# embeddings_queue

created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c8ca-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "multi_worker_model.fit(multi_worker_dataset, epochs=3, steps_per_epoch=70)\n\n\nInstantiate a TensorflowTrainer\nwith 4 workers, and use it to run the new training function.\nfrom ray.train.tensorflow import TensorflowTrainer\nfrom ray.train import ScalingConfig\n\n# For GPU Training, set `use_gpu` to True.\nuse_gpu = False\n\ntrainer = TensorflowTrainer(train_func_distributed, scaling_config=ScalingConfig(num_workers=4, use_gpu=use_gpu))\n\ntrainer.fit()\n\n\nLearn more about Ray Train\n\n\n\n\n\n Tune: Hyperparameter Tuning at Scale\n\n\n\n\nTune is a library for hyperparameter tuning at any scale.\nWith Tune, you can launch a multi-node distributed hyperparameter sweep in less than 10 lines of code.\nTune supports any deep learning framework, including PyTorch, TensorFlow, and Keras.\n\nNote\nTo run this example, install Ray Tune:\npip install -U \"ray[tune]\"\n\n\n\nThis example runs a small grid search with an iterative training function.\nfrom ray import train, tune"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c91a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "def objective(config):  # \u2460\n    score = config[\"a\"] ** 2 + config[\"b\"]\n    return {\"score\": score}\n\n\nsearch_space = {  # \u2461\n    \"a\": tune.grid_search([0.001, 0.01, 0.1, 1.0]),\n    \"b\": tune.choice([1, 2, 3]),\n}\n\ntuner = tune.Tuner(objective, param_space=search_space)  # \u2462\n\nresults = tuner.fit()\nprint(results.get_best_result(metric=\"score\", mode=\"min\").config)\n\n\nIf TensorBoard is installed, automatically visualize all trial results:\ntensorboard --logdir ~/ray_results\n\n\nLearn more about Ray Tune\n\n\n\n Serve: Scalable Model Serving\n\n\n\n\nRay Serve is a scalable model-serving library built on Ray.\n\nNote\nTo run this example, install Ray Serve and scikit-learn:\npip install -U \"ray[serve]\" scikit-learn\n\n\n\nThis example runs serves a scikit-learn gradient boosting classifier.\nimport requests\nfrom starlette.requests import Request\nfrom typing import Dict\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nfrom ray import serve"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c960-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "from ray import serve\n\n\n# Train model.\niris_dataset = load_iris()\nmodel = GradientBoostingClassifier()\nmodel.fit(iris_dataset[\"data\"], iris_dataset[\"target\"])\n\n\n@serve.deployment\nclass BoostingModel:\n    def __init__(self, model):\n        self.model = model\n        self.label_list = iris_dataset[\"target_names\"].tolist()\n\n    async def __call__(self, request: Request) -> Dict:\n        payload = (await request.json())[\"vector\"]\n        print(f\"Received http request with data {payload}\")\n\n        prediction = self.model.predict([payload])[0]\n        human_name = self.label_list[prediction]\n        return {\"result\": human_name}\n\n\n# Deploy model.\nserve.run(BoostingModel.bind(model), route_prefix=\"/iris\")\n\n# Query it!\nsample_request_input = {\"vector\": [1.2, 1.0, 1.1, 0.9]}\nresponse = requests.get(\n    \"http://localhost:8000/iris\", json=sample_request_input)\nprint(response.text)\n\n\nAs a result you will see {\"result\": \"versicolor\"}.\nLearn more about Ray Serve"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c9b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "As a result you will see {\"result\": \"versicolor\"}.\nLearn more about Ray Serve\n\n\n\n RLlib: Industry-Grade Reinforcement Learning\n\n\n\n\nRLlib is an industry-grade library for reinforcement learning (RL) built on top of Ray.\nRLlib offers high scalability and unified APIs for a variety of industry- and research applications.\n\nNote\nTo run this example, install rllib and either tensorflow or pytorch:\npip install -U \"ray[rllib]\" tensorflow  # or torch\n\n\n\nimport gymnasium as gym\nfrom ray.rllib.algorithms.ppo import PPOConfig\n\n\n# Define your problem using python and Farama-Foundation's gymnasium API:\nclass SimpleCorridor(gym.Env):\n    \"\"\"Corridor in which an agent must learn to move right to reach the exit.\n\n    ---------------------\n    | S | 1 | 2 | 3 | G |   S=start; G=goal; corridor_length=5\n    ---------------------"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986c9f6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Possible actions to chose from are: 0=left; 1=right\n    Observations are floats indicating the current field index, e.g. 0.0 for\n    starting position, 1.0 for the field next to the starting position, etc..\n    Rewards are -0.1 for all steps, except when reaching the goal (+1.0).\n    \"\"\"\n\n    def __init__(self, config):\n        self.end_pos = config[\"corridor_length\"]\n        self.cur_pos = 0\n        self.action_space = gym.spaces.Discrete(2)  # left and right\n        self.observation_space = gym.spaces.Box(0.0, self.end_pos, shape=(1,))\n\n    def reset(self, *, seed=None, options=None):\n        \"\"\"Resets the episode.\n\n        Returns:\n           Initial observation of the new episode and an info dict.\n        \"\"\"\n        self.cur_pos = 0\n        # Return initial observation.\n        return [self.cur_pos], {}\n\n    def step(self, action):\n        \"\"\"Takes a single step in the episode given `action`."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ca78-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "def step(self, action):\n        \"\"\"Takes a single step in the episode given `action`.\n\n        Returns:\n            New observation, reward, terminated-flag, truncated-flag, info-dict (empty).\n        \"\"\"\n        # Walk left.\n        if action == 0 and self.cur_pos > 0:\n            self.cur_pos -= 1\n        # Walk right.\n        elif action == 1:\n            self.cur_pos += 1\n        # Set `terminated` flag when end of corridor (goal) reached.\n        terminated = self.cur_pos >= self.end_pos\n        truncated = False\n        # +1 when goal reached, otherwise -1.\n        reward = 1.0 if terminated else -0.1\n        return [self.cur_pos], reward, terminated, truncated, {}"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cac8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Create an RLlib Algorithm instance from a PPOConfig object.\nconfig = (\n    PPOConfig().environment(\n        # Env class to use (here: our gym.Env sub-class from above).\n        env=SimpleCorridor,\n        # Config dict to be passed to our custom env's constructor.\n        # Use corridor with 20 fields (including S and G).\n        env_config={\"corridor_length\": 28},\n    )\n    # Parallelize environment rollouts.\n    .rollouts(num_rollout_workers=3)\n)\n# Construct the actual (PPO) algorithm object from the config.\nalgo = config.build()\n\n# Train for n iterations and report results (mean episode rewards).\n# Since we have to move at least 19 times in the env to reach the goal and\n# each move gives us -0.1 reward (except the last move at the end: +1.0),\n# we can expect to reach an optimal episode reward of -0.1*18 + 1.0 = -0.8\nfor i in range(5):\n    results = algo.train()\n    print(f\"Iter: {i}; avg. reward={results['episode_reward_mean']}\")"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cb18-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Perform inference (action computations) based on given env observations.\n# Note that we are using a slightly different env here (len 10 instead of 20),\n# however, this should still work as the agent has (hopefully) learned\n# to \"just always walk right!\"\nenv = SimpleCorridor({\"corridor_length\": 10})\n# Get the initial observation (should be: [0.0] for the starting position).\nobs, info = env.reset()\nterminated = truncated = False\ntotal_reward = 0.0\n# Play one episode.\nwhile not terminated and not truncated:\n    # Compute a single action, given the current observation\n    # from the environment.\n    action = algo.compute_single_action(obs)\n    # Apply the computed action in the environment.\n    obs, reward, terminated, truncated, info = env.step(action)\n    # Sum up rewards for reporting purposes.\n    total_reward += reward\n# Report results.\nprint(f\"Played 1 episode; total-reward={total_reward}\")\n\n\nLearn more about Ray RLlib"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cb68-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Learn more about Ray RLlib\n\n\n\nRay Core Quickstart#\nTurn functions and classes easily into Ray tasks and actors,\nfor Python and Java, with simple primitives for building and running distributed applications.\n\n\n Core: Parallelizing Functions with Ray Tasks\n\n\n\n\n\n\n\nPython\n\nNote\nTo run this example install Ray Core:\npip install -U \"ray\"\n\n\n\nImport Ray and and initialize it with ray.init().\nThen decorate the function with @ray.remote to declare that you want to run this function remotely.\nLastly, call the function with .remote() instead of calling it normally.\nThis remote call yields a future, a Ray object reference, that you can then fetch with ray.get.\nimport ray\nray.init()\n\n@ray.remote\ndef f(x):\n    return x * x\n\nfutures = [f.remote(i) for i in range(4)]\nprint(ray.get(futures)) # [0, 1, 4, 9]\n\n\n\n\n\nJava\n\nNote\nTo run this example, add the ray-api and ray-runtime dependencies in your project."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cbae-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Java\n\nNote\nTo run this example, add the ray-api and ray-runtime dependencies in your project.\n\nUse Ray.init to initialize Ray runtime.\nThen use Ray.task(...).remote() to convert any Java static method into a Ray task.\nThe task runs asynchronously in a remote worker process. The remote method returns an ObjectRef,\nand you can fetch the actual result with get.\nimport io.ray.api.ObjectRef;\nimport io.ray.api.Ray;\nimport java.util.ArrayList;\nimport java.util.List;\n\npublic class RayDemo {\n\n    public static int square(int x) {\n        return x * x;\n    }"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cbfe-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "public class RayDemo {\n\n    public static int square(int x) {\n        return x * x;\n    }\n\n    public static void main(String[] args) {\n        // Intialize Ray runtime.\n        Ray.init();\n        List<ObjectRef<Integer>> objectRefList = new ArrayList<>();\n        // Invoke the `square` method 4 times remotely as Ray tasks.\n        // The tasks will run in parallel in the background.\n        for (int i = 0; i < 4; i++) {\n            objectRefList.add(Ray.task(RayDemo::square, i).remote());\n        }\n        // Get the actual results of the tasks.\n        System.out.println(Ray.get(objectRefList));  // [0, 1, 4, 9]\n    }\n}\n\n\nIn the above code block we defined some Ray Tasks. While these are great for stateless operations, sometimes you\nmust maintain the state of your application. You can do that with Ray Actors.\nLearn more about Ray Core\n\n\n\n\n\n Core: Parallelizing Classes with Ray Actors"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cc44-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Core: Parallelizing Classes with Ray Actors\n\n\n\n\nRay provides actors to allow you to parallelize an instance of a class in Python or Java.\nWhen you instantiate a class that is a Ray actor, Ray will start a remote instance\nof that class in the cluster. This actor can then execute remote method calls and\nmaintain its own internal state.\n\n\n\nPython\n\nNote\nTo run this example install Ray Core:\npip install -U \"ray\"\n\n\n\nimport ray\nray.init() # Only call this once.\n\n@ray.remote\nclass Counter(object):\n    def __init__(self):\n        self.n = 0\n\n    def increment(self):\n        self.n += 1\n\n    def read(self):\n        return self.n\n\ncounters = [Counter.remote() for i in range(4)]\n[c.increment.remote() for c in counters]\nfutures = [c.read.remote() for c in counters]\nprint(ray.get(futures)) # [1, 1, 1, 1]\n\n\n\n\n\nJava\n\nNote\nTo run this example, add the ray-api and ray-runtime dependencies in your project."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ccbc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Java\n\nNote\nTo run this example, add the ray-api and ray-runtime dependencies in your project.\n\nimport io.ray.api.ActorHandle;\nimport io.ray.api.ObjectRef;\nimport io.ray.api.Ray;\nimport java.util.ArrayList;\nimport java.util.List;\nimport java.util.stream.Collectors;\n\npublic class RayDemo {\n\n    public static class Counter {\n\n        private int value = 0;\n\n        public void increment() {\n            this.value += 1;\n        }\n\n        public int read() {\n            return this.value;\n        }\n    }\n\n    public static void main(String[] args) {\n        // Intialize Ray runtime.\n        Ray.init();\n        List<ActorHandle<Counter>> counters = new ArrayList<>();\n        // Create 4 actors from the `Counter` class.\n        // They will run in remote worker processes.\n        for (int i = 0; i < 4; i++) {\n            counters.add(Ray.actor(Counter::new).remote());\n        }"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cd02-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "// Invoke the `increment` method on each actor.\n        // This will send an actor task to each remote actor.\n        for (ActorHandle<Counter> counter : counters) {\n            counter.task(Counter::increment).remote();\n        }\n        // Invoke the `read` method on each actor, and print the results.\n        List<ObjectRef<Integer>> objectRefList = counters.stream()\n            .map(counter -> counter.task(Counter::read).remote())\n            .collect(Collectors.toList());\n        System.out.println(Ray.get(objectRefList));  // [1, 1, 1, 1]\n    }\n}\n\n\nLearn more about Ray Core\n\n\n\n\n\nRay Cluster Quickstart#\nDeploy your applications on Ray clusters, often with minimal code changes to your existing code.\n\n\n Clusters: Launching a Ray Cluster on AWS\n\n\n\n\nRay programs can run on a single machine, or seamlessly scale to large clusters.\nTake this simple example that waits for individual nodes to join the cluster.\n\n\nexample.py\n\n\n\n\nimport sys\nimport time\nfrom collections import Counter"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cd52-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "example.py\n\n\n\n\nimport sys\nimport time\nfrom collections import Counter\n\nimport ray\n\n\n@ray.remote\ndef get_host_name(x):\n    import platform\n    import time\n\n    time.sleep(0.01)\n    return x + (platform.node(),)\n\n\ndef wait_for_nodes(expected):\n    # Wait for all nodes to join the cluster.\n    while True:\n        num_nodes = len(ray.nodes())\n        if num_nodes < expected:\n            print(\n                \"{} nodes have joined so far, waiting for {} more.\".format(\n                    num_nodes, expected - num_nodes\n                )\n            )\n            sys.stdout.flush()\n            time.sleep(1)\n        else:\n            break\n\n\ndef main():\n    wait_for_nodes(4)\n\n    # Check that objects can be transferred from each node to each other node.\n    for i in range(10):\n        print(\"Iteration {}\".format(i))\n        results = [get_host_name.remote(get_host_name.remote(())) for _ in range(100)]\n        print(Counter(ray.get(results)))\n        sys.stdout.flush()"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cd98-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "print(\"Success!\")\n    sys.stdout.flush()\n    time.sleep(20)\n\n\nif __name__ == \"__main__\":\n    ray.init(address=\"localhost:6379\")\n    main()\n\n\n\nYou can also download this example from our GitHub repository.\nGo ahead and store it locally in a file called example.py.\nTo execute this script in the cloud, just download this configuration file,\nor copy it here:\n\n\ncluster.yaml\n\n\n\n\n# An unique identifier for the head node and workers of this cluster.\ncluster_name: default\n\n# The maximum number of workers nodes to launch in addition to the head\n# node.\nmax_workers: 2\n\n# The autoscaler will scale up the cluster faster with higher upscaling speed.\n# E.g., if the task requires adding more nodes then autoscaler will gradually\n# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.\n# This number should be > 0.\nupscaling_speed: 1.0"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cde8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# This executes all commands on all nodes in the docker container,\n# and opens all the necessary ports to support the Ray cluster.\n# Empty string means disabled.\ndocker:\n    image: \"rayproject/ray-ml:latest-gpu\" # You can change this to latest-cpu if you don't need GPU support and want a faster startup\n    # image: rayproject/ray:latest-cpu   # use this one if you don't need ML dependencies, it's faster to pull\n    container_name: \"ray_container\"\n    # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image\n    # if no cached version is present.\n    pull_before_run: True\n    run_options:   # Extra options to pass into \"docker run\"\n        - --ulimit nofile=65536:65536\n\n    # Example of running a GPU head with CPU workers\n    # head_image: \"rayproject/ray-ml:latest-gpu\"\n    # Allow Ray to automatically detect GPUs\n\n    # worker_image: \"rayproject/ray-ml:latest-cpu\"\n    # worker_run_options: []"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986ce6a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# worker_image: \"rayproject/ray-ml:latest-cpu\"\n    # worker_run_options: []\n\n# If a node is idle for this many minutes, it will be removed.\nidle_timeout_minutes: 5\n\n# Cloud-provider specific configuration.\nprovider:\n    type: aws\n    region: us-west-2\n    # Availability zone(s), comma-separated, that nodes may be launched in.\n    # Nodes will be launched in the first listed availability zone and will\n    # be tried in the subsequent availability zones if launching fails.\n    availability_zone: us-west-2a,us-west-2b\n    # Whether to allow node reuse. If set to False, nodes will be terminated\n    # instead of stopped.\n    cache_stopped_nodes: True # If not present, the default is True."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986cee2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# How Ray will authenticate with newly launched nodes.\nauth:\n    ssh_user: ubuntu\n# By default Ray creates a new private keypair, but you can also use your own.\n# If you do so, make sure to also set \"KeyName\" in the head and worker node\n# configurations below.\n#    ssh_private_key: /path/to/your/key.pem"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d432-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Tell the autoscaler the allowed node types and the resources they provide.\n# The key is the name of the node type, which is just for debugging purposes.\n# The node config specifies the launch config and physical instance type.\navailable_node_types:\n    ray.head.default:\n        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.\n        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.\n        # You can also set custom resources.\n        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called \"custom\", set\n        # resources: {\"CPU\": 1, \"GPU\": 1, \"custom\": 5}\n        resources: {}\n        # Provider-specific config for this node type, e.g. instance type. By default\n        # Ray will auto-configure unspecified fields such as SubnetId and KeyName.\n        # For more documentation on available fields, see:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# For more documentation on available fields, see:\n        # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\n        node_config:\n            InstanceType: m5.large\n            # Default AMI for us-west-2.\n            # Check https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/_private/aws/config.py\n            # for default images for other zones.\n            ImageId: ami-0387d929287ab193e\n            # You can provision additional disk space with a conf as follows\n            BlockDeviceMappings:\n                - DeviceName: /dev/sda1\n                  Ebs:\n                      VolumeSize: 140\n                      VolumeType: gp3\n            # Additional options in the boto docs.\n    ray.worker.default:\n        # The minimum number of worker nodes of this type to launch.\n        # This number should be >= 0.\n        min_workers: 1\n        # The maximum number of worker nodes of this type to launch."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d4e6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "min_workers: 1\n        # The maximum number of worker nodes of this type to launch.\n        # This takes precedence over min_workers.\n        max_workers: 2\n        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.\n        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.\n        # You can also set custom resources.\n        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called \"custom\", set\n        # resources: {\"CPU\": 1, \"GPU\": 1, \"custom\": 5}\n        resources: {}\n        # Provider-specific config for this node type, e.g. instance type. By default\n        # Ray will auto-configure unspecified fields such as SubnetId and KeyName.\n        # For more documentation on available fields, see:\n        # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\n        node_config:\n            InstanceType: m5.large"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d52c-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "node_config:\n            InstanceType: m5.large\n            # Default AMI for us-west-2.\n            # Check https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/_private/aws/config.py\n            # for default images for other zones.\n            ImageId: ami-0387d929287ab193e\n            # Run workers on spot by default. Comment this out to use on-demand.\n            # NOTE: If relying on spot instances, it is best to specify multiple different instance\n            # types to avoid interruption when one instance type is experiencing heightened demand.\n            # Demand information can be found at https://aws.amazon.com/ec2/spot/instance-advisor/\n            InstanceMarketOptions:\n                MarketType: spot\n                # Additional options can be found in the boto docs, e.g.\n                #   SpotOptions:\n                #       MaxPrice: MAX_HOURLY_PRICE\n            # Additional options in the boto docs."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d57c-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Specify the node type of the head node (as configured above).\nhead_node_type: ray.head.default\n\n# Files or directories to copy to the head and worker nodes. The format is a\n# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.\nfile_mounts: {\n#    \"/path1/on/remote/machine\": \"/path1/on/local/machine\",\n#    \"/path2/on/remote/machine\": \"/path2/on/local/machine\",\n}\n\n# Files or directories to copy from the head node to the worker nodes. The format is a\n# list of paths. The same path on the head node will be copied to the worker node.\n# This behavior is a subset of the file_mounts behavior. In the vast majority of cases\n# you should just use file_mounts. Only use this if you know what you're doing!\ncluster_synced_files: []\n\n# Whether changes to directories in file_mounts or cluster_synced_files in the head node\n# should sync to the worker node continuously\nfile_mounts_sync_continuously: False"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d5cc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Patterns for files to exclude when running rsync up or rsync down\nrsync_exclude:\n    - \"**/.git\"\n    - \"**/.git/**\"\n\n# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for\n# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided\n# as a value, the behavior will match git's behavior for finding and using .gitignore files.\nrsync_filter:\n    - \".gitignore\"\n\n# List of commands that will be run before `setup_commands`. If docker is\n# enabled, these commands will run outside the container and before docker\n# is setup.\ninitialization_commands: []"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d612-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# List of shell commands to run to set up nodes.\nsetup_commands: []\n    # Note: if you're developing Ray, you probably want to create a Docker image that\n    # has your Ray repo pre-cloned. Then, you can replace the pip installs\n    # below with a git checkout <your_sha> (and possibly a recompile).\n    # To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image\n    # that has the \"nightly\" (e.g. \"rayproject/ray-ml:nightly-gpu\") or uncomment the following line:\n    # - pip install -U \"ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl\"\n\n# Custom commands that will be run on the head node after common setup.\nhead_setup_commands: []\n\n# Custom commands that will be run on worker nodes after common setup.\nworker_setup_commands: []"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d662-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "# Custom commands that will be run on worker nodes after common setup.\nworker_setup_commands: []\n\n# Command to start ray on the head node. You don't need to change this.\nhead_start_ray_commands:\n    - ray stop\n    - ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --dashboard-host=0.0.0.0\n\n# Command to start ray on worker nodes. You don't need to change this.\nworker_start_ray_commands:\n    - ray stop\n    - ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076\n\n\n\nAssuming you have stored this configuration in a file called cluster.yaml, you can now launch an AWS cluster as follows:\nray submit cluster.yaml example.py --start\n\n\nLearn more about launching Ray Clusters\n\n\n\nDebugging and Monitoring Quickstart#\nUse built-in observability tools to monitor and debug Ray applications and clusters.\n\n\n Ray Dashboard: Web GUI to monitor and debug Ray"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d6a8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Dashboard: Web GUI to monitor and debug Ray\n\n\n\n\nRay dashboard provides a visual interface that displays real-time system metrics, node-level resource monitoring, job profiling, and task visualizations. The dashboard is designed to help users understand the performance of their Ray applications and identify potential issues.\n\n\nNote\nTo get started with the dashboard, install the default installation as follows:\npip install -U \"ray[default]\"\n\n\n\nAccess the dashboard through the default URL, http://localhost:8265.\nLearn more about Ray Dashboard\n\n\n\n Ray State APIs: CLI to access cluster states\n\n\n\n\nRay state APIs allow users to conveniently access the current state (snapshot) of Ray through CLI or Python SDK.\n\nNote\nTo get started with the state API, install the default installation as follows:\npip install -U \"ray[default]\"\n\n\n\nRun the following code.\n    import ray\n    import time\n\n    ray.init(num_cpus=4)"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d6f8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Run the following code.\n    import ray\n    import time\n\n    ray.init(num_cpus=4)\n\n    @ray.remote\n    def task_running_300_seconds():\n        print(\"Start!\")\n        time.sleep(300)\n\n    @ray.remote\n    class Actor:\n        def __init__(self):\n            print(\"Actor created\")\n\n    # Create 2 tasks\n    tasks = [task_running_300_seconds.remote() for _ in range(2)]\n\n    # Create 2 actors\n    actors = [Actor.remote() for _ in range(2)]\n\n    ray.get(tasks)\n\n\nSee the summarized statistics of Ray tasks using ray summary tasks.\n    ray summary tasks\n\n\n    ======== Tasks Summary: 2022-07-22 08:54:38.332537 ========\n    Stats:\n    ------------------------------------\n    total_actor_scheduled: 2\n    total_actor_tasks: 0\n    total_tasks: 2"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d748-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Table (group by func_name):\n    ------------------------------------\n        FUNC_OR_CLASS_NAME        STATE_COUNTS    TYPE\n    0   task_running_300_seconds  RUNNING: 2      NORMAL_TASK\n    1   Actor.__init__            FINISHED: 2     ACTOR_CREATION_TASK\n\n\nLearn more about Ray State APIs\n\n\n\n\nLearn More#\nHere are some talks, papers, and press coverage involving Ray and its libraries.\nPlease raise an issue if any of the below links are broken, or if you\u2019d like to add your own talk!\n\nBlog and Press#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d78e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Blog and Press#\n\nModern Parallel and Distributed Python: A Quick Tutorial on Ray\nWhy Every Python Developer Will Love Ray\nRay: A Distributed System for AI (BAIR)\n10x Faster Parallel Python Without Python Multiprocessing\nImplementing A Parameter Server in 15 Lines of Python with Ray\nRay Distributed AI Framework Curriculum\nRayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo\nFirst user tips for Ray\nTune: a Python library for fast hyperparameter tuning at any scale\nCutting edge hyperparameter tuning with Ray Tune\nNew Library Targets High Speed Reinforcement Learning\nScaling Multi Agent Reinforcement Learning\nFunctional RL with Keras and Tensorflow Eager\nHow to Speed up Pandas by 4x with one line of code\nQuick Tip \u2013 Speed up Pandas using Modin\nRay Blog\n\n\n\nTalks (Videos)#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d810-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Talks (Videos)#\n\nUnifying Large Scale Data Preprocessing and Machine Learning Pipelines with Ray Data | PyData 2021 (slides)\nProgramming at any Scale with Ray | SF Python Meetup Sept 2019\nRay for Reinforcement Learning | Data Council 2019\nScaling Interactive Pandas Workflows with Modin\nRay: A Distributed Execution Framework for AI | SciPy 2018\nRay: A Cluster Computing Engine for Reinforcement Learning Applications | Spark Summit\nRLlib: Ray Reinforcement Learning Library | RISECamp 2018\nEnabling Composition in Distributed Reinforcement Learning | Spark Summit 2018\nTune: Distributed Hyperparameter Search | RISECamp 2018\n\n\n\nSlides#\n\nTalk given at UC Berkeley DS100\nTalk given in October 2019\nTalk given at RISECamp 2019\n\n\n\nPapers#\n\nRay 2.0 Architecture whitepaper\nRay 1.0 Architecture whitepaper (old)\nExoshuffle: large-scale data shuffle in Ray\nRLlib paper\nRLlib flow paper\nTune paper\nRay paper (old)\nRay HotOS paper (old)\n\n\n\n\n\n\n\n\n\n\n\nprevious\nOverview\n\n\n\n\nnext\nInstalling Ray"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986d856-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/getting-started.html", "title": "Getting Started \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "previous\nOverview\n\n\n\n\nnext\nInstalling Ray\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f6c4-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Overview \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.md\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   What you can do with Ray\n  \n\n\n\n   Ray framework\n  \n\n\n\n\n\n\n\n\n\nOverview\n\n\n\n\n Contents \n\n\n\n\n\n   What you can do with Ray\n  \n\n\n\n   Ray framework"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f714-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Overview\n\n\n\n\n Contents \n\n\n\n\n\n   What you can do with Ray\n  \n\n\n\n   Ray framework\n  \n\n\n\n\n\n\n\n\n\nOverview#\nRay is an open-source unified framework for scaling AI and Python applications like machine learning. It provides the compute layer for parallel processing so that you don\u2019t need to be a distributed systems expert. Ray minimizes the complexity of running your distributed individual and end-to-end machine learning workflows with these components:\n\nScalable libraries for common machine learning tasks such as data preprocessing, distributed training, hyperparameter tuning, reinforcement learning, and model serving.\nPythonic distributed computing primitives for parallelizing and scaling Python applications.\nIntegrations and utilities for integrating and deploying a Ray cluster with existing tools and infrastructure such as Kubernetes, AWS, GCP, and Azure.\n\nFor data scientists and machine learning practitioners, Ray lets you scale jobs without needing infrastructure expertise:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f75a-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Easily parallelize and distribute ML workloads across multiple nodes and GPUs.\nLeverage the ML ecosystem with native and extensible integrations.\n\nFor ML platform builders and ML engineers, Ray:\n\nProvides compute abstractions for creating a scalable and robust ML platform.\nProvides a unified ML API that simplifies onboarding and integration with the broader ML ecosystem.\nReduces friction between development and production by enabling the same Python code to scale seamlessly from a laptop to a large cluster.\n\nFor distributed systems engineers, Ray automatically handles key processes:\n\nOrchestration\u2013Managing the various components of a distributed system.\nScheduling\u2013Coordinating when and where tasks are executed.\nFault tolerance\u2013Ensuring tasks complete regardless of inevitable points of failure.\nAuto-scaling\u2013Adjusting the number of resources allocated to dynamic demand."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f7a0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "What you can do with Ray#\nThese are some common ML workloads that individuals, organizations, and companies leverage Ray to build their AI applications:\n\nBatch inference on CPUs and GPUs\nParallel training\nModel serving\nDistributed training of large models\nParallel hyperparameter tuning experiments\nReinforcement learning\nML platform\n\n\n\nRay framework#\n\n\n\n\n\n\nStack of Ray libraries - unified toolkit for ML workloads.\n\n\n\nRay\u2019s unified compute framework consists of three layers:"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f7f0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
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{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray\u2019s unified compute framework consists of three layers:\n\nRay AI Libraries\u2013An open-source, Python, domain-specific set of libraries that equip ML engineers, data scientists, and researchers with a scalable and unified toolkit for ML applications.\nRay Core\u2013An open-source, Python, general purpose, distributed computing library that enables ML engineers and Python developers to scale Python applications and accelerate machine learning workloads.\nRay Clusters\u2013A set of worker nodes connected to a common Ray head node. Ray clusters can be fixed-size, or they can autoscale up and down according to the resources requested by applications running on the cluster.\n\n\n\n\n\n\nScale machine learning workloads\n\n\nBuild ML applications with a toolkit of libraries for distributed\ndata processing,\nmodel training,\ntuning,\nreinforcement learning,\nmodel serving,\nand more.\n\n\nRay AI Libraries\n\n\n\n\n\n\nBuild distributed applications"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f836-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray AI Libraries\n\n\n\n\n\n\nBuild distributed applications\n\n\nBuild and run distributed applications with a\nsimple and flexible API.\nParallelize single machine code with\nlittle to zero code changes.\n\n\nRay Core\n\n\n\n\n\n\nDeploy large-scale workloads\n\n\nDeploy workloads on AWS, GCP, Azure or\non premise.\nUse Ray cluster managers to run Ray on existing\nKubernetes,\nYARN,\nor Slurm clusters.\n\n\nRay Clusters\n\n\n\n\n\nEach of Ray\u2019s five native libraries distributes a specific ML task:\n\nData: Scalable, framework-agnostic data loading and transformation across training, tuning, and prediction.\nTrain: Distributed multi-node and multi-core model training with fault tolerance that integrates with popular training libraries.\nTune: Scalable hyperparameter tuning to optimize model performance.\nServe: Scalable and programmable serving to deploy models for online inference, with optional microbatching to improve performance.\nRLlib: Scalable distributed reinforcement learning workloads."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b986f886-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/index.html", "title": "Overview \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray\u2019s libraries are for both data scientists and ML engineers alike. For data scientists, these libraries can be used to scale individual workloads, and also end-to-end ML applications. For ML Engineers, these libraries provides scalable platform abstractions that can be used to easily onboard and integrate tooling from the broader ML ecosystem.\nFor custom applications, the Ray Core library enables Python developers to easily build scalable, distributed systems that can run on a laptop, cluster, cloud, or Kubernetes. It\u2019s the foundation that Ray AI libraries and third-party integrations (Ray ecosystem) are built on.\nRay runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing\necosystem of community integrations.\n\n\n\n\n\n\n\n\n\n\nprevious\nWelcome to Ray!\n\n\n\n\nnext\nGetting Started\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987001a-83c7-11ee-a273-0242ac1c000c
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created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987006a-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Train API \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n\n\n\n\n\n     Overview\n    \n\n\n\n     PyTorch Guide\n    \n\n\n\n     PyTorch Lightning Guide\n    \n\n\n\n     Hugging Face Transformers Guide\n    \n\n\n\n     More Frameworks\n    \n\n\n\n     User Guides\n    \n\n\n\n     Examples\n    \n\n\n\n     Benchmarks\n    \n\n\n\n     Ray Train API\n    \n\n\n\n\n\n\n\n\n       ray.train.torch.TorchTrainer\n      \n\n\n\n       ray.train.torch.TorchConfig\n      \n\n\n\n       ray.train.torch.get_device\n      \n\n\n\n       ray.train.torch.prepare_model\n      \n\n\n\n       ray.train.torch.prepare_data_loader"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98700b0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray.train.torch.prepare_model\n      \n\n\n\n       ray.train.torch.prepare_data_loader\n      \n\n\n\n       ray.train.torch.enable_reproducibility\n      \n\n\n\n       ray.train.lightning.prepare_trainer\n      \n\n\n\n       ray.train.lightning.RayLightningEnvironment\n      \n\n\n\n       ray.train.lightning.RayDDPStrategy\n      \n\n\n\n       ray.train.lightning.RayFSDPStrategy\n      \n\n\n\n       ray.train.lightning.RayDeepSpeedStrategy\n      \n\n\n\n       ray.train.lightning.RayTrainReportCallback\n      \n\n\n\n       ray.train.huggingface.transformers.prepare_trainer\n      \n\n\n\n       ray.train.huggingface.transformers.RayTrainReportCallback\n      \n\n\n\n       ray.train.tensorflow.TensorflowTrainer\n      \n\n\n\n       ray.train.tensorflow.TensorflowConfig\n      \n\n\n\n       ray.train.tensorflow.prepare_dataset_shard\n      \n\n\n\n       ray.train.tensorflow.keras.ReportCheckpointCallback\n      \n\n\n\n       ray.train.horovod.HorovodTrainer\n      \n\n\n\n       ray.train.horovod.HorovodConfig"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870100-83c7-11ee-a273-0242ac1c000c
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metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray.train.horovod.HorovodTrainer\n      \n\n\n\n       ray.train.horovod.HorovodConfig\n      \n\n\n\n       ray.train.xgboost.XGBoostTrainer\n      \n\n\n\n       ray.train.lightgbm.LightGBMTrainer\n      \n\n\n\n       ray.train.CheckpointConfig\n      \n\n\n\n       ray.train.DataConfig\n      \n\n\n\n       ray.train.FailureConfig\n      \n\n\n\n       ray.train.RunConfig\n      \n\n\n\n       ray.train.ScalingConfig\n      \n\n\n\n       ray.train.SyncConfig\n      \n\n\n\n       ray.train.Checkpoint\n      \n\n\n\n       ray.train.context.TrainContext\n      \n\n\n\n       ray.train.get_checkpoint\n      \n\n\n\n       ray.train.get_context\n      \n\n\n\n       ray.train.get_dataset_shard\n      \n\n\n\n       ray.train.report\n      \n\n\n\n       ray.train.Result\n      \n\n\n\n       ray.train.trainer.BaseTrainer\n      \n\n\n\n       ray.train.data_parallel_trainer.DataParallelTrainer\n      \n\n\n\n       ray.train.gbdt_trainer.GBDTTrainer\n      \n\n\n\n       ray.train.backend.Backend\n      \n\n\n\n       ray.train.backend.BackendConfig\n      \n\n\n\n\n\n\n\n   Ray Tune"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870146-83c7-11ee-a273-0242ac1c000c
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metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "ray.train.backend.BackendConfig\n      \n\n\n\n\n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Contents\n    \n\n\n\n\n   PyTorch Ecosystem\n  \n\n\n\n     PyTorch\n    \n\n\n\n     PyTorch Lightning\n    \n\n\n\n     Hugging Face Transformers\n    \n\n\n\n\n\n   More Frameworks\n  \n\n\n\n     Tensorflow/Keras\n    \n\n\n\n     Horovod\n    \n\n\n\n     XGBoost\n    \n\n\n\n     LightGBM\n    \n\n\n\n\n\n   Ray Train Configuration\n  \n\n\n\n   Ray Train Utilities\n  \n\n\n\n\n\n   Ray Train Output\n  \n\n\n\n   Ray Train Developer APIs\n  \n\n\n\n     Trainer Base Classes\n    \n\n\n\n     Train Backend Base Classes\n    \n\n\n\n\n\n\n\n\n\n\n\nRay Train API\n\n\n\n\n Contents \n\n\n\n\n\n   PyTorch Ecosystem\n  \n\n\n\n     PyTorch"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870196-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Train API\n\n\n\n\n Contents \n\n\n\n\n\n   PyTorch Ecosystem\n  \n\n\n\n     PyTorch\n    \n\n\n\n     PyTorch Lightning\n    \n\n\n\n     Hugging Face Transformers\n    \n\n\n\n\n\n   More Frameworks\n  \n\n\n\n     Tensorflow/Keras\n    \n\n\n\n     Horovod\n    \n\n\n\n     XGBoost\n    \n\n\n\n     LightGBM\n    \n\n\n\n\n\n   Ray Train Configuration\n  \n\n\n\n   Ray Train Utilities\n  \n\n\n\n\n\n   Ray Train Output\n  \n\n\n\n   Ray Train Developer APIs\n  \n\n\n\n     Trainer Base Classes\n    \n\n\n\n     Train Backend Base Classes\n    \n\n\n\n\n\n\n\n\n\n\n\nRay Train API#\n\nPyTorch Ecosystem#\n\n\n\n\n\n\nTorchTrainer\nA Trainer for data parallel PyTorch training.\n\nTorchConfig\nConfiguration for torch process group setup.\n\n\n\n\nPyTorch#\n\n\n\n\n\n\nget_device\nGets the correct torch device configured for this process.\n\nprepare_model\nPrepares the model for distributed execution.\n\nprepare_data_loader\nPrepares DataLoader for distributed execution.\n\nenable_reproducibility\nLimits sources of nondeterministic behavior.\n\n\n\n\n\nPyTorch Lightning#"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98701dc-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "enable_reproducibility\nLimits sources of nondeterministic behavior.\n\n\n\n\n\nPyTorch Lightning#\n\n\n\n\n\n\nprepare_trainer\nPrepare the PyTorch Lightning Trainer for distributed execution.\n\nRayLightningEnvironment\nSetup Lightning DDP training environment for Ray cluster.\n\nRayDDPStrategy\nSubclass of DDPStrategy to ensure compatibility with Ray orchestration.\n\nRayFSDPStrategy\nSubclass of FSDPStrategy to ensure compatibility with Ray orchestration.\n\nRayDeepSpeedStrategy\nSubclass of DeepSpeedStrategy to ensure compatibility with Ray orchestration.\n\nRayTrainReportCallback\nA simple callback that reports checkpoints to Ray on train epoch end.\n\n\n\n\n\nHugging Face Transformers#\n\n\n\n\n\n\nprepare_trainer\nPrepare your HuggingFace Transformer Trainer for Ray Train.\n\nRayTrainReportCallback\nA simple callback to report checkpoints and metrics to Ray Tarin.\n\n\n\n\n\n\nMore Frameworks#\n\nTensorflow/Keras#\n\n\n\n\n\n\nTensorflowTrainer\nA Trainer for data parallel Tensorflow training."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987022c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tensorflow/Keras#\n\n\n\n\n\n\nTensorflowTrainer\nA Trainer for data parallel Tensorflow training.\n\nTensorflowConfig\nPublicAPI (beta): This API is in beta and may change before becoming stable.\n\nprepare_dataset_shard\nA utility function that overrides default config for Tensorflow Dataset.\n\nReportCheckpointCallback\nKeras callback for Ray Train reporting and checkpointing.\n\n\n\n\n\nHorovod#\n\n\n\n\n\n\nHorovodTrainer\nA Trainer for data parallel Horovod training.\n\nHorovodConfig\nConfigurations for Horovod setup.\n\n\n\n\n\nXGBoost#\n\n\n\n\n\n\nXGBoostTrainer\nA Trainer for data parallel XGBoost training.\n\n\n\n\n\nLightGBM#\n\n\n\n\n\n\nLightGBMTrainer\nA Trainer for data parallel LightGBM training.\n\n\n\n\n\n\nRay Train Configuration#\n\n\n\n\n\n\nCheckpointConfig\nConfigurable parameters for defining the checkpointing strategy.\n\nDataConfig\nClass responsible for configuring Train dataset preprocessing.\n\nFailureConfig\nConfiguration related to failure handling of each training/tuning run."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870272-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "FailureConfig\nConfiguration related to failure handling of each training/tuning run.\n\nRunConfig\nRuntime configuration for training and tuning runs.\n\nScalingConfig\nConfiguration for scaling training.\n\nSyncConfig\nConfiguration object for Train/Tune file syncing to RunConfig(storage_path).\n\n\n\n\n\nRay Train Utilities#\nClasses\n\n\n\n\n\n\nCheckpoint\nA reference to data persisted as a directory in local or remote storage.\n\nTrainContext\nContext for Ray training executions.\n\n\n\nFunctions\n\n\n\n\n\n\nget_checkpoint\nAccess the session's last checkpoint to resume from if applicable.\n\nget_context\nGet or create a singleton training context.\n\nget_dataset_shard\nReturns the ray.data.DataIterator shard for this worker.\n\nreport\nReport metrics and optionally save a checkpoint.\n\n\n\n\n\nRay Train Output#\n\n\n\n\n\n\nResult\nThe final result of a ML training run or a Tune trial.\n\n\n\n\n\nRay Train Developer APIs#\n\nTrainer Base Classes#\n\n\n\n\n\n\nBaseTrainer\nDefines interface for distributed training on Ray."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98702b8-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/train/api/api.html", "title": "Ray Train API \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Trainer Base Classes#\n\n\n\n\n\n\nBaseTrainer\nDefines interface for distributed training on Ray.\n\nDataParallelTrainer\nA Trainer for data parallel training.\n\nGBDTTrainer\nAbstract class for scaling gradient-boosting decision tree (GBDT) frameworks.\n\n\n\n\n\nTrain Backend Base Classes#\n\n\n\n\n\n\nBackend\nSingleton for distributed communication backend.\n\nBackendConfig\nParent class for configurations of training backend.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprevious\nRay Train Benchmarks\n\n\n\n\nnext\nray.train.torch.TorchTrainer\n\n\n\n\n\n\n\n\n\n\n\nThanks for the feedback!\n\n\nWas this helpful?\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\nFeedback\n\nSubmit\n\n\n  \n    By The Ray Team\n  \n      \u00a9 Copyright 2023, The Ray Team."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870308-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Ray Examples \u2014 Ray 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nToggle navigation sidebar\n\n\n\n\nToggle in-page Table of Contents\n\n\n\n\n\n\n\n\n\n\n\n\nRay 3.0.0.dev0\n\n\n\n\n\n\n\n\n\n                    Welcome to Ray!\n                \n\n\n\n\n  Ray\n \n\n\n\n\n   Overview\n  \n\n\n\n   Getting Started\n  \n\n\n\n   Installation\n  \n\n\n\n   Use Cases\n  \n\n\n\n   Example Gallery\n  \n\n\n\n   Ecosystem\n  \n\n\n\n   Ray Core\n  \n\n\n\n   Ray Data\n  \n\n\n\n   Ray Train\n  \n\n\n\n   Ray Tune\n  \n\n\n\n   Ray Serve\n  \n\n\n\n   Ray RLlib\n  \n\n\n\n   More Libraries\n  \n\n\n\n   Ray Clusters\n  \n\n\n\n   Monitoring and Debugging\n  \n\n\n\n   Developer Guides\n  \n\n\n\n   Glossary\n  \n\n\n\n   Security\n  \n\n\n\n\n\n\n\n            Theme by the Executable Book Project\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nrepository\n\n\n\n\n\n\n\nopen issue\n\n\n\n\n\n\n\nsuggest edit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRay Examples\n\n\n\n\n\n\n\n\n\nRay Examples#\n\n\n\n\n\n\n\n                         \nSorry! We could not find an example matching that filter."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987034e-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Sorry! We could not find an example matching that filter.\n\n                Help us improve our examples by suggesting one. Tell us what example you would like to\n                have.\n              \n\n\n\n\n\n\n\n\n\nBlog\nHow Ray solves common production challenges for generative AI infrastructure\n\n\n\n\n\n\n\nBlog\nTraining 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray\n\n\n\n\n\n\n\nBlog\nFaster stable diffusion fine-tuning with Ray Train\n\n\n\n\n\n\n\nBlog\nHow to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace\n\n\n\n\n\n\n\nBlog\nHow OpenAI Uses Ray to Train Tools like ChatGPT\n\n\n\n\n\n\n\nCode example\nGPT-J-6B Fine-Tuning with Ray Train and DeepSpeed\n\n\n\n\n\n\n\nCode example\nAviary toolkit serving live traffic for LLMs\n\n\n\n\n\n\n\nTutorial\nGet started with Ray Train from an existing PyTorch codebase\n\n\n\n\n\n\n\nCode example\nDistributed training with LightGBM\n\n\n\n\n\n\n\nTutorial\nDistributed training with XGBoost"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870394-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tutorial\nDistributed training with XGBoost\n\n\n\n\n\n\n\nCode example\nHow to use Ray Data to do batch prediction with the Hugging Face Transformers GPT-J model\n\n\n\n\n\n\n\nCode example\nHow to use Ray Serve to do online serving with the Hugging Face Transformers GPT-J model\n\n\n\n\n\n\n\nCode example\nHow to fine-tune a DreamBooth text-to-image model with your own images.\n\n\n\n\n\n\n\nCode example\nFine-tune dolly-v2-7b with Ray Train, PyTorch Lightning and FSDP\n\n\n\n\n\n\n\nCode example\nTorch Object Detection Example with Ray Train\n\n\n\n\n\n\n\nCode example\nImage Classification Batch Inference with PyTorch ResNet152\n\n\n\n\n\n\n\nCode example\nHow to use Ray Data to do batch prediction with the Stable Diffusion text-to-image model\n\n\n\n\n\n\n\nCode example\nObject Detection Batch Inference with PyTorch FasterRCNN_ResNet50\n\n\n\n\n\n\n\nCode example\nImage Classification Batch Inference with PyTorch ResNet18\n\n\n\n\n\n\n\nCode example\nImage Classification Batch Inference with Huggingface Vision Transformer"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98703e4-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nImage Classification Batch Inference with Huggingface Vision Transformer\n\n\n\n\n\n\n\nCode example\nServing ML models with Ray Serve (Tensorflow, PyTorch, Scikit-Learn, others)\n\n\n\n\n\n\n\nCode example\nBatching tutorial for Ray Serve\n\n\n\n\n\n\n\nCode example\nServing RLlib Models with Ray Serve\n\n\n\n\n\n\n\nCode example\nScaling your Gradio app with Ray Serve\n\n\n\n\n\n\n\nCode example\nJava tutorial for Ray Serve\n\n\n\n\n\n\n\nCode example\nServing a Stable Diffusion Model\n\n\n\n\n\n\n\nCode example\nServing a Distilbert Model\n\n\n\n\n\n\n\nCode example\nServing a Bert Model on AWS NeuronCore\n\n\n\n\n\n\n\nCode example\nServing an Object Detection Model\n\n\n\n\n\n\n\nBlog\nOffline Batch Inference: Comparing Ray, Apache Spark, and SageMaker\n\n\n\n\n\n\n\nBlog\nStreaming distributed execution across CPUs and GPUs\n\n\n\n\n\n\n\nBlog\nUsing Ray Data to parallelize LangChain inference\n\n\n\n\n\n\n\nBlog\nBatch Prediction using Ray Data\n\n\n\n\n\n\n\nBlog\nTraining One Million ML Models in Record Time with Ray\n\n\n\n\n\n\n\nBlog\nMany Models Batch Training at Scale with Ray Core"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987042a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Blog\nMany Models Batch Training at Scale with Ray Core\n\n\n\n\n\n\n\nCode example\nBatch Training with Ray Core\n\n\n\n\n\n\n\nCode example\nBatch Training with Ray Data\n\n\n\n\n\n\n\nTutorial\nTune Basic Parallel Experiments\n\n\n\n\n\n\n\nCode example\nBatch Training and Tuning using Ray Tune\n\n\n\n\n\n\n\nVideo\nScaling Instacart fulfillment ML on Ray\n\n\n\n\n\n\n\nCode example\nUsing Aim with Ray Tune For Experiment Management\n\n\n\n\n\n\n\nCode example\nUsing Comet with Ray Tune For Experiment Management\n\n\n\n\n\n\n\nCode example\nTracking Your Experiment Process Weights & Biases\n\n\n\n\n\n\n\nCode example\nUsing MLflow Tracking & AutoLogging with Tune\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With Ax\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With Dragonfly\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With HyperOpt\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With BayesOpt\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With BlendSearch and CFO\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With TuneBOHB\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With Nevergrad"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987047a-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nHow To Use Tune With Nevergrad\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With Optuna\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With SigOpt\n\n\n\n\n\n\n\nVideo\nProductionizing ML at Scale with Ray Serve\n\n\n\n\n\n\n\nBlog\nSimplify your MLOps with Ray & Ray Serve\n\n\n\n\n\n\n\nTutorial\nGetting Started with Ray Serve\n\n\n\n\n\n\n\nTutorial\nModel Composition in Serve\n\n\n\n\n\n\n\nTutorial\nGetting Started with Ray Tune\n\n\n\n\n\n\n\nBlog\nHow to distribute hyperparameter tuning with Ray Tune\n\n\n\n\n\n\n\nVideo\nSimple Distributed Hyperparameter Optimization\n\n\n\n\n\n\n\nBlog\nHyperparameter Search with \ud83e\udd17 Transformers\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With Keras & TF Models\n\n\n\n\n\n\n\nCode example\nHow To Use Tune With PyTorch Models\n\n\n\n\n\n\n\nCode example\nHow To Tune PyTorch Lightning Models\n\n\n\n\n\n\n\nCode example\nModel Selection & Serving With Ray Serve\n\n\n\n\n\n\n\nCode example\nTuning RL Experiments With Ray Tune & Ray Serve\n\n\n\n\n\n\n\nCode example\nA Guide To Tuning XGBoost Parameters With Tune"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98704c0-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nA Guide To Tuning XGBoost Parameters With Tune\n\n\n\n\n\n\n\nCode example\nA Guide To Tuning LightGBM Parameters With Tune\n\n\n\n\n\n\n\nCode example\nA Guide To Tuning Horovod Parameters With Tune\n\n\n\n\n\n\n\nCode example\nA Guide To Tuning Huggingface Transformers With Tune\n\n\n\n\n\n\n\nCode example\nMore Tune use cases on the Blog\n\n\n\n\n\n\n\nVideo\nRay Train, PyTorch, TorchX, and distributed deep learning\n\n\n\n\n\n\n\nTutorial\nGetting Started with Ray Train\n\n\n\n\n\n\n\nCode example\nFine-tune a \ud83e\udd17 Transformers model on GLUE benchmark\n\n\n\n\n\n\n\nCode example\nPyTorch Fashion MNIST Training Example\n\n\n\n\n\n\n\nCode example\nTensorFlow MNIST Training Example\n\n\n\n\n\n\n\nCode example\nEnd-to-end Horovod Training Example\n\n\n\n\n\n\n\nCode example\nEnd-to-end PyTorch Lightning Training Example\n\n\n\n\n\n\n\nCode example\nFine-tune a PyTorch Lightning Text Classifier with Ray Data\n\n\n\n\n\n\n\nCode example\nEnd-to-end Example for Tuning a TensorFlow Model\n\n\n\n\n\n\n\nCode example\nEnd-to-end Example for Tuning a PyTorch Model with PBT"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870510-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nEnd-to-end Example for Tuning a PyTorch Model with PBT\n\n\n\n\n\n\n\nCourse\nApplied Reinforcement Learning with RLlib\n\n\n\n\n\n\n\nBlog\nIntro to RLlib: Example Environments\n\n\n\n\n\n\n\nCode example\nA collection of tuned hyperparameters by RLlib algorithm\n\n\n\n\n\n\n\nCode example\n\nA collection of reasonably optimized Atari and MuJoCo results for RLlib\n\n\n\n\n\n\n\n\nCode example\nRLlib\u2019s trajectory view API and how it enables implementations of GTrXL (attention net) architectures\n\n\n\n\n\n\n\nCode example\nA how-to on connecting RLlib with the Unity3D game engine for running visual- and physics-based RL experiments\n\n\n\n\n\n\n\nCode example\n\nHow we ported 12 of RLlib\u2019s algorithms from TensorFlow to PyTorch and what we learnt on the way\n\n\n\n\n\n\n\n\nCode example\nThis blog post is a brief tutorial on multi-agent RL and its design in RLlib\n\n\n\n\n\n\n\nCode example\nExploration of a functional paradigm for implementing reinforcement learning (RL) algorithms"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870556-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of defining and registering a gym env and model for use with RLlib\n\n\n\n\n\n\n\nCode example\nExample of how to setup an RLlib algorithm against a locally running Unity3D editor\n\n\n\n\n\n\n\nCode example\nRendering and recording of an environment\n\n\n\n\n\n\n\nCode example\nCoin game example with RLlib\n\n\n\n\n\n\n\nCode example\nExample for how to use a DMLab environment (Watermaze)\n\n\n\n\n\n\n\nCode example\nRecSym environment example (for recommender systems) using the SlateQ algorithm\n\n\n\n\n\n\n\nCode example\nExample demonstrating how to use the SUMO simulator in connection with RLlib.\n\n\n\n\n\n\n\nCode example\nVizDoom example script using RLlib\u2019s auto-attention wrapper\n\n\n\n\n\n\n\nCode example\nExample of how to ensure subprocesses spawned by envs are killed when RLlib exits.\n\n\n\n\n\n\n\nCode example\nAttention Net (GTrXL) learning the \u201crepeat-after-me\u201d environment\n\n\n\n\n\n\n\nCode example\nExample showing how to use the auto-LSTM wrapper for your default- and custom models in RLlib."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98705a6-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of using a custom Keras- or PyTorch RNN model.\n\n\n\n\n\n\n\nCode example\nExample of defining and registering a custom model with a supervised loss.\n\n\n\n\n\n\n\nCode example\nExample of adding batch norm layers to a custom model.\n\n\n\n\n\n\n\nCode example\nExample of how to leverage TensorFlow eager to simplify debugging and design of custom models and policies.\n\n\n\n\n\n\n\nCode example\nExample of a \u201cfast\u201d Model learning only one parameter for tf and torch.\n\n\n\n\n\n\n\nCode example\nShows how to define a custom Model API in RLlib, such that it can be used inside certain algorithms.\n\n\n\n\n\n\n\nCode example\nAn example on how a model can use the trajectory view API to specify its own input.\n\n\n\n\n\n\n\nCode example\nImplementations of MobileNetV2 and torch.hub (mobilenet_v2)-wrapping example models.\n\n\n\n\n\n\n\nCode example\nExample of DeepMind\u2019s Differentiable Neural Computer for partially-observable environments."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98705ec-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of how to use Tune\u2019s support for custom training functions to implement custom training workflows.\n\n\n\n\n\n\n\nCode example\nExample of how to advance the environment through different phases (tasks) over time.\n\n\n\n\n\n\n\nCode example\nHow to setup a custom Logger object in RLlib.\n\n\n\n\n\n\n\nCode example\nExample of how to output custom training metrics to TensorBoard.\n\n\n\n\n\n\n\nCode example\nHow to setup a custom TFPolicy.\n\n\n\n\n\n\n\nCode example\nHow to setup a custom TorchPolicy.\n\n\n\n\n\n\n\nCode example\nExample of how to use RLlib\u2019s lower-level building blocks to implement a fully customized training workflow.\n\n\n\n\n\n\n\nCode example\nExample of how to use the exec. plan of an Algorithm to trin two different policies in parallel (also using multi-agent API).\n\n\n\n\n\n\n\nCode example\nHow to run a custom Ray Tune experiment with RLlib with custom training- and evaluation phases."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987063c-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of how to write a custom evaluation function that is called instead of the default behavior, which is running with the evaluation worker set through n episodes.\n\n\n\n\n\n\n\nCode example\nExample showing how the evaluation workers and the \u201cnormal\u201d rollout workers can run (to some extend) in parallel to speed up training.\n\n\n\n\n\n\n\nCode example\nExample showing how to run an offline RL training job using a historic-data json file.\n\n\n\n\n\n\n\nCode example\nExample of using Ray Serve to serve RLlib models with HTTP and JSON interface\n\n\n\n\n\n\n\nCode example\nThis script offers a simple workflow for 1) training a policy with RLlib first, 2) creating a new policy 3) restoring its weights from the trained one and serving the new policy via Ray Serve.\n\n\n\n\n\n\n\nCode example\nExample of how to setup n distributed Unity3D (compiled) games in the cloud that function as data collecting clients against a central RLlib Policy server learning how to play the game."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870682-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of online serving of predictions for a simple CartPole policy.\n\n\n\n\n\n\n\nCode example\nExample of how to externally generate experience batches in RLlib-compatible format.\n\n\n\n\n\n\n\nCode example\nExample of how to find a checkpoint after a Tuner.fit() via some custom defined criteria.\n\n\n\n\n\n\n\nCode example\nSetup RLlib to run any algorithm in (independent) multi-agent mode against a multi-agent environment.\n\n\n\n\n\n\n\nCode example\nSetup RLlib to run any algorithm in (shared-parameter) multi-agent mode against a multi-agent environment.\n\n\n\n\n\n\n\nCode example\nExample of different heuristic and learned policies competing against each other in rock-paper-scissors.\n\n\n\n\n\n\n\nCode example\nExample of the two-step game from the QMIX paper.\n\n\n\n\n\n\n\nCode example\nExample on how to use RLlib to learn in PettingZoo multi-agent environments.\n\n\n\n\n\n\n\nCode example\nExample of customizing PPO to leverage a centralized value function."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98706d2-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of customizing PPO to leverage a centralized value function.\n\n\n\n\n\n\n\nCode example\nA simpler method of implementing a centralized critic by augmentating agent observations with global information.\n\n\n\n\n\n\n\nCode example\nExample of running a custom hand-coded policy alongside trainable policies.\n\n\n\n\n\n\n\nCode example\nExample of how to define weight-sharing layers between two different policies.\n\n\n\n\n\n\n\nCode example\nExample of alternating training between DQN and PPO.\n\n\n\n\n\n\n\nCode example\nExample of hierarchical training using the multi-agent API.\n\n\n\n\n\n\n\nCode example\nExample of an iterated prisoner\u2019s dilemma environment solved by RLlib.\n\n\n\n\n\n\n\nCode example\nExample of how to setup fractional GPUs for learning (driver) and environment rollouts (remote workers).\n\n\n\n\n\n\n\nCode example\nLearning in arbitrarily nested action spaces.\n\n\n\n\n\n\n\nCode example\nExample of how to handle variable-length or parametric action spaces"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b9870718-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nExample of how to handle variable-length or parametric action spaces\n\n\n\n\n\n\n\nCode example\nHow to filter raw observations coming from the environment for further processing by the Agent\u2019s model(s).\n\n\n\n\n\n\n\nCode example\nHow to use RLlib\u2019s Repeated space to handle variable length observations.\n\n\n\n\n\n\n\nCode example\nLearning with auto-regressive action dependencies (e.g. 2 action components; distribution for 2nd component depends on the 1st component\u2019s actually sampled value).\n\n\n\n\n\n\n\nCode example\nA General Evaluation Platform and Building Toolkit for Single/Multi-Agent Intelligence with RLlib-generated baselines.\n\n\n\n\n\n\n\nCode example\nExample of training autonomous vehicles with RLlib and CARLA simulator.\n\n\n\n\n\n\n\nCode example\nUsing Graph Neural Networks and RLlib to train multiple cooperative and adversarial agents to solve the \u201ccover the area\u201d-problem, thereby learning how to best communicate (or - in the adversarial case - how to disturb communication)."}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b987075e-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nA dense traffic simulating environment with RLlib-generated baselines.\n\n\n\n\n\n\n\nCode example\nExample of setting up a multi-agent version of GFootball with RLlib.\n\n\n\n\n\n\n\nCode example\nA multiagent AI research environment inspired by Massively Multiplayer Online (MMO) role playing games\n\n\n\n\n\n\n\nCode example\nExample of building packet classification trees using RLlib / multi-agent in a bandit-like setting.\n\n\n\n\n\n\n\nCode example\nExample of learning optimal LLVM vectorization compiler pragmas for loops in C and C++ codes using RLlib.\n\n\n\n\n\n\n\nCode example\nExample of using the multi-agent API to model several social dilemma games.\n\n\n\n\n\n\n\nCode example\nCreate a custom environment and train a single agent RL using Ray 2.0 with Tune.\n\n\n\n\n\n\n\nCode example\nExample of training in StarCraft2 maps with RLlib / multi-agent.\n\n\n\n\n\n\n\nCode example\nExample of optimizing mixed-autonomy traffic simulations with RLlib / multi-agent.\n\n\n\n\n\n\n\nCode example\nWorking with custom Keras models in RLlib"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98707ae-83c7-11ee-a273-0242ac1c000c
encoding: FLOAT32
metadata:
{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Code example\nWorking with custom Keras models in RLlib\n\n\n\n\n\n\n\nTutorial\nGetting Started with RLlib\n\n\n\n\n\n\n\nVideo\nDeep reinforcement learning at Riot Games\n\n\n\n\n\n\n\nBlog\nThe Magic of Merlin - Shopify\u2019s New ML Platform\n\n\n\n\n\n\n\nTutorial\nLarge Scale Deep Learning Training and Tuning with Ray\n\n\n\n\n\n\n\nBlog\nGriffin: How Instacart\u2019s ML Platform Tripled in a year\n\n\n\n\n\n\n\nVideo\nPredibase - A low-code deep learning platform built for scale\n\n\n\n\n\n\n\nBlog\nBuilding a ML Platform with Kubeflow and Ray on GKE\n\n\n\n\n\n\n\nVideo\nRay Summit Panel - ML Platform on Ray\n\n\n\n\n\n\n\nCode example\nAutoML for Time Series with Ray\n\n\n\n\n\n\n\nBlog\nHighly Available and Scalable Online Applications on Ray at Ant Group\n\n\n\n\n\n\n\nBlog\nRay Forward 2022 Conference: Hyper-scale Ray Application Use Cases\n\n\n\n\n\n\n\nBlog\nA new world record on the CloudSort benchmark using Ray\n\n\n\n\n\n\n\nCode example\nSpeed up your web crawler by parallelizing it with Ray\n\n\n\n\n\n\n\nTutorial\nImage Classification Batch Inference with Huggingface Vision Transformer"}


created_at: 2023-11-15 15:00:56
topic: persistent://default/default/8de68529-d59f-45fc-87e1-737ed1ccf027
id: b98707f4-83c7-11ee-a273-0242ac1c000c
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{"source": "https://docs.ray.io/en/master/ray-overview/examples.html", "title": "Ray Examples \u2014 Ray 3.0.0.dev0", "language": "en", "chroma:document": "Tutorial\nImage Classification Batch Inference with Huggingface Vision Transformer\n\n\n\n\n\n\n\nTutorial\nImage Classification Batch Inference with PyTorch ResNet152\n\n\n\n\n\n\n\nTutorial\nObject Detection Batch Inference with PyTorch FasterRCNN_ResNet50\n\n\n\n\n\n\n\nTutorial\nMany Model Training with Ray Data\n\n\n\n\n\n\n\nCode example\nBuild Batch Prediction Using Ray\n\n\n\n\n\n\n\nCode example\nBuild a Simple Parameter Server Using Ray\n\n\n\n\n\n\n\nCode example\nSimple Parallel Model Selection\n\n\n\n\n\n\n\nCode example\nFault-Tolerant Fairseq Training\n\n\n\n\n\n\n\nCode example\nLearning to Play Pong\n\n\n\n\n\n\n\nCode example\nA Gentle Introduction to Ray Core by Example\n\n\n\n\n\n\n\nCode example\nUsing Ray for Highly Parallelizable Tasks\n\n\n\n\n\n\n\nCode example\nRunning a Simple MapReduce Example with Ray Core\n\n\n\n\n\n\n\nCode example\nBenchmark example for the PyTorch data transfer auto pipeline\n\n\n\n\n\n\n\nTutorial\nHow To Use Tune\u2019s Scikit-Learn Adapters?\n\n\n\n\n\n\n\nCode example\nSimple example for doing a basic random and grid search."}


