`` parameter is ignored
when running on single node, and instead we use port 0 which instructs torchelastic to chose
a free random port on the host.
Note: (cpu, gpu, memMB) parameters are mutually exclusive with ``h`` (named resource) where
``h`` takes precedence if specified for setting resource requirements.
See `registering named resources <https://pytorch.org/torchx/latest/advanced.html#registering-named-resources>`_.
positional arguments:
str arguments to the main module (required)
optional arguments:
--help show this help message and exit
--script str script or binary to run within the image (default: None)
-m str, --m str the python module path to run (default: None)
--image str image (e.g. docker) (default:
ghcr.io/pytorch/torchx:0.7.0dev0)
--name str job name override in the following format:
``{experimentname}/{runname}`` or ``{experimentname}/``
or ``/{runname}`` or ``{runname}``. Uses the script or
module name if ``{runname}`` not specified. (default: /)
-h str, --h str a registered named resource (if specified takes
precedence over cpu, gpu, memMB) (default: None)
--cpu int number of cpus per replica (default: 2)
--gpu int number of gpus per replica (default: 0)
--memMB int cpu memory in MB per replica (default: 1024)
-j str, --j str [{min_nnodes}:]{nnodes}x{nproc_per_node}, for gpu hosts,
nproc_per_node must not exceed num gpus (default: 1x2)
--env str environment varibles to be passed to the run (e.g.
ENV1=v1,ENV2=v2,ENV3=v3) (default: None)
--max_retries int the number of scheduler retries allowed (default: 0)
--rdzv_port int 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)
--rdzv_backend str the rendezvous backend to use. Only takes effect when
running multi-node. (default: c10d)
--mounts str 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 str whether to run with preset debug flags enabled (default:
False)
--tee int tees the specified std stream(s) to console + file. 0:
none, 1: stdout, 2: stderr, 3: both (default: 3)
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 2024-04-19 18:57:37 INFO Tracker configurations: {}
torchx 2024-04-19 18:57:37 INFO Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2024-04-19 18:57:37 INFO To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2024-04-19 18:57:38 INFO Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2024-04-19 18:57:38 INFO Building workspace docker image (this may take a while)...
torchx 2024-04-19 18:57:38 INFO Step 1/4 : ARG IMAGE
torchx 2024-04-19 18:57:38 INFO Step 2/4 : FROM $IMAGE
torchx 2024-04-19 18:57:38 INFO ---> 6631d167f8fb
torchx 2024-04-19 18:57:38 INFO Step 3/4 : COPY . .
torchx 2024-04-19 18:57:42 INFO ---> 2b015b4d193d
torchx 2024-04-19 18:57:42 INFO Step 4/4 : LABEL torchx.pytorch.org/version=0.7.0dev0
torchx 2024-04-19 18:57:42 INFO ---> Running in cec527a56d3d
torchx 2024-04-19 18:57:47 INFO Removing intermediate container cec527a56d3d
torchx 2024-04-19 18:57:47 INFO ---> 155608fd91e5
torchx 2024-04-19 18:57:47 INFO [Warning] One or more build-args [WORKSPACE] were not consumed
torchx 2024-04-19 18:57:47 INFO Successfully built 155608fd91e5
torchx 2024-04-19 18:57:47 INFO Built new image `sha256:155608fd91e5524403b4f6252a8b37c412615cb0d3342abda7cd5a702a318cd2` based on original image `ghcr.io/pytorch/torchx:0.7.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=dist_app.
torchx 2024-04-19 18:57:47 INFO Waiting for the app to finish...
dist_app/1 [2024-04-19 18:57:48,696] torch.distributed.run: [WARNING]
dist_app/1 [2024-04-19 18:57:48,696] torch.distributed.run: [WARNING] *****************************************
dist_app/1 [2024-04-19 18:57:48,696] torch.distributed.run: [WARNING] 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 [2024-04-19 18:57:48,696] torch.distributed.run: [WARNING] *****************************************
dist_app/0 [2024-04-19 18:57:48,707] torch.distributed.run: [WARNING]
dist_app/0 [2024-04-19 18:57:48,707] torch.distributed.run: [WARNING] *****************************************
dist_app/0 [2024-04-19 18:57:48,707] torch.distributed.run: [WARNING] 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 [2024-04-19 18:57:48,707] torch.distributed.run: [WARNING] *****************************************
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])
dist_app/0 [1]:I am worker 1 of 4!
dist_app/0 [1]:all_reduce output = tensor([6])
dist_app/0 [0]:I am worker 0 of 4!
dist_app/0 [0]:all_reduce output = tensor([6])
torchx 2024-04-19 18:57:55 INFO Job finished: SUCCEEDED
local_docker://torchx/dist_app-dv9m9l1wt5pwpc
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],[env=ENV],[image_repo=IMAGE_REPO],[quiet=QUIET]
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_*
env=ENV (typing.Dict[str, str], None)
environment variables to be passed to the run. The separator sign can be eiher comma or semicolon
(e.g. ENV1:v1,ENV2:v2,ENV3:v3 or ENV1:V1;ENV2:V2). Environment variables from env will be applied on top
of the ones from copy_env
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
quiet=QUIET (bool, False)
whether to suppress verbose output for image building. Defaults to ``False``.
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],[quiet=QUIET]
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
quiet=QUIET (bool, False)
whether to suppress verbose output for image building. Defaults to ``False``.
kubernetes_mcad:
usage:
[namespace=NAMESPACE],[image_repo=IMAGE_REPO],[service_account=SERVICE_ACCOUNT],[priority=PRIORITY],[priority_class_name=PRIORITY_CLASS_NAME],[image_secret=IMAGE_SECRET],[coscheduler_name=COSCHEDULER_NAME],[network=NETWORK]
optional arguments:
namespace=NAMESPACE (str, default)
Kubernetes namespace to schedule job in
image_repo=IMAGE_REPO (str, None)
The image repository to use when pushing patched images, must have push access. Ex: example.com/your/container
service_account=SERVICE_ACCOUNT (str, None)
The service account name to set on the pod specs
priority=PRIORITY (int, None)
The priority level to set on the job specs. Higher integer value means higher priority
priority_class_name=PRIORITY_CLASS_NAME (str, None)
Pod specific priority level. Check with your Kubernetes cluster admin if Priority classes are defined on your system
image_secret=IMAGE_SECRET (str, None)
The name of the Kubernetes/OpenShift secret set up for private images
coscheduler_name=COSCHEDULER_NAME (str, None)
Option to run TorchX-MCAD with a co-scheduler. User must provide the co-scheduler name.
network=NETWORK (str, None)
Name of additional pod-to-pod network beyond default Kubernetes network
aws_batch:
usage:
queue=QUEUE,[user=USER],[privileged=PRIVILEGED],[share_id=SHARE_ID],[priority=PRIORITY],[job_role_arn=JOB_ROLE_ARN],[execution_role_arn=EXECUTION_ROLE_ARN],[image_repo=IMAGE_REPO],[quiet=QUIET]
required arguments:
queue=QUEUE (str)
queue to schedule job in
optional arguments:
user=USER (str, runner)
The username to tag the job with. `getpass.getuser()` if not specified.
privileged=PRIVILEGED (bool, False)
If true runs the container with elevated permissions. Equivalent to running with `docker run --privileged`.
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, 0)
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.
job_role_arn=JOB_ROLE_ARN (str, None)
The Amazon Resource Name (ARN) of the IAM role that the container can assume for AWS permissions.
execution_role_arn=EXECUTION_ROLE_ARN (str, None)
The Amazon Resource Name (ARN) of the IAM role that the ECS agent can assume for AWS permissions.
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
quiet=QUIET (bool, False)
whether to suppress verbose output for image building. Defaults to ``False``.
sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /home/runner/.config/sagemaker/config.yaml
aws_sagemaker:
usage:
role=ROLE,instance_type=INSTANCE_TYPE,[instance_count=INSTANCE_COUNT],[user=USER],[keep_alive_period_in_seconds=KEEP_ALIVE_PERIOD_IN_SECONDS],[volume_size=VOLUME_SIZE],[volume_kms_key=VOLUME_KMS_KEY],[max_run=MAX_RUN],[input_mode=INPUT_MODE],[output_path=OUTPUT_PATH],[output_kms_key=OUTPUT_KMS_KEY],[base_job_name=BASE_JOB_NAME],[tags=TAGS],[subnets=SUBNETS],[security_group_ids=SECURITY_GROUP_IDS],[model_uri=MODEL_URI],[model_channel_name=MODEL_CHANNEL_NAME],[metric_definitions=METRIC_DEFINITIONS],[encrypt_inter_container_traffic=ENCRYPT_INTER_CONTAINER_TRAFFIC],[use_spot_instances=USE_SPOT_INSTANCES],[max_wait=MAX_WAIT],[checkpoint_s3_uri=CHECKPOINT_S3_URI],[checkpoint_local_path=CHECKPOINT_LOCAL_PATH],[debugger_hook_config=DEBUGGER_HOOK_CONFIG],[enable_sagemaker_metrics=ENABLE_SAGEMAKER_METRICS],[enable_network_isolation=ENABLE_NETWORK_ISOLATION],[disable_profiler=DISABLE_PROFILER],[environment=ENVIRONMENT],[max_retry_attempts=MAX_RETRY_ATTEMPTS],[source_dir=SOURCE_DIR],[git_config=GIT_CONFIG],[hyperparameters=HYPERPARAMETERS],[container_log_level=CONTAINER_LOG_LEVEL],[code_location=CODE_LOCATION],[dependencies=DEPENDENCIES],[training_repository_access_mode=TRAINING_REPOSITORY_ACCESS_MODE],[training_repository_credentials_provider_arn=TRAINING_REPOSITORY_CREDENTIALS_PROVIDER_ARN],[disable_output_compression=DISABLE_OUTPUT_COMPRESSION],[enable_infra_check=ENABLE_INFRA_CHECK],[image_repo=IMAGE_REPO],[quiet=QUIET]
required arguments:
role=ROLE (str)
an AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.
instance_type=INSTANCE_TYPE (str)
type of EC2 instance to use for training, for example, 'ml.c4.xlarge'
optional arguments:
instance_count=INSTANCE_COUNT (int, 1)
number of Amazon EC2 instances to use for training. Required if instance_groups is not set.
user=USER (str, runner)
the username to tag the job with. `getpass.getuser()` if not specified.
keep_alive_period_in_seconds=KEEP_ALIVE_PERIOD_IN_SECONDS (int, None)
the duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
volume_size=VOLUME_SIZE (int, None)
size in GB of the storage volume to use for storing input and output data during training (default: 30).
volume_kms_key=VOLUME_KMS_KEY (str, None)
KMS key ID for encrypting EBS volume attached to the training instance.
max_run=MAX_RUN (int, None)
timeout in seconds for training (default: 24 * 60 * 60).
input_mode=INPUT_MODE (str, None)
the input mode that the algorithm supports (default: ‘File’).
output_path=OUTPUT_PATH (str, None)
S3 location for saving the training result (model artifacts and output files). If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the fit() method execution.
output_kms_key=OUTPUT_KMS_KEY (str, None)
KMS key ID for encrypting the training output (default: Your IAM role’s KMS key for Amazon S3).
base_job_name=BASE_JOB_NAME (str, None)
prefix for training job name when the fit() method launches. If not specified, the estimator generates a default job name based on the training image name and current timestamp.
tags=TAGS (typing.List[typing.Dict[str, str]], None)
list of tags for labeling a training job.
subnets=SUBNETS (typing.List[str], None)
list of subnet ids. If not specified training job will be created without VPC config.
security_group_ids=SECURITY_GROUP_IDS (typing.List[str], None)
list of security group ids. If not specified training job will be created without VPC config.
model_uri=MODEL_URI (str, None)
URI where a pre-trained model is stored, either locally or in S3.
model_channel_name=MODEL_CHANNEL_NAME (str, None)
name of the channel where ‘model_uri’ will be downloaded (default: ‘model’).
metric_definitions=METRIC_DEFINITIONS (typing.List[typing.Dict[str, str]], None)
list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: ‘Name’ for the name of the metric, and ‘Regex’ for the regular expression used to extract the metric from the logs.
encrypt_inter_container_traffic=ENCRYPT_INTER_CONTAINER_TRAFFIC (bool, None)
specifies whether traffic between training containers is encrypted for the training job (default: False).
use_spot_instances=USE_SPOT_INSTANCES (bool, None)
specifies whether to use SageMaker Managed Spot instances for training. If enabled then the max_wait arg should also be set.
max_wait=MAX_WAIT (int, None)
timeout in seconds waiting for spot training job.
checkpoint_s3_uri=CHECKPOINT_S3_URI (str, None)
S3 URI in which to persist checkpoints that the algorithm persists (if any) during training.
checkpoint_local_path=CHECKPOINT_LOCAL_PATH (str, None)
local path that the algorithm writes its checkpoints to.
debugger_hook_config=DEBUGGER_HOOK_CONFIG (bool, None)
configuration for how debugging information is emitted with SageMaker Debugger. If not specified, a default one is created using the estimator’s output_path, unless the region does not support SageMaker Debugger. To disable SageMaker Debugger, set this parameter to False.
enable_sagemaker_metrics=ENABLE_SAGEMAKER_METRICS (bool, None)
enable SageMaker Metrics Time Series.
enable_network_isolation=ENABLE_NETWORK_ISOLATION (bool, None)
specifies whether container will run in network isolation mode (default: False).
disable_profiler=DISABLE_PROFILER (bool, None)
specifies whether Debugger monitoring and profiling will be disabled (default: False).
environment=ENVIRONMENT (typing.Dict[str, str], None)
environment variables to be set for use during training job
max_retry_attempts=MAX_RETRY_ATTEMPTS (int, None)
number of times to move a job to the STARTING status. You can specify between 1 and 30 attempts.
source_dir=SOURCE_DIR (str, None)
absolute, relative, or S3 URI Path to a directory with any other training source code dependencies aside from the entry point file (default: current working directory)
git_config=GIT_CONFIG (typing.Dict[str, str], None)
git configurations used for cloning files, including repo, branch, commit, 2FA_enabled, username, password, and token.
hyperparameters=HYPERPARAMETERS (typing.Dict[str, str], None)
dictionary containing the hyperparameters to initialize this estimator with.
container_log_level=CONTAINER_LOG_LEVEL (int, None)
log level to use within the container (default: logging.INFO).
code_location=CODE_LOCATION (str, None)
S3 prefix URI where custom code is uploaded.
dependencies=DEPENDENCIES (typing.List[str], None)
list of absolute or relative paths to directories with any additional libraries that should be exported to the container.
training_repository_access_mode=TRAINING_REPOSITORY_ACCESS_MODE (str, None)
specifies how SageMaker accesses the Docker image that contains the training algorithm.
training_repository_credentials_provider_arn=TRAINING_REPOSITORY_CREDENTIALS_PROVIDER_ARN (str, None)
Amazon Resource Name (ARN) of an AWS Lambda function that provides credentials to authenticate to the private Docker registry where your training image is hosted.
disable_output_compression=DISABLE_OUTPUT_COMPRESSION (bool, None)
when set to true, Model is uploaded to Amazon S3 without compression after training finishes.
enable_infra_check=ENABLE_INFRA_CHECK (bool, None)
specifies whether it is running Sagemaker built-in infra check jobs.
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
quiet=QUIET (bool, False)
whether to suppress verbose output for image building. Defaults to ``False``.
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 2024-04-19 18:57:58 INFO loaded configs from /home/runner/work/torchx/torchx/docs/source/.torchxconfig
torchx 2024-04-19 18:57:58 INFO Tracker configurations: {}
torchx 2024-04-19 18:57:58 INFO Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2024-04-19 18:57:58 INFO To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2024-04-19 18:57:58 INFO Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2024-04-19 18:57:58 INFO Building workspace docker image (this may take a while)...
torchx 2024-04-19 18:57:58 INFO Step 1/4 : FROM pytorch/pytorch:1.10.0-cuda11.3-cudnn8-runtime
torchx 2024-04-19 18:59:16 INFO ---> c3f17e5ac010
torchx 2024-04-19 18:59:16 INFO Step 2/4 : RUN pip install timm
torchx 2024-04-19 18:59:16 INFO ---> Running in d7e528402b5c
torchx 2024-04-19 18:59:16 INFO Collecting timm
torchx 2024-04-19 18:59:16 INFO Downloading timm-0.9.12-py3-none-any.whl (2.2 MB)
torchx 2024-04-19 18:59:16 INFO Requirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (from timm) (0.11.0)
torchx 2024-04-19 18:59:16 INFO Collecting huggingface-hub
torchx 2024-04-19 18:59:16 INFO Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)
torchx 2024-04-19 18:59:17 INFO Requirement already satisfied: pyyaml in /opt/conda/lib/python3.7/site-packages (from timm) (5.4.1)
torchx 2024-04-19 18:59:17 INFO Collecting safetensors
torchx 2024-04-19 18:59:17 INFO Downloading safetensors-0.4.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
torchx 2024-04-19 18:59:17 INFO Requirement already satisfied: torch>=1.7 in /opt/conda/lib/python3.7/site-packages (from timm) (1.10.0)
torchx 2024-04-19 18:59:17 INFO Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.7/site-packages (from torch>=1.7->timm) (3.10.0.2)
torchx 2024-04-19 18:59:17 INFO Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from huggingface-hub->timm) (2.25.1)
torchx 2024-04-19 18:59:17 INFO Collecting fsspec
torchx 2024-04-19 18:59:17 INFO Downloading fsspec-2023.1.0-py3-none-any.whl (143 kB)
torchx 2024-04-19 18:59:17 INFO Collecting importlib-metadata
torchx 2024-04-19 18:59:17 INFO Downloading importlib_metadata-6.7.0-py3-none-any.whl (22 kB)
torchx 2024-04-19 18:59:17 INFO Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub->timm) (3.0.12)
torchx 2024-04-19 18:59:17 INFO Collecting packaging>=20.9
torchx 2024-04-19 18:59:17 INFO Downloading packaging-24.0-py3-none-any.whl (53 kB)
torchx 2024-04-19 18:59:17 INFO Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub->timm) (4.61.2)
torchx 2024-04-19 18:59:17 INFO Collecting zipp>=0.5
torchx 2024-04-19 18:59:17 INFO Downloading zipp-3.15.0-py3-none-any.whl (6.8 kB)
torchx 2024-04-19 18:59:18 INFO Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub->timm) (4.0.0)
torchx 2024-04-19 18:59:18 INFO Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub->timm) (2.10)
torchx 2024-04-19 18:59:18 INFO Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub->timm) (2021.10.8)
torchx 2024-04-19 18:59:18 INFO Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub->timm) (1.26.6)
torchx 2024-04-19 18:59:18 INFO Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision->timm) (1.21.2)
torchx 2024-04-19 18:59:18 INFO Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision->timm) (8.4.0)
torchx 2024-04-19 18:59:18 INFO Installing collected packages: zipp, packaging, importlib-metadata, fsspec, safetensors, huggingface-hub, timm
torchx 2024-04-19 18:59:18 INFO Successfully installed fsspec-2023.1.0 huggingface-hub-0.16.4 importlib-metadata-6.7.0 packaging-24.0 safetensors-0.4.3 timm-0.9.12 zipp-3.15.0
torchx 2024-04-19 18:59:21 INFO Removing intermediate container d7e528402b5c
torchx 2024-04-19 18:59:21 INFO ---> 97ec5f2ae171
torchx 2024-04-19 18:59:21 INFO Step 3/4 : COPY . .
torchx 2024-04-19 18:59:22 INFO ---> 0d5ef852cc7a
torchx 2024-04-19 18:59:22 INFO Step 4/4 : LABEL torchx.pytorch.org/version=0.7.0dev0
torchx 2024-04-19 18:59:22 INFO ---> Running in c15579d0e667
torchx 2024-04-19 18:59:24 INFO Removing intermediate container c15579d0e667
torchx 2024-04-19 18:59:24 INFO ---> 8578cbb21288
torchx 2024-04-19 18:59:24 INFO [Warning] One or more build-args [IMAGE WORKSPACE] were not consumed
torchx 2024-04-19 18:59:24 INFO Successfully built 8578cbb21288
torchx 2024-04-19 18:59:24 INFO Built new image `sha256:8578cbb21288cb100cb65d3cadbdfb236141dc2f6a5e6556ba37ff2eaf4ab3a5` based on original image `ghcr.io/pytorch/torchx:0.7.0dev0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=python.
torchx 2024-04-19 18:59:24 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 2024-04-19 18:59:26 INFO Job finished: SUCCEEDED
local_docker://torchx/torchx_utils_python-xz9nffhh7g1nkc
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
