sentence-transformers How to use strickvl/finetuned-all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("strickvl/finetuned-all-MiniLM-L6-v2")
sentences = [
"How can I configure the orchestrator settings for each cloud provider in ZenML?",
". If not set, the cluster will not be autostopped.down: Tear down the cluster after all jobs finish (successfully or abnormally). If idle_minutes_to_autostop is also set, the cluster will be torn down after the specified idle time. Note that if errors occur during provisioning/data syncing/setting up, the cluster will not be torn down for debugging purposes.\n\nstream_logs: If True, show the logs in the terminal as they are generated while the cluster is running.\n\ndocker_run_args: Additional arguments to pass to the docker run command. For example, ['--gpus=all'] to use all GPUs available on the VM.\n\nThe following code snippets show how to configure the orchestrator settings for each cloud provider:\n\nCode Example:\n\nfrom zenml.integrations.skypilot_aws.flavors.skypilot_orchestrator_aws_vm_flavor import SkypilotAWSOrchestratorSettings\n\nskypilot_settings = SkypilotAWSOrchestratorSettings(\n\ncpus=\"2\",\n\nmemory=\"16\",\n\naccelerators=\"V100:2\",\n\naccelerator_args={\"tpu_vm\": True, \"runtime_version\": \"tpu-vm-base\"},\n\nuse_spot=True,\n\nspot_recovery=\"recovery_strategy\",\n\nregion=\"us-west-1\",\n\nzone=\"us-west1-a\",\n\nimage_id=\"ami-1234567890abcdef0\",\n\ndisk_size=100,\n\ndisk_tier=\"high\",\n\ncluster_name=\"my_cluster\",\n\nretry_until_up=True,\n\nidle_minutes_to_autostop=60,\n\ndown=True,\n\nstream_logs=True\n\ndocker_run_args=[\"--gpus=all\"]\n\n@pipeline(\n\nsettings={\n\n\"orchestrator.vm_aws\": skypilot_settings\n\nCode Example:\n\nfrom zenml.integrations.skypilot_gcp.flavors.skypilot_orchestrator_gcp_vm_flavor import SkypilotGCPOrchestratorSettings\n\nskypilot_settings = SkypilotGCPOrchestratorSettings(\n\ncpus=\"2\",\n\nmemory=\"16\",\n\naccelerators=\"V100:2\",\n\naccelerator_args={\"tpu_vm\": True, \"runtime_version\": \"tpu-vm-base\"},\n\nuse_spot=True,\n\nspot_recovery=\"recovery_strategy\",\n\nregion=\"us-west1\",\n\nzone=\"us-west1-a\",\n\nimage_id=\"ubuntu-pro-2004-focal-v20231101\",\n\ndisk_size=100,\n\ndisk_tier=\"high\",\n\ncluster_name=\"my_cluster\",\n\nretry_until_up=True,\n\nidle_minutes_to_autostop=60,\n\ndown=True,\n\nstream_logs=True\n\n@pipeline(\n\nsettings={\n\n\"orchestrator.vm_gcp\": skypilot_settings",
"he Post-execution workflow has changed as follows:The get_pipelines and get_pipeline methods have been moved out of the Repository (i.e. the new Client ) class and lie directly in the post_execution module now. To use the user has to do:\n\nfrom zenml.post_execution import get_pipelines, get_pipeline\n\nNew methods to directly get a run have been introduced: get_run and get_unlisted_runs method has been introduced to get unlisted runs.\n\nUsage remains largely similar. Please read the new docs for post-execution to inform yourself of what further has changed.\n\nHow to migrate: Replace all post-execution workflows from the paradigm of Repository.get_pipelines or Repository.get_pipeline_run to the corresponding post_execution methods.\n\n📡Future Changes\n\nWhile this rehaul is big and will break previous releases, we do have some more work left to do. However we also expect this to be the last big rehaul of ZenML before our 1.0.0 release, and no other release will be so hard breaking as this one. Currently planned future breaking changes are:\n\nFollowing the metadata store, the secrets manager stack component might move out of the stack.\n\nZenML StepContext might be deprecated.\n\n🐞 Reporting Bugs\n\nWhile we have tried our best to document everything that has changed, we realize that mistakes can be made and smaller changes overlooked. If this is the case, or you encounter a bug at any time, the ZenML core team and community are available around the clock on the growing Slack community.\n\nFor bug reports, please also consider submitting a GitHub Issue.\n\nLastly, if the new changes have left you desiring a feature, then consider adding it to our public feature voting board. Before doing so, do check what is already on there and consider upvoting the features you desire the most.\n\nPreviousMigration guide\n\nNextMigration guide 0.23.0 → 0.30.0\n\nLast updated 12 days ago",
"nML, namely an orchestrator and an artifact store.Keep in mind, that each one of these components is built on top of base abstractions and is completely extensible.\n\nOrchestrator\n\nAn Orchestrator is a workhorse that coordinates all the steps to run in a pipeline. Since pipelines can be set up with complex combinations of steps with various asynchronous dependencies between them, the orchestrator acts as the component that decides what steps to run and when to run them.\n\nZenML comes with a default local orchestrator designed to run on your local machine. This is useful, especially during the exploration phase of your project. You don't have to rent a cloud instance just to try out basic things.\n\nArtifact Store\n\nAn Artifact Store is a component that houses all data that pass through the pipeline as inputs and outputs. Each artifact that gets stored in the artifact store is tracked and versioned and this allows for extremely useful features like data caching which speeds up your workflows.\n\nSimilar to the orchestrator, ZenML comes with a default local artifact store designed to run on your local machine. This is useful, especially during the exploration phase of your project. You don't have to set up a cloud storage system to try out basic things.\n\nFlavor\n\nZenML provides a dedicated base abstraction for each stack component type. These abstractions are used to develop solutions, called Flavors, tailored to specific use cases/tools. With ZenML installed, you get access to a variety of built-in and integrated Flavors for each component type, but users can also leverage the base abstractions to create their own custom flavors.\n\nStack Switching\n\nWhen it comes to production-grade solutions, it is rarely enough to just run your workflow locally without including any cloud infrastructure."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
