plethora of choices that we face when training a deep learning model in the computer vision domain. A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of each of those parameters. The parameters can take discrete or continuous values. It is called a "search" space because we are searching for a point in which minimizes (or maximizes) an Evaluation Function example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering. A $$True$$ value means

accompanying the given prompts. Both these models have been deployed in production. BERT is used in Google Search to improve relevance of results, and GPT-3 is available as an API for interested users to consume hyper-parameters like learning rate, regularization, dropout, etc. This relies on search methods that can range from Random Search to methods that smartly allocate resources to promising ranges of hyper-parameters Architecture Search (NAS) can help go beyond just learning hyper-parameters, and instead search for efficient architectures (layers, blocks, end-to-end models) automatically. A simplistic architecture search could

data: false # # 3. You want this node to be neither master nor data node, but # to act as a "search load balancer" (fetching data from nodes, # aggregating results, etc.) # #node.master: false #node number_of_shards: 1 #index.number_of_replicas: 0 # These settings directly affect the performance of index and search operations # in your cluster. Assuming you have enough machines to hold shards and # replicas, the allows to # _distribute_ a big index across machines. # 2. Having more *replicas* enhances the _search_ performance and improves the # cluster _availability_. # # The "number_of_shards" is a one-time

Docker feature 4.15.3. System-wide information 4.15.4. Show the Docker version information 4.15.5. Search image 4.15.6. Pull image 4.15.7. Listing images 4.15.8. Remove image 4.15.9. Image history 4 the arrows key to navigate in the commands history • CTRL-D to logout/shutdown Karaf • CTRL-R to search previously executed command • CTRL-U to remove the current line Pipe You can pipe the output of docker:version command provides details about the docker version: 4.15.5. Search image The docker:search command (or search() operation on the DockerMBean ) searches for a image on Docker HUB: karaf@root()>

Hub。 你可以通过 docker logout 退出登录。 拉取镜像 你可以通过 docker search 命令来查找官方仓库中的镜像，并利用 docker pull 命令来将它 下载到本地。 例如以 centos 为关键词进行搜索： $ docker search centos NAME 275d79 972a86 6 days ago 94.6MB $ docker push username/ubuntu:17.10 $ docker search username NAME DESCRIPTION STARS 添加 DNS 服务器到容器的 /etc/resolv.conf 中，让容器用这个服务器来 解析所有不在 /etc/hosts 中的主机名。 配置 DNS 141 --dns-search=DOMAIN 设定容器的搜索域，当设定搜索域为 .example.com 时，在搜索一个名 为 host 的主机时，DNS 不仅搜索 host，还会搜索 host.example.com

score_threshold = 0 def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4 ) -> List[Tuple[Document, float]]: scores, indices = self.index.search(np.array([embedding], dtype=np when not enough docs are returned. continue _id = self.index_to_docstore_id[i] doc = self.docstore.search(_id) if not self.chunk_conent: if not isinstance(doc, Document): raise ValueError(f"Could not find 0 <= l < len(self.index_to_docstore_id): _id0 = self.index_to_docstore_id[l] doc0 = self.docstore.search(_id0) if docs_len + len(doc0.page_content) > self.chunk_size: break_flag = True break elif doc0

params = {‘kernel’:[‘linear’, ‘rbf’], ‘C’:[1, 10]} grid_search = GridSearchCV(svc, params, cv=5) grid_search.fit(X_train, y_train) grid_search.best_params_ 在参数网格上进行穷举搜索，方法简单但是搜索速度慢（超参数较多时），且不 容易找到参数空间中的局部最优 {‘kernel’:[‘linear’, ‘rbf’], ‘C’:randint(1, 20)} random_search = RandomizedSearchCV(svc, param_dist, n_iter=10) random_search.fit(X_train, y_train) random_search.best_params_ 在参数子空间中进行随机搜索，选取空间中的100个点进行建模（可从

目录的 .dockercfg 中将保存用户的认证信息。 用户无需登录即可通过 docker search 命令来查找官方仓库中的镜像，并利用 docker pull 命令来将 它下载到本地。 例如以 centos 为关键词进行搜索： $ sudo docker search centos NAME AWS_KEY=AKIAHSHB43HS3J92MXZ \ -e AWS_SECRET=xdDowwlK7TJajV1Y7EoOZrmuPEJlHYcNP2k4j49T \ -e SEARCH_BACKEND=sqlalchemy \ -p 5000:5000 \ registry ` 此外，还可以指定本地路径（如 /home/user/registry-conf 7.26:5000/v1/repositories/test/tags/latest} 用 curl 查看仓库中的镜像。 $ curl http://192.168.7.26:5000/v1/search {"num_results": 7, "query": "", "results": [{"description": "", "name": "library/miaxis_j2ee"}, {"description":

with which to bind to the LDAP server, and search fo domain name of the user being authenticated. This should be th domain name of the user, and should have search access across Key Default Meaning in the DC=co kyuubi.authentication.ldap.bindpw The password for the bind user, to be used to search for the ful the user being authenticated. If the username is specified, this p must also be specified Kerberos Client Get Kerberos TGT Add Kerberos Client Configuration File to JVM Search Path Add Kerberos Ticket Cache to JVM Search Path Ensure core-site.xml Exists in Classpath Connect with JDBC URL Configure

te.json --param=MYSQL_USER=admin # Search all templates, image streams, and Docker images for the ones that match "ruby" oc new-app --search ruby # Search for "ruby", but only in stored templates # can be used to filter search results) oc new-app --search --template=ruby # Search for "ruby" in stored templates and print the output as YAML oc new-app --search --template=ruby --output=yaml 2.2.2. 搜索服 搜索服务 务 要通过关键字搜索特定服务，请运行以下命令： 例如，要检索 PostgreSQL 服务，请运行以下命令： 输 输出示例 出示例 您将看到在其名称中包含 search 关键字的 Operator 列表。 3.5.2.2.3. 获 获取有关服 取有关服务 务的信息 的信息 要获取有关特定服务的更多信息，请运行以下命令： 例如： 输 输出示例 出示例 $