Experiment options

Web UI
CLI
Web UI
Experiment web UI Options List
Option
Description
Machine Type
This is the type of instance to run your Experiment's on. Many Experiments benefit from a machine with a GPU, but some can run just using a CPU.
Container
Experiments are run within a docker container. You can run a public or private container. Learn more here. 
Workspace
The workspace is the collection of code that is run. The only option in the web UI is a Git repo (eg https://github.com/Paperspace/fast-style-transfer.git).  The CLI offers more advanced options such as specifying a local directory, private Git repos, S3 buckets, and more.
Command
The command is the entry point to the container. This is the line of code that will kick off your experiment's job. It could be a bash script ./run.sh or python main.py as a few examples. 
Ports
You have the option to attach a public IP automatically. Supports opening multiple ports simultaneously, separated by : . Learn more about opening ports here. 
Custom Metrics
Enter a list of custom metrics to use with Gradient's statd client, such as percent_failure or percent_success.
CLI
Experiment CLI Options List
Option
Description
name
Experiment name
ports
An optional list of port mappings to open on the instance while the job is running. The port mappings are specified as 'XXXX:YYYY' where XXXX is an external port number and YYYY is an internal port number. Multiple port mappings can be provided as a comma separated list. Port numbers must be greater than 1023. Note: only /tcp protocol usage is supported. Learn more here. 
workspace
Supports a path to a local directory, git repository, or s3 bucket. 
workspaceRef
An option to specify a Git commit hash, branch name or tag. Learn more here. 
workspaceArchive
Path to workspace archive. (Currently being deprecated in an upcoming version.)
workspaceUrl
Path to workspace archive. (Currently being deprecated in an upcoming version.)
workspaceUsername
Used to pass-in a username to a private repo.
workspacePassword
Used to pass-in a password to a private repo.
ignoreFiles
When using the directory upload option, this command can be used to ignore certain files from uploading.
workingDirectory
Working directory for the experiment
artifactDirectory
Where artifacts can be located.
clusterId
Cluster ID of the private Gradient cluster.  Learn more here. 
experimentEnv
Environmental variables in JSON String Format. Example: 
{"HKS_EPOCHS":1,"HKS_MAX_EVALS":4,"DATASET_SIZE":100} Learn more here. 
projectId
Your Project ID.
modelType
This option is used to specify the format of your model. Learn more here. 
modelPath
This option is used to locate your model. Learn more here. 
isPreemptible
Flag that specifies if the Experiment is preemptible. 
container
A reference to a docker image in a public or private docker registry, or a container name provided by Paperspace. Docker image repository references must be in lowercase and may include a tag and a hostname prefix followed by a slash; if committed the hostname defaults to that of the public Docker Hub registry. An example docker image reference: docker.io/mynamespace/myimage:mytag. A container name may be mixed case. (Designated container names are currently only provided as part of various Gradient tutorials and samples.)
machineType
An optional machine type to run the job on: either 'GPU+', 'P4000', 'P5000', 'P6000', 'V100', 'K80', 'P100', or 'TPU'.
command
Experiment entrypoint command.
containerUser
Name of user in container. 
registryUsername
Used to pass-in a username to a private container registry.
registryPassword
Used to pass-in a password to a private container registry.
registryUrl
Used to pass-in a path to a container in private container registry.
no-logs
Flag to prevent streaming logs in terminal.
datasetUri
Url to S3 bucket with dataset
datasetName
Name of dataset
datasetAwsAccessKeyId
S3 bucket's Access Key ID  
datasetAwsSecretAccessKey
S3 bucket's Secret Access Key
datasetVersionId
S3 dataset's version ID
datasetEtag
S3 dataset's ETag
tensorboard
Add to existing TensorBoard. If no or many
 TensorBoard exists a new one will be created.
apiKey
API key to use this time only.
optionsFile
Path to YAML file with predefined options. 
createOptionsFile
Generate template options file

Experiments - Previous

Using Experiments

Single-node & multi-node CLI options

Last updated 6 months ago

Option	Description
Machine Type	This is the type of instance to run your Experiment's on. Many Experiments benefit from a machine with a GPU, but some can run just using a CPU.
Container	Experiments are run within a docker container. You can run a public or private container. Learn more here.
Workspace	The workspace is the collection of code that is run. The only option in the web UI is a Git repo (eg https://github.com/Paperspace/fast-style-transfer.git). The CLI offers more advanced options such as specifying a local directory, private Git repos, S3 buckets, and more.
Command	The command is the entry point to the container. This is the line of code that will kick off your experiment's job. It could be a bash script `./run.sh` or `python main.py` as a few examples.
Ports	You have the option to attach a public IP automatically. Supports opening multiple ports simultaneously, separated by `:` . Learn more about opening ports here.
Custom Metrics	Enter a list of custom metrics to use with Gradient's statd client, such as percent_failure or percent_success.

Option	Description
`name`	Experiment name
`ports`	An optional list of port mappings to open on the instance while the job is running. The port mappings are specified as 'XXXX:YYYY' where XXXX is an external port number and YYYY is an internal port number. Multiple port mappings can be provided as a comma separated list. Port numbers must be greater than 1023. Note: only /tcp protocol usage is supported. Learn more here.
`workspace`	Supports a path to a local directory, git repository, or s3 bucket.
`workspaceRef`	An option to specify a Git commit hash, branch name or tag. Learn more here.
`workspaceArchive`	Path to workspace archive. (Currently being deprecated in an upcoming version.)
`workspaceUrl`	Path to workspace archive. (Currently being deprecated in an upcoming version.)
`workspaceUsername`	Used to pass-in a username to a private repo.
`workspacePassword`	Used to pass-in a password to a private repo.
`ignoreFiles`	When using the directory upload option, this command can be used to ignore certain files from uploading.
`workingDirectory`	Working directory for the experiment
`artifactDirectory`	Where artifacts can be located.
`clusterId`	Cluster ID of the private Gradient cluster. Learn more here.
`experimentEnv`	Environmental variables in JSON String Format. Example: `{"HKS_EPOCHS":1,"HKS_MAX_EVALS":4,"DATASET_SIZE":100}` Learn more here.
`projectId`	Your Project ID.
`modelType`	This option is used to specify the format of your model. Learn more here.
`modelPath`	This option is used to locate your model. Learn more here.
`isPreemptible`	Flag that specifies if the Experiment is preemptible.
`container`	A reference to a docker image in a public or private docker registry, or a container name provided by Paperspace. Docker image repository references must be in lowercase and may include a tag and a hostname prefix followed by a slash; if committed the hostname defaults to that of the public Docker Hub registry. An example docker image reference: docker.io/mynamespace/myimage:mytag. A container name may be mixed case. (Designated container names are currently only provided as part of various Gradient tutorials and samples.)
`machineType`	An optional machine type to run the job on: either 'GPU+', 'P4000', 'P5000', 'P6000', 'V100', 'K80', 'P100', or 'TPU'.
`command`	Experiment entrypoint command.
`containerUser`	Name of user in container.
`registryUsername`	Used to pass-in a username to a private container registry.
`registryPassword`	Used to pass-in a password to a private container registry.
`registryUrl`	Used to pass-in a path to a container in private container registry.
`no-logs`	Flag to prevent streaming logs in terminal.
`datasetUri`	Url to S3 bucket with dataset
`datasetName`	Name of dataset
`datasetAwsAccessKeyId`	S3 bucket's Access Key ID
`datasetAwsSecretAccessKey`	S3 bucket's Secret Access Key
`datasetVersionId`	S3 dataset's version ID
`datasetEtag`	S3 dataset's ETag
`tensorboard`	Add to existing TensorBoard. If no or many TensorBoard exists a new one will be created.
`apiKey`	API key to use this time only.
`optionsFile`	Path to YAML file with predefined options.
`createOptionsFile`	Generate template options file