Projects in tensorforce-client are practically directories on your local drive that are linked to arbitrary google cloud projects from your google account. It’s ok to link more than one local project to the same remote (cloud) project.
To make a local directory a tensorforce-client project, cd into that directory and run:
$ tfcli init -r [gcloud remote project ID] -n [local project name]
If you don’t specify a remote project ID, the client will ask you to provide one. You have to link your local project to an already existing remote google cloud project. The project’s name (-n option) is non-mandatory and the tool will use the remote project’s name.
You will also be asked to provide the service-account (see installation instructions here) that you would like to use for running tfcli commands as well as the private key file to use for client-server communications.
Each local tensorforce-client project contains a convenience directory for configuration files (config/), into which the tool copies - upon project creation - all tensorforce default config files for clusters, experiments, agents, networks, and environments. You can either add more config files to this directory, change existing ones, or ignore this folder altogether and create and maintain your config files elsewhere.
Lists all sub-commands that the main cluster command supports.
$ tfcli cluster create -f [json filename] -n [give the cluster some name]
Creates a Kubernetes (k8s) cluster in the cloud.
The two most important command line flags are -f [json file] and -n [name for the cluster]. Take a look in the configs/clusters directory that is created automatically inside each new project for some example cluster setups (including GPU clusters). For the json-file, you don’t need to give a path (nor the .json extension) if the file is located in the configs/clusters directory.
Instead of providing the -f flag, however, you could also define your cluster’s parameters entirely on the command line (thus without the need for a json file). For supported flags, run:
$ tfcli cluster create --help
For an explanation of the (more powerful) supported json fields, take a look at the cluster class reference here.
$ tfcli cluster list
Lists all clusters currently running in the cloud.
Deletes (shuts down) a cluster running in the cloud. You will only have to provide the name tat you gave to the cluster when you created it via the -c flag.
$ tfcli cluster delete -c [cluster's name]
Use the cluster list command to get the names for all currently running clusters.
Also, if you simply would like to shut down all currently running clusters in your associated gcloud project, do:
$ tfcli cluster delete --all
Lists all sub-commands that the main experiment command supports.
$ tfcli experiment new -f [json filename] -n [give the experiment some name]
Creates a local(!) experiment entry in the current project. By default, this command does not start the experiment in the cloud.
The two most important command line flags are -f [json file] and -n [name for the experiment]. Take a look in the configs/experiments directory that is created automatically inside each new project for some example experiment setups. For the json-file, you don’t need to give a path (nor the .json extension) if the file is located in the configs/experiments directory.
The main parts of an RL experiment are generally the algorithm used (set via the json agent setting), the environment (set via athe json environment setting), in which the agent acts, the neural-network that the agent uses (set via the json network setting), the length of the experiment (set via the various num_timesteps, num_episodes, etc.. settings), and the run_mode. For a detailed description of the different run_modes, take a look here.
For an explanation of all supported json fields, take a look at the Experiment class reference here.
Instead of providing the -f flag on the command line, you could also define your experiment’s parameters entirely on the command line (thus without the need for a json file). For supported flags, run:
$ tfcli experiment new --help
$ tfcli experiment list
Lists all experiments that currently exist in this local project. Their run status (‘running’, ‘paused’, etc..) is shown as well.
$ tfcli experiment start -e [name of the experiment to start] -c [name of the cluster to start it on]
Starts an already existing experiment on a given cluster in the cloud. The -c option is optional and can be omitted, because all experiments are created automatically with a cluster specification. Should the cluster (the -c provided one or the one that’s coming with the experiment) not already be running, it will be created (started) in the cloud before the experiment is set up.
A started experiment will run until one of the stop conditions (settable through num_episodes and/or num_timesteps in the experiment’s json spec file or via the command line) is met. If the cluster the eperiment runs on belongs to the experiment (as opposed to a separately created cluster), that cluster is shut down after experiment completion.
NOTE: Tensorforce-client is still largely under development. As we are conducting reinforcement learning experiments with our different TensorForce-supported environments in the cloud, we will add more and more functionality to this client, especially focusing on representing results and making it easier to benchmark and compare different algorithms and neural network models. The following experiment-related sub-commands are not implemented yet:
Pauses an already running experiment. A paused experiment can be resumed by passing the ``
Stops (aborts) an experiment